You’ve likely seen swarm behaviour in nature – or at least video of it.Flocks of starlings produce incredible patterns known as murmurations, undulating with split-second speed. Certain species of termites build massive and complex mounds, complete with sophisticated ventilation shafts to ensure air exchange and temperature regulation. Some types of fish, when attacked by predators, form tightly whirling balls as a defense mechanism. Bees build complex hives and have specific roles that all contribute to the collective. And ants, as you’re aware, can do a lot more than spoil picnics.All of these, though very different, are examples of swarm behaviour. No single individual is in charge, yet these creatures work together to great synergic effect.Below: A starling murmuration at Rigg by Walter Baxter, CC BY-SA 2.0 via Wikimedia Commons<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIxMjI5MjY1MDU0LUFfc3RhcmxpbmdfbXVybXVyYXRpb25fYXRfUmlnZ18tX2dlb2dyYXBoLm9yZ18udWtfLV80MzAyOTMxLmpwZy53ZWJwIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><h3 id="isPasted">Swarm Robotics</h3>It was actually through these observations in nature, particularly of insects, that the concept of swarm robotics first emerged.“In a robot swarm, the collective behavior of the robots results from local interactions between the robots and between the robots and the environment in which they act… The research of swarm robotics is to study the physical body and the controlling behaviours of robots. It is inspired but not limited by the emergent behaviour observed in social insects, called swarm intelligence. Relatively simple individual rules can produce a large set of complex <a title="Swarm behaviour" href="https://en.wikipedia.org/wiki/Swarm_behaviour" target="_blank">swarm behaviours</a>. A key component is the communication between the members of the group that build a system of constant feedback. The swarm behaviour involves constant change of individuals in cooperation with others, as well as the behaviour of the whole group,” states <a href="https://en.wikipedia.org/wiki/Swarm_robotics" target="_blank">this Wikipedia entry</a>.Much of the early research (and some of the more complex, current research) involves high numbers of very simple robotic devices. But as the field has developed, so too has the size and capabilities of the robots being used in swarm applications and research.“The idea behind swarm robotics is the robots are able to communicate with each other and thus improve their decision-making and data acquisition in a given environment from a real world application standpoint,” explains Head of R&amp;D Sales Luke Corbeth.&nbsp;<h3>The Boston Example</h3>One InDro client, Boston University, is using a very large fleet of small but smart robots called <a href="https://indrorobotics.ca/limorobot/" target="_blank">LIMO</a>in swarm scenarios. One aspect of their research involves how autonomous vehicles interact with each other. Can the robots detect others merging and take appropriate action? How can autonomous vehicles detect and interact with robots that have been programmed to behave as if they’re human-driven vehicles?Research like this is obviously more practical (and far less costly) to carry out in a lab rather than on city streets. It’s important work as we head toward the Smart Cities of the future, where eventually all vehicles will be autonomous – and much of the current infrastructure (traffic lights, stop signs) will no longer be necessary because all Connected Automated Vehicles will be part of a network.“These vehicles become nodes in an Internet in which the vehicles talk to each other,” said Christos Cassandras,&nbsp;Distinguished Professor of Engineering, Head of the Division of Systems Engineering, and Professor of Electrical and Computer Engineering in an earlier interview with InDro.“They exchange information and so, ideally cooperatively, they can improve metrics of congestion, of energy, of pollution, of comfort, of safety – perhaps safety being predominant.”It’s fascinating work, and you can check out our story about it <a href="https://indrorobotics.ca/boston-university-uses-agilex-limo-for-research/" target="_blank">here</a>.But the ability of swarms to move and collect and share data collaboratively also opens up other use-case scenarios.“If you are able to team your robots together, obviously you can cover a lot more area in a shorter period of time because the robots are essentially building a map together rather than being dependent on a single robot to build that map. One of the most obvious applications is search and rescue,” says Corbeth.To illustrate, we borrow this example from research being carried out at Carnegie Mellon University:<iframe width="640" height="360" src="https://www.youtube.com/embed/bAC8KGawZ7g??si=27CU6UboBVxOJi-P&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&amp;lt;span class="fr-mk" style="display: none;"&amp;gt;&amp;amp;nbsp;&amp;lt;/span&amp;gt;&amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;gt;&amp;amp;amp;nbsp;&amp;amp;lt;/span&amp;amp;gt;&amp;amp;amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;nbsp;&amp;amp;amp;amp;lt;/span&amp;amp;amp;amp;gt;</iframe><h3 id="isPasted">InDro's Custom Swarm</h3>We recently built and shipped four identical custom robots for North Carolina State University’s Department of Mechanical and Aerospace Engineering. Corbeth was the conduit, putting together a package that suited their very specific needs. It began with selecting one of our 18 available platforms.“They chose the Scout Mini, which is far and away our most cost-effective platform. And it’s unique&nbsp;in that it’s capable both indoors, like in their lab, but also for outdoor use,” says Corbeth.That, of course, was just the start. Each robot was outfitted with the <a href="https://indrorobotics.cahttps//indrorobotics.ca/commander/" target="_blank">InDro Commander</a>, which tucks a lot of power and flexibility within its IP54-rated enclosure. Commander houses the brains or the robot – which in this case is a very powerful piece of compute. Commander is completely customisable based on client requirements and contains ROS1 and ROS2 folders. (It also enables rapid integration and recognition of multiple sensors, and has the option of shipping with either or both of InDro’s autonomy stacks for outdoor or indoor use – think GPS-denied and SLAM. In this case, the client will be building their own autonomy stack, as that’s part of their research.)And this client wanted Commander equipped with a powerhouse.“They upgraded from a Jetson Xavier NX to the AGX Orin Developer Kit. That’s the best-in-class onboard compute you can add to a robot these days,” he adds.That’s not hyperbole. The AGX contains a&nbsp;2048-core NVIDIA Ampere architecture GPU with 64 Tensor Cores. Unfamiliar with Tensor Cores? They mean ultra fast AI processing.“Tensor Cores enable mixed-precision computing, dynamically adapting calculations to accelerate throughput while preserving accuracy and providing enhanced security,” states the <a href="https://www.nvidia.com/en-us/data-center/tensor-cores/" target="_blank">NVIDIA website</a>. “The latest generation of Tensor Cores are faster than ever on a broad array of AI and high-performance computing (HPC) tasks. From 4X speedups in training trillion-parameter generative AI models to a 30X increase in inference performance, NVIDIA Tensor Cores accelerate all workloads…”That AI computer is capable of – wait for it – 275 TOPS, or trillions of operations per second. (Yes, it’s kind of hard to get your head around that.)&nbsp;<h3>Sensors, Sensors, Sensors</h3>Corbeth worked with the client to outfit each swarm robot with the sensors needed for the job. Starting with the basics, each robot contains two pinhole cameras (one front, one rear) so the operator can remotely see the robot’s eye view using our proprietary dashboard, <a href="https://indrorobotics.ca/new-indro-controller-a-simple-solution-to-complex-robotics-missions/" target="_blank">InDro Controller</a>. For depth perception, we added ZED X stereo cameras which – like human eyes – see the world in three dimensions. These are used for obstacle avoidance (in conjunction with LiDAR, which we’ll get to).Connectivity is key with any robot. But when it comes to swarm robotics, it’s particularly important to have high-speed, low-latency communication not just with the controller, but between the robots themselves.“Obviously doing swarm requires 5G connectivity,” says Corbeth. “So there’s a 5G modem in each robot as well as our Wi-Fi development module. In addition to communication between the robots and with InDro Controller, this also means they can program these robots without having to hard-wire into them. In fact, students or researchers could upload algorithms from pretty much any location – they don’t need to be on-site or on the same WiFi network, which has been the traditional route.”Each robot features GPS and IMU modules, plus a Robosense Helios RS-5515 32-beam LiDAR module. These sense the environment in three dimensions by rapidly scattering and receiving the reflections from eye-safe laser beams. It allows the robot to safely navigate and map in completely unfamiliar and GPS-denied locations, and even carry out precision scans. We recently featured Robosense in <a href="https://indrorobotics.ca/robosense-sets-new-bar-for-affordable-powerful-lidar-sensors/" target="_blank">this post</a>, detailing how the quality is absolutely on par with the biggest names in the industry. (Selecting Robosense for this project saved the client close to $80,000 without losing any capabilities.)Outstanding in their field: A look at the InDro-built swarm fleet just prior to shipping to the client<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1721229465024-Image-7-2-scaled.jpeg.webp" style="width: 100%px;" class="fr-fic fr-dib"><h3 id="isPasted">InDro's Take</h3>It was a fascinating project to build a small swarm from the ground up for the specific needs of this client. We look forward to checking in with North Carolina State University once the research is fully underway – and will report back to you.“There’s no doubt that swarm robotics is an important tool, not only in R&amp;D but increasingly in real-world applications,” says InDro Robotics Founder and CEO Philip Reece. “Whether it’s search and rescue, mapping difficult environments at scale, or developing algorithms that will enable Connected Automated Vehicles in the Smart Cities of the future, swarms are synergic tools that produce outcomes far greater than the sum of their parts – and that do so with greater speed and efficiency.”Interested in exploring options for swarm robotics? Contact Luke Corbeth <a href="https://indrorobotics.ca/connectwithanexpert/" target="_blank">here.</a>

InDro completes design, build and delivery of robots for swarm research.

Robots for Swarm Research

Recent advances in robotics and artificial intelligence allow robots to carry out diverse tasks in manufacturing, warehouse operations, delivery services and more, benefitting humans who would otherwise undertake these physically demanding – and potentially dangerous – jobs.However, robots remain passive to their environment as they are not yet able to interact intelligently with or directly manipulate it to respond to events that may prevent mission completion. This shortfall becomes particularly pronounced in unpredictable environments where unexpected physical barriers or gaps can thwart robots’ progress toward mission objectives. In these situations, current autonomy algorithms are left with no recourse but to declare mission failure.&nbsp;<a href="https://engineering.wustl.edu/faculty/Yiannis-Kantaros.html" target="_blank">Yiannis Kantaros</a>, assistant professor in the Preston M. Green Department of Electrical &amp; Systems Engineering, has received a five-year, $591,457 Faculty Early Career Development (CAREER) Award from the National Science Foundation (NSF) to address these limitations. With the NSF’s support, Kantaros aims to advance robot intelligence and enable teams of robots to interact collaboratively with their perceived environment to overcome unanticipated obstacles and events.CAREER awards support junior faculty who model the role of teacher-scholar through outstanding research, excellence in education and the integration of education and research within the context of the mission of their organization.&nbsp;<a href="https://engineering.wustl.edu/faculty/research/index.html#nsf_career#nsf_career" target="_blank">One-third of current McKelvey Engineering faculty have received the award</a>.&nbsp;“Safety-critical applications such as disaster relief and search-and-rescue missions often occur in unstructured environments that are challenging for humans to navigate, requiring teams of robots to be deployed,” Kantaros said. “One major challenge is the unpredictable nature of these environments, which can hinder robots from completing tasks due to unexpected obstacles, debris, structural changes or physical gaps that block access to critical areas.“Unlike humans, robots lack the ability to interact intelligently with their surroundings to overcome these issues,” Kantaros continued. “My research will address this challenge by enabling teams of robots to interact with their environment and overcome challenges by intelligently reconfiguring it, for example, by pushing obstacles out of the way or using objects to bridge gaps.”By marrying artificial perception with decision-making capabilities, Kantaros will equip robots with the ability to not only perceive their surroundings but also to take a more active approach and modify surroundings to achieve their goals. A central component of his project is developing perception-based planning capabilities for robot teams operating in unknown and unstructured environments.“The proposed research will result in fundamental robot capabilities that will substantially enhance multi-robot planning methods in unstructured environments,” Kantaros said. “The developed planning algorithms will enable robot teams to interact with the perceived environment, accommodating imperfect AI-enabled perception and effectively responding to unforeseen events that could hinder task completion.”Imagine a fleet of robots working in tandem, dynamically reshaping their environment to navigate through treacherous terrain or deliver aid to remote locations with unprecedented efficiency and adaptability.&nbsp;“These autonomy algorithms are expected to offer unprecedented flexibility in various humanitarian and scientific applications in unknown environments, leading to enhancements in mission performance and robot safety, ultimately saving lives and resources,” Kantaros noted. “Important applications include search and rescue operations, disaster relief efforts, and environmental monitoring and surveillance.”

