Content Creation

Robotics Software

Radar finds objects by sending out radio waves and timing the echo that comes back. This guide explains how radar works from first principles, runs the radar equation with real numbers, and compares the frequency bands, radar types and applications you will meet in practice.

What Is Radar? How It Works, Frequency Bands, Types, and Uses

Network topology is the shape of a network: which nodes connect to which, and which paths data can take between them. This guide covers the seven types, how they compare, why the physical and logical views differ, and how to choose one for IoT and industrial systems.

Network Topology: 7 Types and How to Choose One

PLC and SCADA, HMI Equipment for Industry 4.0

In this article, we'll see how PLC and SCADA systems differ, how they work together, and which protocols, scan times and security controls matter in industrial automation.

PLC and SCADA: Key Differences and How They Work Together

Programmable Logic Controller (PLC) vs Distributed Control System (DCS)

This guide compares PLC vs DCS architecture, scan times, redundancy, scalability, programming, costs, and applications to help you choose the right industrial control system.

PLC vs DCS: Key Differences and How to Choose

This article is a practical PLC programming guide covering the five IEC 61131-3 languages, the scan cycle that governs how your code actually runs, ladder and structured text examples, and the mistakes that cost engineers the most commissioning time.

PLC Programming: Languages, Scan Cycles, and Working Ladder Logic

An STM32 development board (left) alongside an ESP32 development board (right)

STM32 vs ESP32 compared on architecture, Wi-Fi, power, peripherals, toolchains, safety, and longevity. Current 2026 specs from vendor datasheets, plus a decision table.

STM32 vs ESP32: Choosing the Right Microcontroller for Your Design

This article explains what is a PLC, what's inside it, how the scan cycle works, which programming languages the current IEC standard defines, and how to choose the right controller for your application.

What Is a PLC? A Complete Guide to Programmable Logic Controllers

The electric wire behind the control board.

What a programmable automation controller is, how PACs are built and programmed, and how to choose between a PLC and a PAC.

What Is a Programmable Automation Controller (PAC)?

Clustering - A Common Unsupervised Learning Technique

Unsupervised learning is a machine learning approach that finds patterns, structure, or useful representations in unlabeled data. It groups similar observations, compresses high-dimensional features, detects outliers, discovers item relationships, and supports modern representation learning.

What Is Unsupervised Learning? Types, Algorithms, and Examples (2026)

GPUs excel in parallel processing for graphics and AI training with scalability, while NPUs focus on low-latency AI inference on edge devices, enhancing privacy by processing data locally. Together, they complement each other in addressing different stages of AI workloads efficiently.

NPU vs GPU: Understanding the Key Differences and Use Cases

This article explores TPU vs GPU differences in architecture, performance, energy efficiency, cost, and practical implementation, helping engineers and designers choose the right accelerator for AI workloads today!

TPU vs GPU: Comprehensive Technical Comparison

Understanding industrial vision systems by examining their components, imaging fundamentals, AI integration since 2020, and how to choose the right solution for every application.

Machine Vision Systems: Components, AI Integration & Selection Guide

A detailed engineering reference on performance, signaling, and power differences in current graphics memory standards.