Yiannis Kantaros studies teams of autonomous robots working collaboratively in the Washington University Miniature City (WashU Mini-City), constructed in McKelvey Hall. (Photo: Yiannis Kantaros)

Yiannis Kantaros will enable teams of robots to interact collaboratively, perceive and respond to their environment with a CAREER Award from the National Science Foundation

Advancing robot autonomy in unpredictable environments

Industrial robots have brought programmable automation to factory floors and areas for storage, sorting, and packing, as businesses seek to increase their productivity and reduce operating overheads.More recently, collaborative robots (cobots) have arrived and created an extra, new world of application opportunities. Designed to work safely alongside humans, they bring the unique ability to act as co-workers contributing to the processes that must be completed to achieve the result.This softer image of the robot as a coworker – in contrast to those unapproachable, caged superhumans used for hazardous jobs like heavy lifting, welding, and paint spraying - promotes the adoption of cobots in a wider range of tasks. It raises the exciting prospect of combining human qualities such as natural dexterity and visual acuity, combined with the superior speed, repeatability, and strength of the robot.&nbsp;Cobots are also assumed to have a reliability advantage over humans, with no tendencies towards illness, lateness, or loss of concentration while working. As important as performance, efficiency, and safety, reliability needs to be designed in to ensure that the cobot will deliver the operator’s expected return on investment (ROI).<h2 dir="ltr">Motor-Drive Design Requirements</h2><img alt="A diagram of a diagram Description automatically generated" src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE3OTg1NjYwOTk2LTE3MTc5ODU2NjA5OTYucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" width="602" height="242" class="fr-fic fr-dii" style="width: 780px;">Figure 1. Cobot power, communication, sensing, and driving subsystems.Figure 1 describes the underlying electrical architecture of a typical industrial cobot. The motors and motor-drive circuits that move each of the cobot’s joints must be able to handle the necessary loads and movements without straining or overheating. Motors that offer a high torque-to-weight ratio and small form factor in relation to the demands at each joint are usually preferred to permit a lightweight and compact robot design that can be safe and easy to work with around humans.&nbsp;However, such requirements put a significant level of expectations on the motor-drive circuitry where the power electronics reside. The smallest possible module size is required, consistent with the goals of compactness and light weight. The size constraints limit thermal performance, which increases the stress on power semiconductor components. Frequent starting and stopping, as well as emergency stops when safety sensors indicate proximity of a human coworker, generate heat within the power semiconductors which must be dissipated to preserve the device.&nbsp;Cobot applications typically operate with a bus voltage of below 60V, translating to the power stage for the motor driver being driven by MOSFETs in a three-phase inverter topology. High energy efficiency is clearly an important consideration in current markets, so MOSFETs with low on-resistance are desirable. On the other hand, high availability and uptime are critical requirements that directly impact productivity and ROI, placing emphasis on choosing devices that are designed for ruggedness and reliability.&nbsp;When driving high-power motors, MOSFETs are vulnerable to failures caused by locked rotors, parasitic effects and brownout events that can expose the devices to excessive current and prolonged switching time. To withstand these threats, the chosen device needs a strong safe operating area (SOA) and avalanche robustness. Enhancing these parameters usually comes at the expense of increased RDS(on). Hence trading-off some efficiency in pursuit of superior thermal dissipation and higher excess-voltage, current-surge, and avalanche capabilities is warranted. Seeking all these qualities together with a compact footprint to minimise the size of the driver module narrows the selection of suitable devices.In fact, the constraints that determine the choices of power semiconductors for cobots are remarkably close to those for today’s heavy-duty cordless handheld power tools, such as high-power drills, impact wrenches, sanders, and grinders. These, too, must respect tight limitations on physical dimensions, while also delivering high current and withstanding intensive thermal loads particularly during repeated starting and stopping. German sports equipment pioneer Jaykay GmbH encountered similar limitations when creating its range of innovative e-trucks and paddle boards, and overcame them using Nexperia MOSFETs that are optimised for ruggedness and reliability in compact motor drives.<h2 dir="ltr">Power Semiconductor Selection</h2>Given the complexity of today’s application demands and the latest advances in device technology, focusing on the traditional RDS(on) x Qg figure of merit (FOM) is no longer the best starting point when selecting MOSFETs. The differentiating factor for every design is to find the best silicon-package combination.While optimisation and enhancement at the silicon level has blazed the trail for new MOSFETs with greater current and voltage handling capability than previous generations, the package technology plays a massive role in meeting the needs of high-power applications. Compared to traditional power packages, Nexperia’s LFPAK technology combines significantly lower parasitic inductance and electrical resistance with greatly improved thermal performance. The key innovation, enabling these improvements, is the copper clip bond that replaces conventional bond wires at the source connection, as shown in Figure 2. Compared to wire bonds, the copper clip bond prevents localised current crowding thereby eliminating hotspots and ensuring a more uniform current spread, while also acting as a heat sink to the die.&nbsp;<img alt="Full screen preview" src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE3OTg1NzgzMzM4LTE3MTc5ODU3ODMzMzgucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" width="485" height="450" class="fr-fic fr-dii" style="width: 780px;">Figure 2: Transparent view of a LFPAK56 package(LFPAK package image)Combining the latest silicon and LFPAK56 package has enabled a half-bridge MOSFET pair comprising high-side and low-side devices for 3-phase motor control in a 5mm x 6mm outline. The package uses flexible leads to improve overall reliability and the internal copper clip that connects the silicon drain and source terminals to the package leads has greater electrical performance and thermal dissipation than traditional bond wires, thereby boosting reliability. While the MOSFET drain tab remains the dominant path in dissipating heat from the die, the LFPAK source clip makes a significant contribution. Therefore, ensuring a suitable amount of copper in the PCB connections to the source pins helps to maximise the overall thermal performance.In high-power applications, where space constraints conflict with demands for high performance and robustness, copper clip package technology is an extremely valuable part of the solution that helps to maximise the value of the silicon. Robustness and reliability also comes down to thermal cycling and in some cases, the sheer level of current the devices need to handle in case of a fault condition.&nbsp;<h2 dir="ltr">Conclusion</h2>For robot makers, delivering a safe and cost-effective system to the marketplace calls for intelligent design decisions at all stages, including an accurate, flexible, and responsive control strategy, cost-effective and optimally sized motor, and reliable and efficient power semiconductors. &nbsp;Given the intended growth of this market, with the ever-increasing power density, the power semiconductors are a critical component to ensure that the motor and, by extension, the entire cobot is driven efficiently, reliably, and safely.&nbsp;

Industrial robots have brought programmable automation to factory floors and areas for storage, sorting, and packing, as businesses seek to increase their productivity and reduce operating overheads.

Engineering Cobot Motor Drives for Reliability and Efficiency

Robotic systems automate repetitive tasks, handling complex, powerful motions and working in environments that are dangerous or harmful to humans. More integrated, higher-performance microcontrollers (MCUs) enable higher power efficiency and smoother and safer motion with much higher accuracy, therefore increasing productivity and automation. For example, higher accuracy – sometimes within 0.1 mm – is important for applications that handle laser welding, precision coatings, or inkjet or 3D printing.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1717222891211-GUID-20240130-SS0I-FNRF-GSFH-MVQTTTVX1WBR-low.jpg" style="width: 100%px;" class="fr-fic fr-dib">The number of axes in a robot arm and the type of control architecture needed (either centralized or distributed, also known as decentralized) will ultimately determine the right MCU or motor-control integrated circuit (IC) for that system. Modern factories use a mix of robots with varying numbers of axes, defined as the degree of movement and rotation across x, y or z planes, in order to handle different phases of manufacturing; therefore, there are various control architectures throughout the factory floor.It’s important to choose MCUs with additional performance headroom to provide future scalability, as well as the capacity to support add-on features. Planning for scalability and additional features can reduce cost, time and complexity during the design process.In this article, I’ll explore centralized and distributed motor-control architectures and the design considerations for the integrated, real-time MCUs enabling them. To read about key motor drive design challenges for robotics, read our technical article, “<a href="https://e2e.ti.com/blogs_/b/industrial_strength/posts/how-motor-drive-innovations-are-helping-solve-robotic-movement-design-challenges" target="_blank">How motor drive innovations are helping solve robotic movement design challenges</a>.”<h2>A centralized architecture</h2>In a centralized system (<a href="https://www.ti.com/document-viewer/lit/html/SSZTD23#FIG_ZVY_HCV_G1C" target="_blank">Figure 1</a>), one MCU controls multiple axes. This approach is effective at handling heat dissipation in higher-power motor drivers (typically above 2-3 kW) that require large heat sinks and cooling fans. In this architecture, position data is usually acquired externally through a resolver board or aggregator connected to the encoders.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1717222997634-GUID-20240130-SS0I-NM5K-FRBN-8SQTBFPXBCCR-low.png" style="width: 100%px;" class="fr-fic fr-dib">Figure 1 Block diagram of a centralized motor-control architecture for multiple-axis systemsTypically, in this architecture, multiple power stages are on the same printed circuit board or in close proximity; thus, one MCU can control multiple axes. This approach simplifies the real-time control and synchronization between multiple axes, as there is no need for longer communication lines between multiple motor-control MCUs.The motor-control MCU/microprocessor in a centralized architecture mainly requires high-performing real-time processing cores such as Arm® Cortex®-R5F cores or digital signal processors, real time communication interfaces such as EtherCAT, and enough pulse-width modulation (PMW) channels and peripherals for voltage and current sensing. MCUs such as the AM2432 can enable scalable multiple-axis systems and offer real-time control peripherals for as many as six axes and real-time communications in a single chip.In the past, field-programmable gate arrays or application-specific integrated circuits were primarily used for centralized motor control in automated systems. However, the high level of integration and cost-effectiveness of Arm Cortex MCUs can help you meet your system’s performance requirements while also enabling design scalability and flexibility.While centralized control architectures can meet the performance and efficiency design requirements of high-powered automated systems such as heavy-payload industrial robots, they do require additional cables from the cabinet to the mechanical motor in the joint, as well as from the position sensors to the aggregators. Those wires do not only cost more, they also wear out and require maintenance.<h2>A decentralized architecture</h2>Decentralized architectures (<a href="https://www.ti.com/document-viewer/lit/html/SSZTD23#FIG_N21_HCV_G1C" target="_blank">Figure 2</a>) have recently grown in popularity for systems with lower power requirements, and have become the standard approach for collaborative robot (cobot) manipulators.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1717223024230-GUID-20240130-SS0I-W9T6-X4MK-G7DDQDDXB8LN-low.png" style="width: 100%px;" class="fr-fic fr-dib">Figure 2 Block diagram of a decentralized motor control architecture for single-axis systemsDecentralized architectures integrate multiple single-axis motor drives into each joint within the robot, connected and synchronized through a real-time communication interface such as EtherCAT. Each drive usually controls one single axis and handles certain safety functions locally. Therefore, each MCU needs real-time control and communication capabilities, motor-control peripherals for a single axis, three to six PWM channels, on-chip successive approximation register analog-to-digital converters, or delta-sigma modulator inputs.In these applications, the position sensor usually sits next to the MCU, so these MCUs need a digital or analog interface to read data from the position sensor. While this architecture requires more MCUs, cost reductions at the system level can be significant, since you’ll need less cabling between the power bus and communication interface. Modern real-time MCUs such as the TMS320F28P650DK integrate all necessary peripherals including safety peripherals, which can enable single- or two-chip solutions for an integrated axis in decentralized architectures with high performance at a small size.<h2>Conclusion</h2>While motors may not be the trendiest system in a robot – especially when compared to artificial intelligence-enabled systems – they are the “muscles” that keep factories moving. They might be the most crucial part of modern manufacturing, so selecting the right control device deserves a lot of consideration. As these devices become more integrated, additional capabilities such as edge computing and wireless connectivity may make their way into motor-control designs.

More integrated, higher-performance microcontrollers (MCUs) enable higher power efficiency and smoother and safer motion with much higher accuracy, therefore increasing productivity and automation.

How MCUs increase system performance in robot motor control designs

In this episode, we talk about how a fashion company spun-off from MIT has developed the world’s first truly one-size-fits-all garment.

Podcast: Novel Fabrics + Robots -- Sustainable One-size-fits-all Fashion Revolution

A pick and place robot in an industrial setup

Understanding the inception, evolution, composition, and types of pick and place robots and their impact on various industrial sectors 

Pick and Place Robots: An In-Depth Guide to Their Functionality and Applications

This article was discussed in our <a href="https://www.wevolver.com/profile/the.next.byte" rel="noopener noreferrer" target="_blank">Next Byte podcast</a>.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/7a6ed573-f46a-40bb-b396-d38d1207aa2e?dark=false" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> The full article will continue below.<hr>Until recently, bespoke tailoring — clothing made to a customer’s individual specifications — was the only way to have garments that provided the perfect fit for your physique. For most people, the cost of custom tailoring is prohibitive. But the invention of active fibers and innovative knitting processes is changing the textile industry.“We all wear clothes and shoes,” says Sasha MicKinlay MArch ’23, a recent graduate of the MIT Department of Architecture. “It’s a human need. But there’s also the human need to express oneself. I like the idea of customizing clothes in a sustainable way. This dress promises to be more sustainable than traditional fashion to both the consumer and the producer.”McKinlay is a textile designer and researcher at the Self-Assembly Lab who designed the <a href="https://selfassemblylab.mit.edu/4d-knit-dress" target="_blank">4D Knit Dress</a> with Ministry of Supply, a fashion company specializing in high-tech apparel. The dress combines several technologies to create personalized fit and style. Heat-activated yarns, computerized knitting, and robotic activation around each garment generates the sculpted fit. A team at Ministry of Supply led the decisions on the stable yarns, color, original size, and overall design.“Everyone’s body is different,” says Skylar Tibbits, associate professor in the Department of Architecture and founder of the Self-Assembly Lab. “Even if you wear the same size as another person, you're not actually the same.”<iframe width="640" height="360" src="https://player.vimeo.com/video/893959788" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;&nbsp;&nbsp;&nbsp;</iframe>4D Knit Dress: Transforming Style. Video: Self-Assembly Lab<h2 id="isPasted">Active textiles</h2>Students in the Self-Assembly Lab have been working with dynamic textiles for several years. The yarns they create can change shape, change property, change insulation, or become breathable. Previous applications to tailor garments include <a href="https://selfassemblylab.mit.edu/active-textile-tailoring" target="_blank">making sweaters</a> and <a href="https://news.mit.edu/2022/form-fitting-face-mask-lavender-tessmer-0718" target="_blank">face masks</a>. Tibbits says the 4D Knit Dress is a culmination of everything the students have learned from working with active textiles.McKinlay helped produce the active yarns, created the concept design, developed the knitting technique, and programmed the lab’s industrial knitting machine. Once the garment design is programmed into the machine, it can quickly produce multiple dresses. Where the active yarns are placed in the design allows for the dress to take on a variety of styles such as pintucks, pleats, an empire waist, or a cinched waist.“The styling is important,” McKinlay says. “Most people focus on the size, but I think styling is what sets clothes apart. We’re all evolving as people, and I think our style evolves as well. After fit, people focus on personal expression.”<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTM1NjY2MDgwLU1pbmlzdHJ5X0ZJTkFMX2RyZXNzZXMuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Designers explored this series of possible styles for the robotic heat activation of the 4D Knit Dress. Image courtesy of the Self-Assembly Lab.Danny Griffin MArch ’22, a current graduate student in architectural design, doesn’t have a background in garment making or the fashion industry. Tibbits asked Griffin to join the team due to his experience with robotics projects in construction. Griffin translated the heat activation process into a programmable robotic procedure that would precisely control its application.“When we apply heat, the fibers shorten, causing the textile to bunch up in a specific zone, effectively tightening the shape as if we’re tailoring the garment,” says Griffin. “There was a lot of trial and error to figure out how to orient the robot and the heat gun. The heat needs to be applied in precise locations to activate the fibers on each garment. Another challenge was setting the temperature and the timing for the heat to be applied.”It took a while to determine how the robot could&nbsp;reach all areas of the dress.“We couldn’t use a commercial heat gun — which is like a handheld hair dryer — because they’re too large,” says Griffin. “We needed a more compact design. Once we figured it out, it was a lot of fun to write the script for the robot to follow.”A dress can begin with one design — pintucks across the chest, for example — and be worn for months before having heat re-applied to alter its look. Subsequent applications of heat can tailor the dress further.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTM1NjgyOTQyLVJvYm90aWNfYXJtX2F0X3dvcmsuanBlZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">Robotic heat activation can be seen during a demonstration at Ministry of Supply in Boston. Photo: Olivia Mintz<h2>Beyond fit and fashion</h2>Efficiently producing garments is a “big challenge” in the fashion industry, according to Gihan Amarasiriwardena ’11, the co-founder and president of Ministry of Supply.“A lot of times you'll be guessing what a season's style is,” he says. “Sometimes the style doesn't do well, or some sizes don’t sell out. They may get discounted very heavily or eventually they end up going to a landfill.”“Fast fashion” is a term that describes clothes that are inexpensive, trendy, and easily disposed of by the consumer. They are designed and produced quickly to keep pace with current trends. The 4D Knit Dress, says Tibbits, is the opposite of fast fashion. Unlike the traditional “cut-and-sew” process in the fashion industry, the 4D Knit Dress is made entirely in one piece, which virtually eliminates waste.“From a global standpoint, you don’t have tons of excess inventory because the dress is customized to your size,” says Tibbits.McKinlay says she hopes use of this new technology will reduce the amount of waste in inventory that retailers usually have at the end of each season.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTM1NzEwMDIxLU1pbmlzdHJ5X0ZJTkFMX0RyYWZ0MDUgY29weS5qcGVnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">A complete 4D Knit Dress comes out of a Stoll industrial knitting machine after being produced in one piece, without any cutting or sewing. Photo courtesy of the Self-Assembly Lab.“The dress could be tailored in order to adapt to these changes in styles and tastes,” she says. “It may also be able to absorb some of the size variations that retailers need to stock. Instead of extra-small, small, medium, large, and extra-large sizes, retailers may be able to have one dress for the smaller sizes and one for the larger sizes. Of course, these are the same sustainability points that would benefit the consumer.”The Self-Assembly Lab has collaborated with Ministry of Supply on projects with active textiles for several years. Late last year, the team debuted the <a href="https://selfassemblylab.mit.edu/4d-knit-dress" target="_blank">4D Knit Dress</a> at the company’s flagship store in Boston, complete with a robotic arm working its way around a dress as customers watched. For Amarasiriwardena, it was an opportunity to gauge interest and receive feedback from customers interested in trying the dress on.“If the demand is there, this is something we can create quickly” unlike the usual design and manufacturing process, which can take years, says Amarasiriwardena.Griffin and McKinlay were on hand for the demonstration and pleased with the results. For Griffin, with the “technical barriers” overcome, he sees many different avenues for the project.“This experience leaves me wanting to try more,” he says.McKinlay too would love to work on more styles.“I&nbsp;hope this research project helps people rethink or reevaluate their relationship with clothes,” says McKinlay. “Right now when people purchase a piece of clothing it has only one ‘look.’ But, how exciting would it be to purchase one garment and reinvent it to change and evolve as you change or as the seasons or styles change? I'm hoping that's the takeaway that people will have.”

Caption:In late 2023 the MIT Self Assembly Lab and the high-tech fashion company Ministry of Supply debuted their 4D Knit Dress at the latter's store Boston, complete with a robotic arm working its way around a dress as customers watched. Credits:Image courtesy of MIT Self Assembly Lab

Developed by the Self-Assembly Lab, the 4D Knit Dress uses several technologies to create a custom design and a custom fit, while addressing sustainability concerns.

Is this the future of fashion?