GDDR6 vs GDDR7: A Technical Comparison of Graphics Memory

We got to play around with the F1 Starter Kit, built around the F1 Smart Module from SG Wireless, a module which encompasses 4 different radio technologies: cellular, LoRa, Bluetooth Low Energy and WiFi.We were given the challenge to build something, while using multiple technologies from within the module. The idea was quickly set: build a tracking application that combines Wi-Fi and cellular, with the option to add BLE beacons at a later stage.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzkyNDAxOTY0NTE5LUlNR18zMTQ5IC0gMjIwMHB4LmpwZWciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:812px" class="fr-fic fr-dib" />Unboxing the F1 Starter KitHere's what we built, and what we experienced along the way.<iframe width="640" height="360" src="https://www.youtube.com/embed/H-LdJrk9eZ4?&amp;wmode=opaque&amp;rel=0" frameborder="0" class="fr-draggable"></iframe> <h3>Pycom’s heritage</h3>Remember the days of Pycom? Famous for its <a href="https://www.kickstarter.com/projects/pycom/lopy-the-lora-wifi-and-bluetooth-iot-development-p" target="_blank">Kickstarter campaigns</a> and its huge community of MicroPython developers. The successful Kickstarter led to the LoPy, the LoRa, Wi-Fi and Bluetooth IoT board.Fast-forward 6 years. By then, their latest module was developed comprising LoRa, Sigfox, WiFi, BLE and Cellular (LTE-M and NB-IoT), but reality kicked-in. <a href="https://www.seasongroup.com/?utm_source=blog1&amp;utm_medium=referral&amp;utm_campaign=stars2610" target="_blank">Season Group</a>, a Hong Kong-based electronics design and manufacturing company, came to the rescue and acquired Pycom's technology and its engineering team in late 2022. The assets and expertise were brought into Season Group’s IoT-focused subsidiary: <a href="https://sgwireless.com/?utm_source=blog1&amp;utm_medium=referral&amp;utm_campaign=stars2610" target="_blank">SG Wireless</a>. The module Pycom was working on at the time, based on Espressif’s ESP32-S3, eventually became the F1 Smart Module.Today, SG Wireless combines Pycom’s developer-focused heritage with Season Group’s expertise in hardware and firmware development, certification and manufacturing, helping companies take IoT products from initial prototype to production. <h3>The F1 Smart Module and the F1 Starter Kit</h3>F1 Smart Module supports four connectivity options: WiFi, Bluetooth, LoRa and Cellular (LTE-M and NB-IoT). The module is pre-certified, programmable in MicroPython, native C and soon with the Arduino IDE. It's built for rapid prototyping and easy scaling. The flagship module comes with all four radios, but there are also<a href="https://sgwireless.com/product/f1-c-cellular-module/?utm_source=blog1&amp;utm_medium=referral&amp;utm_campaign=stars2610" target="_blank"> a cellular</a>, BLE and WiFi version and<a href="https://sgwireless.com/product/f1-l-lora-module/?utm_source=blog1&amp;utm_medium=referral&amp;utm_campaign=stars2610" target="_blank"> a LoRa</a>, BLE and WiFi one.The F1 module is built around Espressif's ESP32-S3: a dual-core 32-bit Xtensa LX7 running at up to 240 MHz, with 384 KB ROM and 512 KB SRAM on chip, plus 8 MB PSRAM and 16 MB flash on the module. In deep sleep the MCU draws around 10 µA.Learn more at <a href="https://sgwireless.com/the-f1-smart-module/?utm_source=blog1&amp;utm_medium=referral&amp;utm_campaign=stars2610" target="_blank">SG Wireless’ website</a>.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzkyNDAyMTYwMDE3LUYxIFNtYXJ0IE1vZHVsZS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" />The F1 Smart ModuleThe F1 Starter Kit is the Arduino-compatible devkit, with the F1 Smart Module on board. It comes with access to Ctrl, SG Wireless' device management platform for firmware updates as well as data storage and visualization.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzkyNDAyMTg0MzM5LUlNR18zMzQyIC0gMTYwMHcuanBlZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" class="fr-fic fr-dib" />The F1 Starter Kit<h3>Wi-Fi tracking, but serverless</h3>The goal: track the location of the device without adding any extra sensors or peripherals. So-called WiFi sniffing is a great way of localizing assets as it – other than GPS – works indoors, and doesn’t require long and time-consuming satellite scans. To do so, the device scans for Wi-Fi access points and sends their unique addresses (BSSIDs), together with their signal strengths (Received Signal Strength Indicators or RSSIs) to a cloud platform. From there, an API call is made to a database that returns the location, including an accuracy radius.This had to work seamlessly, without me having to run a server myself. <h3>Architecture</h3><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzkyNDAyMjc1NjIyLURpYWdyYW0tMi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" /><ol><li>Scan. The F1 wakes up at preconfigurable time intervals, currently set to 15 minutes, and scans for nearby Wi-Fi access points. It keeps up to 6 strongest BSSIDs and drops randomized addresses (such as phone hotspots).  To avoid sending data while the device hasn't moved, it compares the BSSIDs with those of the last message sent. A new message is sent only when at least 50% of them have changed. If nothing changes, the device still sends an uplink at least once every 6 hours, so we can tell a stationary device from a dead one.  </li><li>Send. It connects over cellular using the <a href="https://soracom.io/" target="_blank">Soracom</a> SIM and sends the list of access point addresses and signal strengths to <a href="https://tago.io/" target="_blank">TagoIO</a> in a UDP message. Once an ACK is received, the module goes to sleep. </li><li>Resolve. In TagoIO, each received data message triggers a script, which sends the addresses and signal strengths to the <a href="https://developers.google.com/maps/documentation/geolocation/overview" target="_blank">Google Geolocation API</a>. </li><li>Store. Google returns the latitude, longitude and accuracy radius. The script writes these back to TagoIO, where they are stored. </li><li>Show. A TagoIO dashboard shows the device’s location on a map, including its accuracy.</li></ol><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzkyNDAyMzIzMjUxLVNvcmFjb20ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width:100%px" class="fr-fic fr-dib" />Soracom ConsoleSoracom is used as the connectivity provider, enabling the F1 Smart Module to connect over LTE-M. The Soracom SIM comes with access to a centralised management platform and a console the monitor the connection. The same platform is designed to support connected devices as they move from prototyping into larger-scale deployments.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzkyNDAyMjg4NTMzLVRhZ29JTy5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" />TagoIO dasbhoardTagoIO is a full-stack IoT cloud platform that supports light-weight, low-bandwidth data transmissions to build end-to-end applications. Apart from data storage and visualization, it comes with the ability to run scripts to automate data processing.  <h3>Conclusion</h3>It works, better than I expected.  The existing tools let the device run by itself, without me having to run any servers with impressive accuracy. With just 3 access points, we already get a location within 20 to 50m. More access points bring that down to less than 20m. Impressive!The messages are small. Each access point adds about 22 bytes, so a full message is roughly 150 to 250 bytes, depending on how many access points we include. With some minor adaptations, the payload can be further reduced to make it work with LoRaWAN, which allows up to 222 bytes per message.Only send when it moves. Currently, the application scans at a configurable time interval. When the device is stationary for a while, it keeps scanning for WiFi networks and comparing them to the previous set, which drains the battery. A better way is to add an accelerometer that triggers the Wi-Fi scan. As long as no movement is detected, the device stays in deep sleep, consuming as little energy as possible.The other radios allow for further application development. We only used Wi-Fi and cellular, the same module could:<ul><li>Use Bluetooth beacons for more precise location, indoors</li><li>Use LoRaWAN as a fallback when there is no cellular coverage (or vice versa)</li><li>Fall back to the cell tower location when there aren't enough Wi-Fi networks around, using a database like <a href="https://www.opencellid.org/" target="_blank">OpenCelliD</a></li></ul><hr />The F1 Starter Kit is a great platform for prototyping, giving plenty of directions to pursue due to the multi-radio setup. The kit is widely available and the documentation gets you started quickly. The Pycom community has been dormant for the past few years, but with the F1 Starter Kit, along with SG Wireless’ efforts to activate <a href="https://forum.sgwireless.com/?utm_source=blog1&amp;utm_medium=referral&amp;utm_campaign=stars2610" target="_blank">the forum</a>, we may see part of the community revive.The bigger promise of the F1 Starter Kit is a seamless transition from prototyping to production where the same module can be used in both stages. SG Wireless’ expertise in IoT, combined with Season Group’s capabilities in hardware development, certification and manufacturing, should lead to a seamless process to get ideas to market. But, let’s dig into the process to optimize the WiFi-based tracker and get it ready for production. A new article is coming up that describes the next steps of moving a prototype to production. Stay tuned! Laurens Slats