It’s been some time since we’ve had an update on&nbsp;<a href="https://www.realliferobotics.com/" target="_blank">Real Life Robotics</a>, an Ontario-based firm gaining traction with its BUBS last-mile delivery platform.A lot has happened over the past year, with all of it appearing to point toward a solid trajectory.“We started off 2024 with a bang – with a really exciting story that has to potential to significantly change the current trajectory of Canadian small and medium businesses from coast to coast,” says CEO and Founder Cameron Waite.The story? Real Life Robotics (RLR) was one of eight Canadian startups selected by the&nbsp;<a href="https://www.cfin-rcia.ca/media/news-releases/foodtechnext-jan" target="_blank">Canadian Food Innovation Network</a> to receive funding through its&nbsp;<a href="https://www.cfin-rcia.ca/funding/foodtech-next" target="_blank">FoodTech Next</a> program. FoodTech Next offers funding for early-stage Canadian technology firms who seek to be part of – or sell to – the wider food industry. Through this program, RLR will receive nearly $250,000 to test its solution in real-world environments and validate the return on investment for the food sector.&nbsp;In the current economic landscape, Canadian businesses grapple with increased customer demands for last-mile delivery and reduced profitability. Currently, most rely on third-party companies like Skip The Dishes, DoorDash and Uber Eats, which charge 20-35 per cent commissions on each order.Automation is poised to play a critical role in helping businesses efficiently and sustainably move products from Point A to B (and even C, D, E…).“The overarching goal of FoodTech Next is to accelerate the commercialization of Canadian innovation by generating first demonstration opportunities,” says its website.The announcement made the news, including <a href="https://www.cbc.ca/listen/live-radio/1-92/clip/16042550" target="_blank">this CBC interview</a> with Waite.Below: Founder/CEO Cameron Waite making a pitch for the FoodTech Next program…<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA5OTIzNzIzMDY0LTIwMjQwMjE0LUNGSU4tRm9vZFRlY2gtMjAyNC0xNTAtc2NhbGVkLndlYnAiLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib"><h3 id="isPasted">THE PLATFORM</h3>When the RLR team carries out demonstrations across the country, it brings along BUBS – one of several solutions Real Life Robotics has available to customise and deploy. Out of the box, BUBS has a large cargo bay (50 kg) suitable for transporting everything from bottled water to medical supplies to prepared meals.BUBS can be remotely tele-operated over 5G networks with near-zero latency, or carry out deliveries autonomously. It can work indoors or out and has an IP protection that makes it impervious to inclement weather (with the exception of, say, a hurricane or tornado). It has excellent obstacle avoidance and a number of other features intended for safe operation near people. It’s also perceived as “friendly” – which is important as these devices are introduced in public spaces.BUBS can also be customised, pending client needs.“We’re a cargo and last-mile delivery robotics platform,” says Waite. “But clients inevitably and always have some sort of unique requirement to their business in order to fully adopt and scale automation. There’s no reason BUBS can’t be bigger or smaller, or a different shape or colour – or have its cargo bay modified to carry something unique or unusual that requires modification.”And the business model? Well, it’s not just about selling a single robot. It’s about that first robot proving its worth to the client in economic terms.“It’s nice to say: ‘I have a robot,'” says Waite. “But our goal is really to have a robot or automation solution that has a positive business impact so that you want to scale and buy more of them. Otherwise, you buy just one.”&nbsp;<h3>MORE OPTIONS EN ROUTE</h3>It’s not just BUBS. Real Life Robotics, says Waite, has another solution it’s about to unveil: A quadruped delivery robot. Having four legs instead of wheels means deliveries are not limited by stairs or rough terrain. And, in a world where businesses increasingly want their space or inventory scanned, this robot is capable of carrying out those tasks as it goes about its delivery work.While food delivery in public spaces is clearly the focus of Real Life Robotics (and is tied to the federal grant), Waite emphasises that BUBS can be put to use in far wider use-cases.“Right now our focus is the local delivery side of the food industry,” says Waite. “But beyond that, it could also be products – like hats at a hockey game or bottled water&nbsp;at a. convention centre. It all really comes down to increasing the margins for our clients while ensuring a high-quality memorable delivery experience for the customer.”Below: BUBS, making things easier…<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA5OTIzODc2NTk4LUN1c3RvbWVyLWxvYWRzLUJVQlMtanBnLndlYnAgY29weS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib"> <h3 id="isPasted">NEW TEAM AND FUNDING ROUND</h3>Though we’ve been focusing on the robots, you can’t build a business without a team. And there, Waite says Real Life Robotics has been fortunate in attracting some A-level talent who share his vision.The company has acquired <a href="https://uwaterloo.ca/scholar/bjdehart" target="_blank">Brandon J. DeHart</a> as Chief Technical Officer. DeHart is the head of the University of Waterloo’s <a href="https://uwaterloo.ca/robohub/" target="_blank">RoboHub</a>, the epicentre of all things robotic at the university. (Waterloo’s Engineering department, according to the RoboHub website, “has the largest and most active robotics and automation research group in Canada, supporting Canada’s largest robotics and automation cluster.”)So DeHart comes with impeccable credentials, and has the expertise to both modify products and engineer completely new solutions for RLR. He has both a PhD and a Masters degree.“In terms of a robotics pedigree, this guy is top of the tops,” says Waite. “So we’re really, really excited about having him on board as CTO and I’m really excited to see what we can build together.”You can read more about DeHart in this <a href="https://www.realliferobotics.com/news/supercharging-the-real-life-robotics-team" target="_blank">RLR blog post</a>. And you can look at him, in the photo below…<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1709924018119-Brandon-J-DeHart-RLR-jpg.webp" style="width: 100%px;" class="fr-fic fr-dib"><h3 id="isPasted">NEW CFO, HEAD OF GROWTH</h3>Real Life Robotics has also attracted a new Chief Financial Officer, as well as a Head of Growth.The CFO is Ian Watson. Based in the UK, Watson spent 25 years with <a href="https://www.pwc.com/ca/en/" target="_blank">Price Waterhouse Cooper</a> before venturing into the startup space. He has owned and managed several startups, and is a Canadian Chartered Accountant. Watson holds a Master’s degree in Business (McMaster University) and a BA in Economics (Wilfrid Laurier University).“He’s deeply experienced in the financial and accounting worlds, plus he knows startups and how to help businesses grow strategically,” says Waite.Sharif Virani is the new Head of Growth – and appears to be a great fit. Before joining RLR, Virani held a similar position with&nbsp;<a href="https://tinymile.ai/" target="_blank">Tiny Mile</a>, which deploys small pink robots for food delivery. The company is currently operating in Miami – and had previously been running its operation in Toronto. If you ever saw one of those little robots on the news or social media, odds are Virani was behind that.“He’s a serial entrepreneur in the restaurant industry space – so he understands that market really well,” says Waite.And those are just three of the recent additions to the RLR team. A total of 10 people are now part of the company, with several on the sales and operations side. Together, they bring a total of 53 years of direct experience in the robotics industry.&nbsp;<h3>FUNDING ROUND</h3>As Real Life Robotics prepares for its next phase of growth, the company is currently in a Series A raise round.“The traction we’ve received – from the partnerships we’ve been developing, the grants we’ve been winning, and the client revenue we’re now generating – tells a really strong story to the investment community,” says Waite.Below: a 360° video tour of the University of Waterloo’s RoboHub. Be sure to scroll around to see the big picture…<iframe width="640" height="360" src="https://www.youtube.com/embed/O_7KSGdVLoc??si=GRuboC_a2bKF_SND&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&amp;lt;span class="fr-mk" style="display: none;"&amp;gt;&amp;amp;nbsp;&amp;lt;/span&amp;gt;</iframe><h3 id="isPasted">WAIT A MINUTE</h3>Cameron Waite talks a good game. But how do we safely introduce robots onto public roadways? Though Toronto now has a pilot project underway to evaluate such delivery robots, it’s not like they’re going to be embraced tomorrow.So how can Waite be so optimistic, when public acceptance (and even permission from municipalities) is not guaranteed?The answer, he says, has been in ground work and relationship building. RLR is also part of multiple stakeholder organisations that have been actively engaging on precisely this issue. The company is part of the following groups:<ul><li><a href="https://www.urbanroboticsfoundation.org/" target="_blank">Urban Robotics Foundation</a></li><li><a href="https://www.roboticscouncil.ca/" target="_blank">Canadian Robotics Council</a></li><li>Founding member of the&nbsp;<a href="https://uwaterloo.ca/robohub/professional-partnership-program" target="_blank">RoboHub Professional Partnership Program</a></li><li>Founding member the Toronto Autonomous Mobility Working Group</li></ul>That last one? Its members are <a href="https://gatik.ai/" target="_blank">Gatik</a>,&nbsp;<a href="https://www.tesla.com/" target="_blank">Tesla</a>,&nbsp;<a href="https://www.uber.com/ca/en/" target="_blank">Uber</a> and&nbsp;<a href="https://www.realliferobotics.com/" target="_blank">Real Life Robotics</a>.Lobbying groups are one thing. But getting the automated rubber to meet the road is another. Here, Waite reveals that RLR is ahead of the game: It has permission from five municipalities across the country to operate and test its vehicles in public spaces.“That’s really what this government grant is targeted toward: Exploring the validity of robots in public environments,” says Waite.&nbsp;<h3>MAJOR PARTNERSHIPS</h3>Waite also says RLR has some significant partners onboard, including “an incredibly large restaurant space company.”“This partner not only gives us some amazing insight and data that we can utilise to ensure that this technology truly has ROI and business value to that industry. They also give us some scale and scope as we grow.”But why not name names?“The reason is simply that we’re moving a little bit faster than their marketing team can handle.”Waite promises some significant announcements on this front soon, and deployment of RLR robots in the streets in the near future. Again, Waite won’t reveal details but pledges that the use-case is “really cool” and will grab headlines.“By the summer, for sure, Real Life Robotics robots will be in the field – and there will be a huge story around it.”He adds that RLR has Indro Robotics as an engineering partner, and access to&nbsp;<a href="https://indrorobotics.ca/indro-forge-prepped-for-next-phase-expansion/" target="_blank">InDro Forge</a>, a rapid fabrication and prototyping facility based in Ottawa. These two partnerships, he says, speed custom fabrication for clients, in addition to builds of entirely new robots.Below: BUBS on the street…doing good work<iframe width="640" height="360" src="https://www.youtube.com/embed/kCwf5IxBOQE??si=K4hr3BrPVpKTftsV&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&amp;lt;span class="fr-mk" style="display: none;"&amp;gt;&amp;amp;nbsp;&amp;lt;/span&amp;gt;</iframe><h3 id="isPasted">INDRO’S TAKE</h3>We’ve been watching Real Life Robotics with interest over the past year and we’re pleased to see the terrific progress that’s been made.“Robotics and automation can make things much easier for many businesses, but municipal permissions for more than a one-off demo have traditionally been tricky in this space,” says InDro Robotics CEO Philip Reece.“That’s starting to shift, but you can’t have public adoption and acceptance until these devices are actually in the field. And they can’t be put in the field without the vision and cooperation of municipalities. We’re pleased to see that Real Life Robotics has made gains in this area, and congratulate them on winning that important grant. We look forward to seeing RLR robots in the streets – somewhere – this summer.”For more information on RLR, including an investor deck, contact Cameron Waite&nbsp;<a href="Mailto:cameron@realliferobotics.com">here</a>.

New Team, Research Grant and Partners

Real Life Robotics Expands

Industry 5.0 is a new phase of industrialization that builds upon the fourth industrial revolution (Industry 4.0) by adding a people-centric approach. It is characterized by the extensive use of automation, data exchange, and smart manufacturing technologies in a manner that fosters the well-being of the workers. It bridges the gap of the unfulfilled promise of full automation from Industry 4.0 in processes where a human in the loop is still needed, &nbsp;increasing the competitiveness of the industry and helping attract the best talents.The ultimate goal is to create human-centric, efficient, flexible, and responsive production environments that can quickly adapt to changing market demands.To achieve this goal, this next generation of manufacturing environments leverages the latest available technologies to the entire production process. One of the key operational aspects of manufacturing is intralogistics -understood as the management, control, and optimization of internal material and information flows.&nbsp;Intralogistics 5.0 leverages automated material handling, Internet of Things (IoT), data analytics, and Artificial Intelligence (AI) to create smart, interconnected, data-driven production environments and empower human workers.&nbsp;This intralogistics evolution is not only adding efficiency to industrial processes but is also driving a shift in manufacturing, from large, rigid systems into modular, flexible, and robot-supported self-optimizing ones.<h2>Optimizing Material Handling with Visual AI-Enabled Robotic Vehicles</h2>Autonomous Mobile Robots (AMRs), equipped with Visual AI and orchestrated through a Fleet Management System (FMS) are at the forefront of this transformation. The shape and function of these AMRs range from autonomous forklifts, shuttle systems, tugger trains, and stackers to platform or mouse robots, goods-to-person, and pick-assist robots. Their material transport activities are ideally tracked directly by the Enterprise Resource Planning (ERP) system of the factory.AMRs are designed to navigate with agility and accuracy through complex factory environments using a combination of sensors, cameras, and real-time data processing. The integration of Visual AI enables them to perceive their surroundings, identify objects and obstacles, make informed decisions, and share information in real-time, adding a number of benefits at each step of the process:<ul><li id="isPasted">Enhanced Goods-In: Vehicles equipped with Visual AI contribute to this stage by autonomously transporting goods from receiving areas to designated storage locations reducing manual work and extending operation hours. Additionally, the visual input ensures accurate identification and categorization of incoming goods, feeding the ERP, and laying the foundation for precise inventory management.</li><li>Optimized Storage and Retrieval: Visual AI allows robotic vehicles to analyze available storage space and stack goods efficiently, optimizing storage capacity, ensuring timely retrieval of items, and providing real-time inventory updates.</li><li>Streamlined Production Supply: Visual AI makes autonomous intralogistics vehicles inherently flexible, thus they can optimize routes dynamically, ensuring a continuous and streamlined supply of materials to production lines.&nbsp;</li><li>Accurate Order Picking: Visual AI technology supports workers in order picking, significantly reducing error rates and ensuring accurate order fulfillment.‍</li><li>Efficient Dispatch and Goods-Out: Visual AI enables robotic logistics vehicles to perform order verification and optimize load onto transportation vehicles, adding efficiency to the preparation and organization of finished goods for delivery to customers or distribution centers.</li></ul><h2>Flexible Manufacturing</h2>Besides the optimization across the material handling process, and on account of their inherent reconfigurability, Visual AI AMRs can flexibly support diverse production scenarios, such as batch, mass, or customized production, and different manufacturing layouts, from linear or modular to cellular.&nbsp;Serving as flexible links between individual production stages, these robots are also fundamental in the transition from traditional rigid conveyor-belt-based manufacturing to flexible-cell manufacturing, where production units can be reconfigured and rearranged without downtime.&nbsp;This flexible movement and connection of production cells, create dynamic and modular production environments that can adapt to different product variants and volumes, to respond to changing customer demands and market conditions.<h2>Challenges on the Path Towards the Factories of the Future</h2>Visual AI allows operating this Factory of the Future today. While the technology is already here, its adaption and implementation are only starting to pick up speed. The transition to Intralogistics 5.0 and new levels of automation is mostly gradual and incremental as it requires interoperability, scalability, and human-robot collaboration.<h3>Interoperability</h3>Many manufacturing facilities and warehouses are currently operating with mixed fleets that consist of both manned and unmanned material-handling vehicles, and an initial and crucial challenge in the path toward Intralogistics 5.0 is to integrate both types of shop floor traffic into an all-encompassing FMS and ERP.At this stage, equipping manned vehicles -like forklifts, tuggers, or stackers- with Visual AI-enabled Real Time Location System (RTLS) to track their location and feed the system with the visual data they capture would enable an FMS to orchestrate floor traffic holistically.Adding observability to manned traffic and integrating it into an FMS cancels out typical shortcomings of mixed fleets such as human drivers colliding with AMRs, inefficient manned routing unobservable by the FMS, or inventory compromised by human errors.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAyOTAzNTU1NTQzLU1hbm5lZF9Vbm1hbm5lZF9UcmFmZmljX0ludGVncmF0aW9uX1NldmVuc2Vuc2VfUm9ib3RpY3MucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib">Visual AI-enabled RTLS feeding manned traffic data into an FMSThe Visual AI-enabled RTLS adds value in manufacturing and warehousing unlocking completely new opportunities in material handling applications, where some operations will continue to be performed using manned vehicles for the next 20+ years.To make the benefits of holistically managed mixed fleets broadly accessible to the market, Sevensense Robotics is developing a Visual AI-enabled RTLS, which is already available to early adopters.<h3>Scalability</h3>In many cases, as part of their automation journey, manufacturers opt for initially commissioning AMRs at a small scale in a dedicated sector of their facilities. However, expanding the AMRs’ operating space from a wing of a plant or to the entire factory has traditionally been time-consuming.Until now, it implied a complex and disruptive endeavor of stopping operations and dedicating specialized professionals to the map expansion. including running a vehicle in manual mode to scan the new areas of the facility where robots will operate, and then manually editing and merging the new maps.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1702909540635-Continous+map+updates+V2.gif" style="width: 302px;" class="fr-fic fr-dib">Lifelong Visual SLAM enables seamless map updatesTo solve this scalability blocker, Sevensense has developed a solution that makes the map expansion seamless. Combining continuous map updates -also known as Lifelong Visual SLAM- with swarm intelligence, robots equipped with this technology can automatically map the new sections of the environment and share the extended map with the rest of the fleet. All they need to start their exploration is an anchor point in the existing map and the expansion is computed on-the-fly.By automatically incorporating relevant changes in the environment, Sevensense’s Lifelong Visual SLAM also offers the key benefit of lifelong robust, high precision localization &lt;5mm (&lt;0.2in) without markers, reflectors, or beacons, allowing the robots to operate in dynamic, ever-changing environments.<h3>Human-Robot Collaboration</h3>An increasing number of companies are incorporating AMRs into their operations. However, the lack of seamless human-machine collaboration solutions poses the need for dedicated robotic operations zones or the introduction of robotic shifts, restricting their flexibility and efficiency while also limiting the ROI.Visual AI AMRs have the built-in capability of understanding the context of the environment, distinguishing between unobstructed paths, obstacles -and types of obstacles-, and adapting their behavior accordingly in real-time.When the robot encounters an obstacle, the system identifies the type of object, then estimates its velocity and predicts its trajectory, calculates the most efficient path, and automatically performs an obstacle avoidance maneuver, rerouting the robot.This navigation mode can boost efficiency, in particular in scenarios when robots share the shop floor with other robots, but it may not be the optimal solution when robots share the shop floor with people.&nbsp;On the one hand, as human behavior is less predictable, robots have less visibility of a person’s movement intent than they have on other mobile robots or RTLS-tracked manned vehicles. On the other hand, having a robot overtake people while walking across a facility may be perceived as unpredictable and unsafe, making human workers feel anxious.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAyOTAzNzkyNTUyLUh1bWFuX01hY2hpbmVfQ29sbGFib3JhdGlvbl9Sb2JvdF9OYXZpZ3Rpb25fU2V2ZW5zZW5zZV9Sb2JvdGljcy5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib">Visual AI is key to developing collaborative AMRsSolving the human-robot collaboration challenge is key to enabling Industry 5.0 and is motivating Sevensense’s work to solve this issue by combining scene understanding and a people-centric approach, to turn AMRs into Co-AMRs. The solution involves Visual AI perceiving the obstacle as a person and then commanding the robot to perform a people-friendly behavior such as stopping and waiting for the person to continue their movement or complete their task.To learn more about Sevensense Robotics and its Visual AI technology, visit <a href="https://www.sevensense.ai/" target="_blank" rel="noopener noreferrer">sevensense.ai</a>

Visual AI enables Autonomous Mobile Robots to fulfil Industry 5.0 requirements

Visual AI is applied to overcome the obstacles in the path of the Factories of the Future