The F1 Starter Kit, built around the F1 Smart Module which supports cellular, LoRa, BLE and WiFi

Tracking without GPS: hands-on with the F1 Starter Kit

Embedded AI moved from concept to working prototype at the first-ever embedded world North America Hackathon, where student teams were challenged to turn real-world problems into functional Edge AI solutions.The competition brought together 52 students across 12 teams, who had two days to develop solutions across four challenges: industrial automation, sensing, sustainability and robotics. Each team worked with an Arduino UNO Q, while the hackathon was powered by Arduino, Qualcomm, Quilter and ASUS.[1]<h2>Two Days of Learning, Building and Problem-Solving</h2>Rather than simply proposing concepts, participants had to develop working solutions within a tightly constrained timeframe. Teams had to learn unfamiliar tools, build hardware and software systems, train or integrate AI models, troubleshoot problems and demonstrate their results.That intensity was reflected in comments from Giovanni Salinas, Technical Advisor and Judge from Supplyframe, who said the challenges required students to learn new skills before they could even begin developing their solutions. He praised their drive, positive energy and problem-solving approach throughout the two-day event.Ben Jordan, Judge from Quilter, highlighted another dimension of the competition. He noted that although the solutions were creative and technology-driven, the results showed the continuing importance of human judgement in deciding what should be built and how technology should address practical problems.<h1><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1791997928144-1791997928143.jpeg" width="602" height="401" class="fr-fic fr-dii" />Team 10. Source: Supplyframe.</h1><h2>Sustainability: ZOT ROT or NOT</h2>Team 10, ZOT ROT or NOT, won the Sustainability Challenge with a camera-powered conveyor designed to distinguish recyclable material, organic waste, and trash. [1]The team's Hackaday.io project describes a system in which a camera captures objects moving along a conveyor while the Arduino UNO Q processes the images locally. The system classifies items into the three categories and communicates the result to an OLED display, LEDs, and the sorting mechanism. [2]The team also documented its Edge Impulse object-detection model. Its initial unoptimised model achieved 89.6% validation accuracy, with an estimated on-device latency of 5 milliseconds. [3]<a href="https://hackaday.io/project/206767-zot-rot-or-not" target="_blank" rel="noopener noreferrer">Explore ZOT ROT or NOT on Hackaday.io</a>The project demonstrated a practical application of local computer vision: instead of relying on remote processing, the classification pipeline operates directly on the embedded system.<h1><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1791997928150-1791997928150.jpeg" width="602" height="401" class="fr-fic fr-dii" />Team 5. Source: Supplyframe.</h1><h2>Robotics: EmbedX</h2>Team 5, EmbedX, won the Robotics Challenge with an on-device AI system for robotic motion and navigation.[1]The project combined machine vision with robotic control, demonstrating how an embedded platform can connect perception with physical movement. The challenge highlighted an important characteristic of Edge AI: intelligence becomes more valuable when it can respond directly to information collected from the surrounding environment.Rather than treating AI as a separate software function, the project connected sensing, computation, and physical action into a single system.<a href="https://hackaday.io/project/206753-team-embedx-robotics" target="_blank" rel="noopener noreferrer">Explore the EmbedX Robotics project on Hackaday.io</a><h1><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1791997928156-1791997928156.jpeg" width="602" height="401" class="fr-fic fr-dii" />Team 3. Source: Supplyframe.</h1><h2>Industrial Automation: CRATE</h2>Team 3's CRATE — Chironomy Recognition As A Touchless Experience won the Industrial Automation Challenge.[1]The project uses an Arduino UNO Q, a camera, and a gesture-recognition model to identify hand gestures and translate them into commands. The team's Hackaday documentation states that it used a YOLOv5-based classifier and a dataset containing more than 450 images captured during the hackathon. [4]The final system reportedly achieved an average inference time of about 175 milliseconds and a 0.94 mAP@50 score, while displaying recognised gestures through the Arduino's LED matrix. [5]CRATE demonstrated how computer vision can be used to create alternative human-machine interfaces, particularly in environments where touch-based interaction may not always be convenient.<a href="https://hackaday.io/project/206741-crate-ewna-hackathon-team-3" target="_blank" rel="noopener noreferrer">Explore CRATE on Hackaday.io</a><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1791997928162-1791997928162.jpeg" width="602" height="401" class="fr-fic fr-dii" />Team 8. Source: Supplyframe.<h2>Sensing: Zotduino</h2>Team 8, Zotduino, won the Sensing Challenge with a sound-event classification system designed around the needs of an elderly person living alone. [1]The concept focused on recognising potentially important household sounds, including events such as falls, broken glass and fire alarms, while maintaining privacy. According to the team's project documentation, sound classification was performed locally on the Arduino UNO Q, meaning audio did not need to be sent away from the device for processing. [6]The team explained that its design deliberately avoided cameras and cloud-based audio processing. A local classifier analyses the sound, while the system can provide a visual indication through an LED matrix.[6]This approach demonstrated one of the practical benefits of Edge AI: intelligent sensing can happen close to the data source while reducing the need to transmit sensitive information elsewhere.<a href="https://hackaday.io/project/206738-ewna-hackathon-team-8" target="_blank" rel="noopener noreferrer">Explore Zotduino on Hackaday.io</a><h2>Making Embedded AI More Accessible</h2>Across the four winning projects, a common theme emerged: AI was not treated as an isolated software capability. Each team connected machine learning with physical hardware, sensors and real-world actions.Rajan Mistry, a judge from Quilter, described the experience as an opportunity for students to learn new technologies, overcome engineering challenges and turn ideas into reality. He also pointed to platforms such as the Arduino UNO Q as making innovation more accessible.The projects reflected that accessibility in different ways. ZOT ROT or NOT applied computer vision to waste sorting; CRATE used vision for touchless interaction; Zotduino explored privacy-conscious sound classification; and EmbedX connected AI with robotics.<h1><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1791997928168-1791997928168.jpeg" width="602" height="401" class="fr-fic fr-dii" />2026 embedded world North America Hackathon. Source: Supplyframe.</h1><h2>From Ideas to Working Systems</h2>The 2026 hackathon demonstrated the growing practicality of Edge AI in embedded development. Students took a problem, selected appropriate sensing and computing tools, integrated AI, and built a working prototype in just two days. [1]Perhaps the most important takeaway was not any single project, but the workflow shared by all four teams: understand a practical problem, experiment with available technology, iterate quickly, and connect intelligence to the physical world.As embedded AI continues to develop, that ability to move quickly from an idea to a functioning system could become increasingly important. The hackathon clearly demonstrated that the next generation of embedded engineers is already experimenting with that approach.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1791997928173-1791997928173.jpeg" width="602" height="401" class="fr-fic fr-dii" />2026 embedded world North America Hackathon Sponsors. Source: Supplyframe.<hr /><h3>References</h3><ol><li>Hackaday. Post-event announcement for the first-ever embedded world North America Hackathon. LinkedIn, 2026.<a href="https://www.linkedin.com/company/hackaday/"> https://www.linkedin.com/company/hackaday/</a></li><li>Hackaday.io, “ZOT ROT or NOT.” Project description and system overview for the camera-powered waste-sorting conveyor using Arduino UNO Q. https://hackaday.io/project/206767-zot-rot-or-not </li><li>Hackaday.io, “Object Detection Model ZOT ROT or NOT.” Model accuracy, dataset and on-device latency details. https://hackaday.io/project/206767-zot-rot-or-not/log/251074-object-detection-model </li><li>Hackaday.io, “CRATE: EWNA Hackathon Team 3 — Files.” Dataset and YOLOv5 model information. https://hackaday.io/project/206741-crate-ewna-hackathon-team-3</li><li>Hackaday.io, “CRATE: EWNA Hackathon Team 3  Final log.” Inference performance, model score and system details. https://hackaday.io/project/206741-crate-ewna-hackathon-team-3/log/251101-final-log</li><li>Hackaday.io, “EWNA Hackathon Team 8 — How we came up with the idea.” Privacy-focused sound classification concept and local processing approach. https://hackaday.io/project/206738-ewna-hackathon-team-8/log/251059-how-we-came-up-with-the-idea</li></ol>