Autonomous Mobile Robots Powering Industry 5.0

Engineers at Princeton University and Google have come up with a new way to teach robots to know when they don’t know. The technique involves quantifying the fuzziness of human language and using that measurement to tell robots when to ask for further directions. Telling a robot to pick up a bowl from a table with only one bowl is fairly clear. But telling a robot to pick up a bowl when there are five bowls on the table generates a much higher degree of uncertainty — and triggers the robot to ask for clarification.Because tasks are typically more complex than a simple “pick up a bowl” command, the engineers use large language models (LLMs) — the technology behind tools such as ChatGPT — to gauge uncertainty in complex environments. LLMs are bringing robots powerful capabilities to follow human language, but LLM outputs are still frequently unreliable, said <a href="https://engineering.princeton.edu/faculty/anirudha-majumdar" target="_blank">Anirudha Majumdar</a>, an assistant professor of <a href="https://mae.princeton.edu/" target="_blank">mechanical and aerospace engineering</a> at Princeton and the senior author of a <a href="https://openreview.net/forum?id=4ZK8ODNyFXx" target="_blank">study</a> outlining the new method.“Blindly following plans generated by an LLM could cause robots to act in an unsafe or untrustworthy manner, and so we need our LLM-based robots to know when they don’t know,” said Majumdar.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1701898003603-knowno-ambiguities_WEB-2048x871.jpg" style="width: 766px;" class="fr-fic fr-dib">The researchers tested their method on a simulated robotic arm and on two types of robot hardware. The most complex experiments (left) involved a robotic arm mounted on a wheeled platform and placed in an office kitchen with a microwave and a set of recycling, compost and trash bins. Another set of experiments used a tabletop robotic arm (right) tasked with sorting a set of toy food items into two different categories; a setup with a left and right arm (center) added an additional layer of ambiguity. Images by the researchersThe system also allows a robot’s user to set a target degree of success, which is tied to a particular uncertainty threshold that will lead a robot to ask for help. For example, a user would set a surgical robot to have a much lower error tolerance than a robot that’s cleaning up a living room.“We want the robot to ask for enough help such that we reach the level of success that the user wants. But meanwhile, we want to minimize the overall amount of help that the robot needs,” said&nbsp;<a href="https://allenzren.github.io/" target="_blank">Allen Ren</a>, a graduate student in mechanical and aerospace engineering at Princeton and the study’s lead author. Ren received a&nbsp;<a href="https://www.corl2023.org/awards" target="_blank">best student paper award</a> for his Nov. 8 presentation at the Conference on Robot Learning in Atlanta. The new method produces high accuracy while reducing the amount of help required by a robot compared to other methods of tackling this issue.The researchers tested their method on a simulated robotic arm and on two types of robots at Google facilities in New York City and Mountain View, California, where Ren was working as a student research intern. One set of hardware experiments used a tabletop robotic arm tasked with sorting a set of toy food items into two different categories; a setup with a left and right arm added an additional layer of ambiguity.The most complex experiments involved a robotic arm mounted on a wheeled platform and placed in an office kitchen with a microwave and a set of recycling, compost and trash bins. In one example, a human asks the robot to “place the bowl in the microwave,” but there are two bowls on the counter — a metal one and a plastic one.The robot’s LLM-based planner generates four possible actions to carry out based on this instruction, like multiple-choice answers, and each option is assigned a probability. Using a statistical approach called conformal prediction and a user-specified guaranteed success rate, the researchers designed their algorithm to trigger a request for human help when the options meet a certain probability threshold. In this case, the top two options — place the plastic bowl in the microwave or place the metal bowl in the microwave — meet this threshold, and the robot asks the human which bowl to place in the microwave.In another example, a person tells the robot, “There is an apple and a dirty sponge … It is rotten. Can you dispose of it?” This does not trigger a question from the robot, since the action “put the apple in the compost” has a sufficiently higher probability of being correct than any other option.“Using the technique of conformal prediction, which quantifies the language model’s uncertainty in a more rigorous way than prior methods, allows us to get to a higher level of success” while minimizing the frequency of triggering help, said the study’s senior author&nbsp;<a href="https://engineering.princeton.edu/faculty/anirudha-majumdar" target="_blank">Anirudha Majumdar</a>, an assistant professor of mechanical and aerospace engineering at Princeton.Robots’ physical limitations often give designers insights not readily available from abstract systems. Large language models “might talk their way out of a conversation, but they can’t skip gravity,” said coauthor&nbsp;<a href="https://andyzeng.github.io/" target="_blank">Andy Zeng</a>, a research scientist at Google DeepMind. “I’m always keen on seeing what we can do on robots first, because it often sheds light on the core challenges behind building generally intelligent machines.”Ren and Majumdar began collaborating with Zeng after he gave a talk as part of the&nbsp;<a href="https://robo.princeton.edu/seminar" target="_blank">Princeton Robotics Seminar</a> series, said Majumdar. Zeng, who earned a computer science Ph.D. from Princeton in 2019, outlined Google’s efforts in using LLMs for robotics, and brought up some open challenges. Ren’s enthusiasm for the problem of calibrating the level of help a robot should ask for led to his internship and the creation of the new method.“We enjoyed being able to leverage the scale that Google has” in terms of access to large language models and different hardware platforms, said Majumdar.Ren is now extending this work to problems of active perception for robots: For instance, a robot may need to use predictions to determine the location of a television, table or chair within a house, when the robot itself is in a different part of the house. This requires a planner based on a model that combines vision and language information, bringing up a new set of challenges in estimating uncertainty and determining when to trigger help, said Ren.The paper, “<a href="https://openreview.net/forum?id=4ZK8ODNyFXx" target="_blank">Robots That Ask for Help: Uncertainty Alignment for Large Language Model Planners</a>,” was presented Nov. 8 at the Conference on Robot Learning. In addition to Ren, Zeng and Majumdar, coauthors include Anushri Dixit and Alexandra Bodrova of Princeton; and Sumeet Singh, Stephen Tu, Noah Brown, Peng Xu, Leila Takayama, Fei Xia, Jake Varley, Zhenjia Xu and Dorsa Sadigh of Google DeepMind. The research was supported in part by the U.S. National Science Foundation and the Office of Naval Research.

Engineers at Princeton University and Google have come up with a new way to teach robots to know when they don’t know and ask for clarification from a human. Photo by the researchers

Modern robots know how to sense their environment and respond to language, but what they don’t know is often more important than what they do know. Teaching robots to ask for help is key to making them safer and more efficient.

How do you make a robot smarter? Program it to know what it doesn't know.

So: You’ve got a great idea for a new technology product or process.That’s the first step: A concept that you’ve put some thought into. Of course, there’s a long road ahead before that brilliant idea becomes an actual commercial product. But how do you gauge that progress as you move along the development path? How would you describe where you’re at in a way that others might quickly grasp?Luckily, there’s a tool for that. It’s called the Technology Readiness Levels scale, or TRL.“It’s a&nbsp;standard measuring stick for everyone to communicate where they are with development,” explains InDro Robotics Engineering Lead Arron Griffiths.The TRL tool was first developed by NASA researcher Stan Sadin back in 1974 with seven basic levels. It would take another 15 years before the levels were formally defined, during which time two additional levels were added. There are now nine steps up the ladder, where TRL 9 is the equivalent of a working product that could be mass-produced or commercialized.Which means, of course, that Level 1 is at the very beginning of the technology development process.“Level 1 is universally seen as a napkin idea – where you’ve jotted down a concept,” says Griffiths.That’s a perfect analogy for TRL 1.For greater clarity, each level on the scale offers a short definition, a description, and examples of activities. The short definition for Level 1 is “Basic Principles Observed and Reported.” The description is “Lowest level of technology readiness. Scientific research begins to be translated into applied research and development (R&amp;D).”In terms of examples, Level 1 “Activities might include theoretical studies of a technology’s basic properties.” And yes, that could include a napkin sketch.Below: Aerospace is one of many industries to use TRLs. The noise-reducing chevron nozzles seen on the cowling below would have gone through each of the nine levels. Photo via Wikimedia Commons by John Crowley.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAxNzg5OTkyNjc5LUdlbmVyYWxfRWxlY3RyaWNfR0VueC0xQl8xNDIzMzQ5Nzc3Nl9jcm9wcGVkLWpwZWcud2VicCIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 766px;" class="fr-fic fr-dib"> <h3 id="isPasted">Moving Up The TRL Scale</h3>Great! You’ve got that napkin sketch done.Obviously there’s a lot to do between that initial idea and a finished product suitable for commercialization. To get to TRL 2, you simply need to put more thought into it. You’re not actually building or programming yet, just putting greater clarity and focus on what you hope to accomplish.TRL 2 is defined as “Technology concept and/or application formulated.” Here’s its <a href="https://www.canada.ca/en/department-national-defence/programs/defence-ideas/technology-readiness-level.html" target="_blank" rel="noopener noreferrer">description</a>:“Invention begins. Once basic principles are observed, practical applications can be invented. Applications are speculative, and there may be no proof or detailed analysis to support the assumptions.”You could think of this stage as refining the idea, with activities limited to research and/or analytical studies.TRL 3 means you’re actually beginning the R&amp;D process. This might include some lab or analytical studies. At this stage you’re trying to validate predictions you’ve made about separate elements or components of the technology. The components you’re working with aren’t yet integrated – nor is it expected that the components you’re working with are at their final version. <h3>Two TRL Scales</h3>Before we move along, it’s worth noting there are actually two different TRL scales in use. The first (and the one we’re using here) is the NASA scale. But the European Union has its own TRL scale.&nbsp;“So there is some cloudiness,” explains Griffiths. “Typically the top and bottom of the scales are the same, but the middle moves around a bit. You have to be sure people are reading from the same scale. Typically when I talk to a client, I will show them the scale I am using.”Griffiths also emphasizes that during R&amp;D, the phase of development doesn’t always slot neatly into one of the TRL stages.&nbsp;“It’s typical to say: ‘We’re roughly about TRL 6’ – it’s not an exact science.”Below: The&nbsp;<a href="https://indrorobotics.ca/commander/" target="_blank" rel="noopener noreferrer">InDro Commander</a>&nbsp;module, with LiDAR sensor. This popular commercial product, which allows for rapid integration of ROS-based robots and sensors (and more), is TRL 9.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAxNzkwMTI5MzIxLUluRHJvLUNvbW1hbmRlci0xLjYtY29weS5qcGcud2VicCIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib" alt="InDro Commander Platform Agnostic Brain Box"> <h3 id="isPasted">Climbing The Ladder&nbsp;</h3>American inventor Thomas Edison once said: “Genius is one per cent inspiration and ninety-nine per cent perspiration.” The same could be said of the process of inventing a product for commercialization. Once that napkin sketch is done (the one per cent), there’s still a lot of methodical slogging ahead. (Trust us, we know.)TRL 4 is the stage where you’re putting things together. Basic components are integrated and readied for testing in a simulated environment. The short definition, via Canada’s <a href="https://www.canada.ca/en/department-national-defence/programs/defence-ideas/technology-readiness-level.html" target="_blank">Department of National Defence</a>, is “Component(s)/subsystem(s) and/or process validation in a laboratory environment.”The logical progression continues with the next step.“TRL 5 means it’s ready for testing in a lab environment,” explains InDro Lead Engineer Griffiths. He also adds that this middle stage – TRL 4 through 7 – “is always the difficult part.”Once TRL 5 is passed, it’s time to start seeing if the integrated components will work together in a simulated or lab environment. At this stage, TRL 6, the product is considered to be getting pretty close to its desired configuration. Yes, there will be further tweaking to come, but you’re getting there.Below: InDro’s Street Smart Robot (the large white unit). The product has been built but not yet deployed in winter conditions. This would be at TRL 7. Every other robot in this image would have made it to TRL 9.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1701791421275-DSCF4031-jpg.webp" style="width: 731px;" class="fr-fic fr-dib"> <h3 id="isPasted">Nearly There!</h3>All that hard work has been paying off. Your product is assembled and has been tested in simulation or other lab environment. Now it’s time to get it out into the real world to see how it performs. Congratulations, you’ve reached TRL 7, where “Prototype system [is] ready (form, fit, and function) for demonstration in an appropriate operational environment.”“TRL 7 is more like a long-term&nbsp;deployment. Once you can show it to be working in a real-world environment – outside of the lab – then you get to Levels 8 and 9,” says Griffiths.These final two levels are usually pretty exciting. Once the product/solution has been proven to work in its final form – and in the environment where it’s expected to be deployed as a product – you’ve reached TRL 8. Just one more to go.&nbsp;<h3>The Final Level</h3>Remember that Street Smart Robot you just saw a picture of? Well, it’s ready to go. And once the wintry conditions take hold in Ottawa, we’ll be operating that machine in ice and snow on Ottawa streets. Specifically, on bike lanes in Ottawa, where it will detect hazardous conditions (including potholes) that might pose challenges for safe cycling. City of Ottawa maintenance crews will then be notified of the problem (and its location) so they can address the issue. (You can read more about the SSR <a href="https://indrorobotics.ca/streetsmart-inspectionrobot/" target="_blank">here</a>.)And once the SSR is operating smoothly in those intended conditions? We will have achieved TRL 9, meaning “Actual solution proven through successful deployment in an operational setting.” <h3>Not Always A Smooth Path</h3>It’s easy enough to describe these levels. And in doing so, it can appear to be a straightforward, linear path where engineers move seamlessly from one level to the next. Reality is not quite so simple. Depending on the project, progress in the early stages can be made very rapidly.“Most people get up to Level 5 fairly quickly,” says Griffiths. “You can even get to Level 5 in a day if you’re doing software development – you can literally go from an idea all the way up to a basic rudimentary prototype.”But – as flagged earlier – things get a little trickier once you hit those middle levels.“You can think of it as walking up a hill to Level 5,” he says. “Then there’s this valley. A lot of stuff dies in Level 6 and 7. There’s not a lot of success there because once you push the technology into actual environments the success rate is very low. So a lot of time is spent in Levels 5 and 6 trying to make a system that can make it to Level 7 successfully, and then on to Level 8 – where you’re essentially across the valley.”Below: A graphic outlines the short definitions of Technology Readiness Levels<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1701791491699-TRL+Scale.png" style="width: 766px;" class="fr-fic fr-dib"> <h3 id="isPasted">InDro's Take</h3>The TRL scale is extremely useful in the R&amp;D world, in that it concisely conveys where a product is along the path to commercial development. And while it’s great for engineers, it’s also useful to help clients understand where one of our products is along that journey.We’ve scaled this ladder many times over the years. Sometimes it’s a relatively easy climb. But, like all Research and Development companies, we’ve also had a few products that never made it beyond the valley Arron Griffiths described. That’s R&amp;D.“The Technology Readiness Level scale is a really useful tool, and part of our daily language at InDro Robotics,” says CEO Philip Reece. “Each level represents unique challenges – and that valley Arron described can sometimes be a disappointing bit of landscape. But we learn something even with the occasional failure.“Thankfully, we have a creative and tenacious engineering team that seems to thrive on difficult challenges – and InDro now has a growing stable of products that have achieved TRL 9 and gone on to commercial success.”If you’re working on your own project and would like to know where it is on the TRL scale, you can use this <a href="https://ised-isde.canada.ca/site/clean-growth-hub/en/technology-readiness-level-trl-assessment-tool" target="_blank">assessment tool</a> from Industry, Science and Economic Development Canada.