Highlights from the 2026 embedded world North America Hackathon

Edge AI in Action

NEXT HORIZONS, a new cross-industry event from Vector, brings together software architects, engineers, technical decision-makers and business leaders for two days of discussion around the challenges shaping software-defined systems. <a href="https://nexthorizons.com/?etcc_cmp=nxh26&amp;etcc_med=paid&amp;et_cmp_seg4=newsletter&amp;et_cmp_seg5=eventpromo&amp;etcc_par=wevolver" target="_blank" rel="noopener noreferrer"></a><a href="https://nexthorizons.com/?etcc_cmp=nxh26&amp;etcc_med=paid&amp;et_cmp_seg4=newsletter&amp;et_cmp_seg5=eventpromo&amp;etcc_par=wevolver" target="_blank" rel="noopener noreferrer" class="button-main-action">Register now for this free event.</a><h2>Decide What You Can Build Next: 4 Tech Tracks</h2>Each track starts from a question practitioners are already grappling with, and the sessions are built to answer it.<h3>Control Software-Defined Systems at Scale</h3>Day 1, November 17How can you maintain control when systems, supply chains, and organizations become more complex?Complexity compounds across architecture, supplier relationships, and org charts at the same time, and control has to be designed in at each of those levels. This track covers architecture decisions, lifecycle governance, organizational transformation, and operating models.<h3>Make AI Deployable and Trustworthy</h3>Day 1, November 17How can you make AI deployable while ensuring accountability, traceability, and regulatory oversight?A model that performs well in a lab still has to meet technical, regulatory, and safety requirements before it reaches a product. This track works through AI governance, regulatory compliance, safety engineering, and the workflows that make AI evidence traceable.<h3>Accelerate Engineering Workflows Without Compromise</h3>Day 2, November 18How can you streamline end-to-end engineering workflows without compromising safety, security, quality, or compliance?The challenge is not simply to move faster, but to make speed sustainable across the development lifecycle. This track focuses on DevOps, verification and validation, virtualization, and engineering automation that shorten cycles while keeping the safeguards intact.<h3>Scale Platforms Beyond Projects</h3>Day 2, November 18How do you build scalable, safe, and secure software platforms while keeping control over architecture, partners, and lifecycle ownership?A platform outlives the project that created it, so ownership questions arrive sooner than most roadmaps assume. This track addresses platform architecture, legacy modernization, partnerships, and lifecycle ownership.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzkxODc3NTA5NDIwLVZlY3Rvci1UZWFzZXJiaWxkXzEyMDB4OTAwcHguanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width:100%px" class="fr-fic fr-dib" /><h2>Four Formats, One Personal Agenda</h2>You choose which sessions to attend, and the formats reward different kinds of attention:<ul><li>Keynotes set the strategic frame, with perspectives from AI, futurology, OEM leadership, and industrial pioneers.</li><li>Presentations deliver proven knowledge, use cases, and success stories from people who have shipped.</li><li>Deep Dives put small groups on concrete challenges, working hands-on with tools alongside peers who share their technical reality.</li><li>Networking &amp; Exhibition spans 800 sqm, with open areas for conversations that go beyond the usual industry routine.</li></ul>Three stages and four deep-dive rooms host sessions run in English or German. AI-based interpretation makes every session available in English.<h2>Who's Speaking</h2>The lineup keeps growing. Confirmed voices include:<ul><li>Maik Manthey, CTO, Jungheinrich</li><li>Steffen Tacke, Director R&amp;D, Mercedes-Benz</li><li>Prof. Eric Sax, Head of ITIV, Karlsruhe Institute of Technology</li><li>Dr. Hiie-Mai Unger, CEO, DB Systemtechnik</li><li>Dr. Ravi Subramanian, Chief Product Management Officer, Synopsys</li><li>John Wall, President, QNX</li></ul>Speakers from Neura Robotics, Intellias, Harmann, T-Systems, Bertrandt and many other leading companies round out the program.<a href="https://nexthorizons.com/speakers?etcc_cmp=nxh26&amp;etcc_med=paid&amp;et_cmp_seg4=newsletter&amp;et_cmp_seg5=eventpromo&amp;etcc_par=wevolver">See all speakers here.</a><h2>Who Should Attend</h2>Strategic keynotes and curated exchange formats serve C-level leaders and technical decision-makers. Deep dives and exhibits are built for software architects, engineers, and developers. You can attend one day or both, so the track that matches your priorities sets your schedule.<h2>Plan Your Visit</h2>When: November 17–18, 2026 Where: Vector Headquarters, Holderäckerstraße 36, 70499 Stuttgart Cost: Free. Registration is personalized, so colleagues register individually. Getting There: Free parking with charging stations on site, Weilimdorf S-Bahn station five minutes away on foot, Stuttgart Airport about 30 minutes by road.<h4><a href="https://nexthorizons.com/?etcc_cmp=nxh26&amp;etcc_med=paid&amp;et_cmp_seg4=newsletter&amp;et_cmp_seg5=eventpromo&amp;etcc_par=wevolver" class="button-main-action">Register for NEXT HORIZONS 2026.</a></h4>

November 17–18, 2026 - Vector Headquarters, Stuttgart

NEXT HORIZONS 2026 - Move Faster. Stay in Control.

Most engineering organizations ship something they would rather a competitor never saw. In cloud software, that code stays on a server the company controls. In embedded products, however, it does not, because the software that defines the product gets compiled, flashed, and handed to whoever buys the device.That gap carries a real price. The Institute for Employment Research (IAB) puts the economic damage from industrial and economic espionage at roughly $400 billion a year in the United States, about 2.1 percent of GDP, and around €200 billion in Germany. Its 2025 survey also found that 22 percent of German firms conducting research and development confirmed an espionage attack in the previous five years, compared with 8.3 percent of those without an R&amp;D function [1]. Innovation, in other words, attracts attention.Automotive is a particularly clear case. A single electronic control unit (ECU) can hold years of calibration work, a proprietary control algorithm, and cryptographic keys, all within one binary. This article looks at why that binary is exposed by default, what modern tooling can pull out of it, and how <a href="https://emproof.com/en/solution">Emproof's Nyx</a> hardens it in place.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1790776375099-Emproof_Binary-Protection_Automotive.jpg" style="width:100%px" class="fr-fic fr-dib" />Emproof Binary Protection for Automotive. Source: emproof<h2>The Binary Is the Real Attack Surface</h2>Firmware leaves the building through more doors than most teams count. Most microcontrollers expose a debug or readout interface, and over-the-air update packages can often be downloaded and unpacked [2]. Diagnostic interfaces sit on the vehicle bus. In automotive programs specifically, binaries also move routinely between Tier 1 suppliers and OEMs as a normal part of integration work.Once someone has the file, the tooling is free. Ghidra, the reverse engineering suite released by the US National Security Agency, disassembles the image, reconstructs control-flow graphs, and decompiles machine code into C-like pseudocode. Signature databases identify known library functions, while constant-finder plugins locate cryptographic routines by their lookup tables [2].In practice, an analyst can therefore find the function that validates a license, read the conditions gating a paid feature, or locate a hardcoded key by pattern rather than by luck. None of this requires access to the original source code.<h3>Why Readout Protection Is Not Enough</h3>Readout protection is a single barrier, and a hardware one at that. Fault injection, which glitches supply voltage or clock timing at precisely the right instant, can skip the check outright, while bootloader logic bugs and side-channel methods offer other ways in [2].It also guards only one path, doing nothing about an image pulled from an update server or shared with an integration partner. Meanwhile, the code underneath stays fragile, since roughly 70 percent of severe vulnerabilities at major technology companies trace back to memory-safety issues, and about 80 percent of embedded systems will continue to rely on C and C++ over the next decade.<h2>Large Language Models Lowered the Skill Barrier</h2>Decompilation was never the real bottleneck. Interpreting the output was always the slower and more expensive half of the job.Ghidra hands back pseudocode full of names such as FUN_0800a1c4 and undefined4 uVar7, and turning that into a working understanding of the code has traditionally required an experienced engineer and a considerable number of hours. That step is now largely automated. When decompiler output is fed to a capable large language model (LLM), the model will describe a function's purpose in plain English, propose meaningful variable names, annotate the control flow, and flag branches that resemble a licensing or safety check.The shift is economic rather than technical. Analysis that once cost expert-days now costs minutes, so an attacker can review far more binaries, and someone without deep reverse engineering experience can still obtain useful results. Complexity was never security, although it did reliably buy time, and that time is now shrinking.<h2><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1790702404656-1790702404656.jpeg" width="624" height="367" class="fr-fic fr-dii" />Close-up of an electronic board with components. Source: AdobeStock. </h2><h2>What Emproof Nyx Does to the Compiled Binary</h2><a href="https://emproof.com/en/home">Emproof</a> is a German embedded security company, and Nyx is its binary transformation tool. It runs after compilation, directly on the binary, with no source code requirement and no hardware change. As a result, it can also be applied to third-party, legacy, and already-deployed firmware.