A Guide to Understanding Technology Readiness Levels

Technology Readiness Levels Explained

There’s nothing like synergy. And a new high-precision solution for location and heading – a collaboration between <a href="https://indrorobotics.ca" target="_blank" rel="noopener noreferrer">Indro Robotics</a> and <a href="https://www.tallysman.com" target="_blank" rel="noopener noreferrer">Tallysman Wireless</a> – is the result.As you likely know, while GPS is great – and good enough for us driving around using WAZE – its accuracy can leave something to be desired. Traditional Global Navigation Satellite System (GNSS) solutions generally are accurate to around 2.5 metres Circular Error Probability (CEP). That means the reported location has a 50 per cent chance of being within 2.5 metres of where it actually is. What’s more, that error rate exists under ideal conditions – with the detection system stationary and with an unobstructed view of satellites.That’s good enough for a car with a driver, or a cargo ship. But many robotic applications require far greater precision. If you’re running a remote inspection robot using GPS waypoints, 2.5 metres isn’t good enough.Think about it. In the example of a remote inspection robot, the device is generally operating at the location of a high-value asset. You might want a repeatable routine where the robot can get up close to look at gauges, valves, or anything else requiring inspection. You might want it to pass through a doorway, or get very close to – without touching – a highly energised electrical component. So accuracy matters, whether in this application or many other use-cases.Now, InDro and Tallysman Wireless, a Calian company, are pleased to announce a solution.Below: An InDro Sentinel inspection robot<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk5NDY1MzQ4MzgwLTIwMjIwNDI3XzE2NDU0NDAwOF9pT1MgY29weS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"><h3>Location, Location, Location</h3>That real estate phrase certainly applies when it comes to high-level robotics. But how do you get from a potential error of 2.5 metres down to, say, 2.5 centimetres – two orders of magnitude?In this case, the solution came by partnering with Tallysman Wireless.The company is known for its leading-edge GNSS and Iridium antennas. Tallysman also has an enviable reputation for customising those antennas for global clients seeking specific GNSS solutions. Ships, aircraft, trains and even drones carry out critical missions daily while relying on Tallysman solutions. InDro, meanwhile, is known for its R&amp;D work, robots and custom innovations in the aerial and ground robotics world. So the potential was there for collaboration.Because – and we know this well – it’s not just as simple as plugging antennas and receivers together and hoping for the best.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk5NDY1Njg1Njk5LVNjcmVlbnNob3QtMjAyMy0wOS0yNi1hdC0xLjUzLjE4LVBNLnBuZy53ZWJwIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib"><h3 id="isPasted">The Issue(s)</h3>Antennas – even really good ones – are finicky pieces of equipment. Depending on what you’re mounting them on, they can be subject to interference that diminishes their performance. The type of connector attaching them to a receiver, even the length of the cable used in that connection, can also detract from optimal signal acquisition and accuracy. That’s why you want someone like Tallysman Wireless on board.“We are experts in Global Navigation Satellite System (GNSS) solutions, specifically the antennae side of &nbsp;things,” explains Gord Echlin, Tallysman’s Director of Business Development.“A lot of people assume, unless you use the term GPS, that it’s a fairly easy thing to implement and get accurate results. Nothing could be farther from the truth.”As Tallysman Wireless explains in a brochure:“The problem is that keeping the antenna to system interface in the analog domain requires a lot of RF expertise to manage, expertise that is not widely available, and these issues amplify with long signal transmission over cables.&nbsp;Digital systems with built-in LTE communication links, common components of autonomous systems, are direct threats to the integrity of the GNSS signal.&nbsp;“Tallysman Wireless has solved this problem by integrating their sensitive, high performance, GNSS antennas in the same package as advanced GNSS receivers, in what is commonly called a ‘Smart’ Antenna. The GNSS processing solution is in compact, carefully engineered to mitigate the potential impairments between the antenna and receiver, and the Position/Navigation/Timing (PNT) information is now communicated to application system over in the digital domain, over a serial interface (UART, USB, CANbus, or Automotive Ethernet).”Or, as Echlin puts it: “We take the receiver, and we take the antenna and put it in the same package to mitigate the outside interference such that you get less than two centimetres of error. That, along with very precise heading information – with accuracy to 0.3 degree.” <h3>The InDro Connection</h3>With the Tallysman Wireless integrated solution, there was just one more piece of the puzzle remaining. How to integrate this into a robot? That’s where InDro’s engineering team came along.The package required a software interface where none had existed before. InDro engineers created a ROS2 coding solution and integrated the Tallysman smart antenna onto our InDro Backpack – which we use with Unitree quadrupeds.InDro Backpack is our system for remote teleoperations over 5G (and 4G), which also allows for rapid sensor integration and other customization using ROS1 and ROS2 software libraries (which, along with an EDGE computer and high-speed modem, are on-board).The complete solution – hardware and software – enable consistent, high-accuracy positioning. InDro Plans to offer this complete solution to clients in need of precision positioning.Below: The Tallysman Smart Antenna solution, integrated with new ROS2 coding into the InDro Backpack<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk5NDY1ODE5MjkyLTIwMjMwOTA2XzEzNDcxOS1qcGVnLndlYnAiLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib">InDro was pleased to partner with Tallysman Wireless on this integration project. With robots increasingly used for remote inspection of high-value assets, accurate positioning and heading data has become essential.“Being able to tap into the expertise of Tallysman Wireless – and combine their solution with software from InDro engineers – has resulted in a powerful solution,” says InDro Robotics CEO Philip Reece.“We’ve already begun integrating this onto our own robots, and look forward to offering this to clients in need of the most accurate and reliable positioning possible.”Looking for more information? <a href="https://indrorobotics.ca/connectwithanexpert/" target="_blank" rel="noopener noreferrer">Connect with us HERE</a>.&nbsp;

GNSS Solution for Ground Robots

InDro Robotics and Tallysman Partner on a New Precision GNSS Solution for Ground Robots

The Robot Operating System (ROS) has been a game-changer in the world of robotics, enabling developers to create and manage complex robot systems with ease. However, as technology evolves, so does the need for more advanced tools and capabilities. This has led to the development of ROS 2, the successor to ROS 1, and it's gaining traction rapidly among developers. Let's explore the key differences between ROS 1 and ROS 2 and why many developers are making the switch:<h3>ROS 1: The Foundation</h3>ROS 1, often referred to simply as ROS, was introduced in 2007 and quickly became the go-to framework for robot development. It provided a flexible and modular architecture for building robotic applications, and its extensive library of packages and tools made it an attractive choice for developers. ROS 1 was built on a message-passing middleware called ROS Master, which uses the DDS (Data Distribution Service) communication standard for inter-process communication.<h3>ROS 2: The Evolution</h3>ROS 2 was introduced as the successor to ROS 1 and addresses several limitations that developers encountered with the original system. Some of the key differences and improvements <a href="http://design.ros2.org/articles/changes.html" target="_blank" rel="noopener noreferrer">outlined by the ROS organization</a> in ROS 2 include:<ol><li>Real-time Support: ROS 2 offers enhanced real-time capabilities, making it suitable for safety-critical and time-sensitive applications, which were challenging to achieve with ROS 1.</li><li>Middleware Agnostic: Unlike ROS 1, which relies on the ROS Master, ROS 2 is middleware agnostic. This means it can support multiple middleware systems, such as DDS, Fast RTPS, and more. This flexibility allows for better compatibility with various hardware and network configurations.</li><li>Improved Security: ROS 2 incorporates improved security features, ensuring that robotic systems can operate securely in environments where data integrity and confidentiality are paramount.</li><li>Enhanced Cross-Platform Compatibility: ROS 2 has been designed with cross-platform compatibility in mind, making it easier to run on various operating systems, including Windows.</li><li>Simplified Dependency Management: ROS 2 streamlines package management, reducing dependencies and making it easier to manage the libraries and modules required for a project.</li></ol>Here's a chart outlining some of the key technical differences between ROS and ROS2:<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk4OTQ1ODI4MDE5LVJPUyB2cyBST1MyIENoYXJ0LnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 766px;" class="fr-fic fr-dib"><h3>Why More Developers Are Making the Switch:</h3>Improved Real-time Capabilities: ROS 2's support for real-time systems is a significant attraction for developers working on applications where timing and synchronization are crucial, such as autonomous vehicles and industrial automation.Middleware Flexibility:&nbsp;ROS 2's middleware agnosticism allows developers to choose the best communication middleware for their specific use case, ensuring better integration with different hardware and network configurations.Enhanced Security: With an increased focus on security, ROS 2 provides a safer platform for robots operating in critical environments, including healthcare, defense, and manufacturing.Cross-Platform Compatibility: ROS 2's compatibility with various operating systems, including Windows, opens up new possibilities for developers and allows for a broader range of deployment options.Long-Term Support: ROS 2 is well-supported and continually updated, providing developers with a future-proof platform for their projects. ROS 1 is no longer under active development, which means that it may not receive the same level of support and enhancements.Overall, ROS 2 is designed to address some of the limitations and challenges of ROS 1, such as scalability, performance, security, and cross-platform compatibility. While both ROS 1 and ROS 2 have the same core functionality, many differences exist between the two, and the decision to use one over the other depends on specific use cases and requirements. To see a more in-depth analysis please <a href="https://roboticsbackend.com/ros1-vs-ros2-practical-overview/" target="_blank" rel="noopener noreferrer">visit “roboticsbackend”</a>.Here's what InDro's Engineering Manager, Arron Griffith, had to say: "Now is the time to make the switch to ROS 2 (or start using it). ROS 2 has matured over the 5 years to equal or better than ROS 1 after its first 5 years. Plus ROS 1 will be going <a href="http://wiki.ros.org/Distributions" target="_blank" rel="noopener noreferrer">End of Life over the next 2 years</a>, so there is no point in starting with ROS 1 anymore, the future is ROS 2".As the robotics industry continues to advance, ROS 2 is expected to play a pivotal role in shaping the future of robot development. If you're a developer, it's time to consider making the switch to ROS 2 to harness its full potential. &nbsp;Questions about ROS or ROS2? &nbsp;<a href="https://indrorobotics.ca/connectwithanexpert/" target="_blank" rel="noopener noreferrer">Connect with an expert HERE.</a>