<a href="https://emproof.com/en/nyx-microcontroller">Nyx Microcontroller</a> applies several layers of protection in a single pass:<ul><li>Control-flow obfuscation restructures the code layout so that decompiler output no longer mirrors the original program structure.</li><li>Secret hiding conceals keys and sensitive data held inside the image.</li><li>Anti-tamper checks the deployed image against a verified reference at startup, then triggers a response defined by the manufacturer, such as entering a safe state.</li><li>Anti-debug and anti-emulation techniques frustrate dynamic analysis on a live device.</li><li>Exploit mitigation adds stack canaries and control-flow integrity (CFI) to defend against memory-corruption attacks.</li></ul>Because these transformations rewrite the binary, correctness matters as much as protection. Emproof therefore preserves program semantics and uses formal methods, including translation validation, to prove that the transformed code behaves as the original did [3].<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1790776477002-Emproof_Code-Hardening+Kopie.jpg" style="width:100%px" class="fr-fic fr-dib" />Source: emproof<h3>Overhead and Toolchain Integration</h3>Weight and runtime are the reasons embedded code so often ships unprotected. Emproof puts typical memory overhead for Nyx at 10 to 15 percent, and that figure is tunable. Engineering teams can protect the full image or scope transformations to specific modules, optimize each one for size or for speed, and exclude sections with hard real-time deadlines [3].For integration, the tool ships as a Docker container that drops into pipelines such as Jenkins, GitLab CI, GitHub Actions, and Azure DevOps, through a command-line interface (CLI) or a REST API. Analysis and transformation run entirely inside the customer's own infrastructure, and reproducible builds and delta testing keep the tool compatible with existing verification workflows.<h2>Why This Lands Hardest in Automotive</h2>Nyx Microcontroller covers the architectures that dominate automotive design, including Infineon AURIX (TriCore), Arm Cortex-M and Cortex-R, and Renesas RH850 [4]. The requirements tighten further in safety. A compromised braking or steering ECU is not only an intellectual property (IP) problem; it is a functional safety one. The enterprise edition of Nyx therefore carries ISO 26262 ASIL-D certification, the highest Automotive Safety Integrity Level, assessed by TÜV Nord, and it generates audit artifacts for safety documentation [4, 5]. One of the world's largest Tier 1 suppliers is a reference customer.Regulation pushes in the same direction. UNECE WP.29 R155 ties vehicle type approval to a certified cybersecurity management system [6], ISO/SAE 21434 defines the engineering process behind it [7], and the EU Cyber Resilience Act extends secure-by-design obligations to connected products more generally [8].<h2>Protection at the Binary Level Becomes the Baseline</h2>Firmware IP protection is shifting from a differentiator to an expectation. The binary was always readable, but what has changed is that reading it now takes hours instead of weeks, and regulators have started asking manufacturers what they did about it.Working at the binary level is what makes the answer practical, since it doesn't require rewriting, silicon changes, or recompiling of code a supplier may not even own, at an overhead small enough for devices that previously had no room for security at all. Emproof offers browser-based demos for engineers who want to see what the transformed output looks like.<hr /><h3>References</h3>[1] A. Glitz, S. Kohaut, and I. Möller, "Nine percent of businesses fall victim to espionage," IAB Short Policy Report, no. 2/2025, Institute for Employment Research, Nuremberg, Germany, May 2025. [Online]. Available: https://doku.iab.de/kurzber/2025/kb2025-02_en.pdf [2] Emproof, "Defend against the threat of reverse engineering with Emproof Nyx." [Online]. Available: https://emproof.com/en/reverse-engineering. [Accessed: Aug. 2, 2026].[3] Emproof, "Solution: Emproof Nyx." [Online]. Available: https://emproof.com/en/solution. [Accessed: Aug. 2, 2026].[4] Emproof, "Nyx Microcontroller." [Online]. Available: https://emproof.com/en/nyx-microcontroller. [Accessed: Aug. 2, 2026].[5] Emproof, "Emproof Nyx awarded ISO 26262 certification for functional safety in automotive components." [Online]. Available: https://emproof.com/news-posts/emproof-nyx-awarded-iso-26262-certification-for-functional-safety-in-automotive-components/. [Accessed: Aug. 3, 2026].[6] United Nations Economic Commission for Europe, UN Regulation No. 155 — Uniform provisions concerning the approval of vehicles with regards to cyber security and cyber security management system, Geneva, Switzerland, 2021.[7] ISO/SAE 21434:2021, Road vehicles — Cybersecurity engineering, International Organization for Standardization, Geneva, Switzerland, 2021.[8] European Parliament and Council of the European Union, Regulation (EU) 2024/2847 on horizontal cybersecurity requirements for products with digital elements (Cyber Resilience Act), Oct. 2024.