Why Developers Are Making the Switch

ROS 1 vs. ROS 2

Building robots is hard.Even if you start with a manufactured platform for locomotion, the work ahead can be challenging and time-consuming. How many sensors will require a power supply and data routing? What EDGE processing is needed? How will a remote operator interface with the machine? What coding will allow everything to work in unison and ensure the best data and performance possible? How will data be transmitted or stored?That’s the hard stuff, which inevitably requires a fair bit of time and effort. &nbsp;Typical robotics development is lengthy, it can take anywhere from 3 to 12 months from idea to a functional robot rolling around. The more complex the robotic solution, the more time it takes. <a href="https://indrorobotics.ca/commander/" target="_blank" rel="noopener noreferrer">InDro Commander</a> can reduce this to 1 to 3 months, depending on the complexity, plus InDro Robotics provides clients with software tutorials and one-on-one support.It’s that hurdle – one faced by pretty much everyone in the robotics R&amp;D world – that led to the creation of InDro Commander.<h2><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk1Njc2MjEwMDg5LUNvbW1hbmRlciBvbiBCdW5rZXIuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib"></h2><h2>What Does InDro Commander Do?</h2><a href="https://indrorobotics.ca/commander/" target="_blank" rel="noopener noreferrer">InDro Commander</a> is a platform-agnostic module that can bolt on to pretty much any means of locomotion. In the photo above, it’s the box mounted on top of the AgileX bunker (just above the InDro logo). &nbsp;Commander is a single box with critical software and hardware designed to simplify payload integration and enable turn-key tele-operations. &nbsp;Whether you’re adding LiDAR, thermal sensors, RTK, Pan-Tilt-Zoom cameras – or pretty much any other kind of sensor – Commander takes the pain out of integration. &nbsp;With its modular system, reduced development time, software tutorials and one-on-one support, clients can get their robot solution to market faster and with less hassle.The module offers multiple USB inputs for sensors, allowing developers to decide on a mounting location and then simply plug them in. A powerful Jetson EDGE computer handles onboard compute functions. The complete Robot Operating System software libraries (ROS1 and ROS2) are bundled in, allowing developers to quickly access the code needed for various sensors and functions.“Our engineering team came up with the concept of the InDro Commander after integrating and customizing our own robots,” says Philip Reece, CEO of InDro Robotics. “We realized there were hurdles common to all of them – so we designed and produced a solution. Commander vastly simplifies turning a platform into a fully functioning robot.”Account Executive, Luke Corbeth takes it further:“The Commander serves as a “brain-box” for any UGV,” he says. “It safely houses the compute, connectivity, cameras, sensors and other hardware in an IP54 enclosure.”It also comes in several options, depending on the client’s requirements.“There are three ‘standard versions’ which are bundles to either be Compute Ready, Teleoperations Ready or Autonomy Ready,” adds Corbeth.“I’ve realized over time that the value of Commander is our ability to customize it to include, or more importantly, not include specific components depending on the needs of the project and what the client already has available. In reality, most Commanders I sell include some, but not usually all, of what’s in the Commander Navigate. We’re also able to customize to specific needs or payloads.”Below: Commander comes in multiple configurations<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk1Njc0MDk1MDA2LVNjcmVlbnNob3QtMjAyMy0wOS0xOS1hdC0yLjMzLjQ2LVBNLnBuZy53ZWJwIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib"><h2>Commander Does The Work</h2>With InDro Commander, developers can spend more time on their actual project or research – and far less time on the build.“For end-users wanting a fully customized robot, Commander saves a huge amount of time and hassle,” says InDro Engineering Lead, Arron Griffiths. “Customers using this module see immediate benefits for sensor integration, and the web-based console for remote operations provides streaming, real-time data. Commander also supports wireless charging, which is a huge bonus for remote operations.”Commander serves as the brains for several InDro ground robots, including Sentinel. This machine was recently put through its paces over 5G in a <a href="https://indrorobotics.ca/putting-sentinel-through-its-paces-at-epri/" target="_blank">test for EPRI</a>, the Electric Power Research Institute.Below: Commander is mounted on Sentinel, a site based autonomous robot, capable of complete continuous monitoring of infrastructure, with the ability to carry out autonomous inspection rounds with thermal, RGB and zoom sensors, for recognition of change detection.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk1NjczODM5MTAwLVNlbnRpbmVsIEVQUkkuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" class="fr-fic fr-dii"><h2>5G Operations</h2>Depending on the model, Commander can also serve as a plug &amp; play device for operations over 4G or 5G networks. In fact, InDro was invited by US carrier T-Mobile to a 2022 event in Washington State to demonstrate&nbsp;the live, remote tele-operation of a Sentinel inspection robot.Using a simple Xbox controller plugged into a laptop at T-Mobile HQ in Bellevue WA, we operated a Sentinel in Ottawa – more than 4,000 kilometres away. There was no perceptible lag, and even untrained operators were able to easily control remote operations and cycle between the Pan Tilt Zoom camera, a thermal sensor, and a wide-angle camera used for situational awareness by the operator. Data from all sensors was displayed on the dashboard, with the ability for the operator to easily cycle between them.Below: T-Mobile’s John Saw, Executive Vice President, Advanced &amp; Emerging Technologies, talks about InDro Commander-enabled robots teleoperating over 5G networks&nbsp;<iframe width="640" height="360" src="https://www.youtube.com/embed/-C88iEnsh7E?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&amp;lt;span class="fr-mk" style="display: none;"&amp;gt;&amp;amp;nbsp;&amp;lt;/span&amp;gt;&amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;gt;&amp;amp;amp;nbsp;&amp;amp;lt;/span&amp;amp;gt;&amp;amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;amp;gt;&amp;amp;amp;amp;nbsp;&amp;amp;amp;lt;/span&amp;amp;amp;gt;&amp;amp;amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;nbsp;&amp;amp;amp;amp;lt;/span&amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;nbsp;&amp;amp;amp;amp;amp;lt;/span&amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;nbsp;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/span&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;nbsp;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/span&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;nbsp;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/span&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;nbsp;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/span&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;nbsp;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/span&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;nbsp;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/span&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;nbsp;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/span&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;nbsp;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/span&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;</iframe><h2 id="isPasted">Future-Proof</h2>Platforms change. Needs evolve. New sensors hit the market.With Commander on board, developers don’t need to start from scratch. The modular design enables end-users to seamlessly upgrade platforms down the road by simply unbolting Commander and affixing it to the new set of wheels (or treads).Any sensor, including LiDAR, can be quickly integrated with InDro Commander.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk1Njc1MDA4NjA2LVNjcmVlbnNob3QgMjAyMy0wOS0yNSBhdCA0LjQ5LjIzIFBNLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 766px;" class="fr-fic fr-dib"> You likely know the saying: “Necessity is the mother of invention.”InDro developed this product because we could see its utility – both for our own R&amp;D, and for clients. We’ve put Commander to use on multiple custom InDro robots, with many more to come. (We have even created a version of this for Enterprise drones.)“On the commercial side, our clients have really benefited from the inherent modularity that the Commander provides,” says Luke Corbeth.“Since the ‘brains’ are separate from the ‘body,’ this simplifies their ability to make the inevitable repairs or upgrades they’ll require. These clients generally care about having a high functioning robot reliably completing a repetitive task, and Commander allows us to operate and program our robots to do this.”It can also save developers money.“On the R&amp;D side, the customizable nature of the Commander means they only purchase what they don’t already have,” adds Corbeth.“For instance, many clients are fortunate enough to have some hardware already available to them whether it’s a special camera, LiDAR or a Jetson so we can support the integration of their existing systems remotely or they can send this hardware directly to us. This cuts down lead times and helps us work within our clients’ budgets as we build towards the dream robot for their project.”Still have questions or want to learn more about Commander?&nbsp;<a href="https://indrorobotics.ca/commander/" target="_blank" rel="noopener noreferrer">Click HERE.</a>

A platform-agnostic module that can bolt on to pretty much any means of locomotion

InDro Commander Streamlines Robotics Research and Development

Delineating cobots and industrial robots, this analysis drives deep into their respective roles and divergent characteristics that make each a vital cog in the machinery of Industry 4.0.

Cobots vs Robots: Understanding the Key Differences and Applications

Universal Robots-Fri Jun 30 2023 23:59:59 GMT+0200 (Central European Summer Time)

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University of Michigan-Fri Jun 30 2023 23:59:59 GMT+0200 (Central European Summer Time)

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University of Michigan-Sat Sep 30 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

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Inovo Robotics Ltd-Fri Jun 30 2023 23:59:59 GMT+0200 (Central European Summer Time)

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READY Robotics-Fri Jun 30 2023 23:59:59 GMT+0200 (Central European Summer Time)

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Rozum Robotics-Fri Jun 30 2023 23:59:59 GMT+0200 (Central European Summer Time)

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Unitree Robotics-Thu Aug 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

Unitree Robotics-Thu Aug 31 2023 23:59:59 GMT+0200 (Central European Summer Time)

Unitree Robotics-Sat Sep 30 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

collaborative robots (cobots)

Robotic joints, which are sometimes known as axes, are the moveable parts of a robot that cause relative motion between adjacent links. These links refer to the rigid components that connect the joints to ensure their proper and straightforward operation. 

Robot Joints: An In-Depth Guide to Anatomy, Physics and Challenges in Design

collaborative robots (cobots)

Profiles