A modern vehicle's competitive advantage lies in compiled firmware. Binary-level protection secures that code without source access, hardware changes, or a heavy memory budget.

The Growing Cost of Embedded IP Theft and How Firmware Hardening Helps

<h3>Interview with Mario Mauerer</h3>Robotics is entering a new phase. The industry conversation is no longer centred on whether robots can perform impressive demonstrations in controlled environments, but whether they can operate reliably, safely, and efficiently in the real world.Across industrial automation, logistics, humanoids, service robotics, surgical systems, and autonomous mobility, robotics teams are facing the same reality: success depends on far more than intelligence alone. Motion systems, actuator integration, thermal management, reliability, safety, and system architecture all play a critical role in determining whether a robot becomes a scalable product or remains a promising prototype.As the Global Business Development Manager, Robotics, at maxon, Mario Mauerer works closely with robotics developers across a broad range of applications. In this interview, he shares his perspective on what separates successful robotic deployments from demos, why motion-system design must happen early, and what robotics teams should prioritize as the industry scales into increasingly complex real-world environments.<h2>Robotics Beyond the Demo</h2>Robotics is advancing quickly, but real-world deployment still looks very different from the lab. In your view, what separates robots that demo well from robots that succeed in the field?Robots that work well in the field typically show one or both of two traits. First, they are designed for a very narrow and often relatively simple task, such as transporting materials, moving carts, or performing repeatable inspections. Second, they are deployed in controlled environments, with close collaboration between the robotics company and the customer.According to Mauerer, successful deployment is only partially about the robot itself.“An equally important aspect is the business approach of the deploying company,” he explains. “Tasks must be sharply defined, product understanding must be consciously nurtured, and growth has to be sensible.”He believes one of the biggest risks for robotics companies is scaling too aggressively before systems are truly ready for deployment.“Companies cannot sell robots that barely work too aggressively,” he says. “Otherwise, their hardware will be overtaken by real-world disappointment.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1781112100914-1781112100914.jpeg" width="602" height="348.914572808456" class="fr-fic fr-dii" />Across today’s robotics landscape, industrial robots, mobile robots, humanoids, service robots, and others, what engineering challenges keep showing up regardless of application?Mauerer points out that robotics remains one of the most multidisciplinary engineering fields today.“The challenges span the entire software stack, locomotion, perception, autonomy, manipulation, as well as hardware, including actuators, power electronics, compute, communication, batteries, mechanics, polymers, fabrication, and supply chain,” he says.Among those challenges, motion systems remain especially important.“Actuators are a very critical aspect because they are everywhere in modern robots,” Mauerer explains. “They need to operate reliably and with high performance while still remaining cost-effective.”That balance between torque, velocity, efficiency, controllability, robustness, and cost continues to define many of the industry’s toughest engineering trade-offs.There is a lot of attention on AI and autonomy right now. What motion-related realities do teams still underestimate when turning robotic intelligence into physical performance?For Mauerer, safety remains one of robotics’ most underestimated challenges.“Robots are terribly strong, while humans and surrounding equipment are often comparatively soft and weak,” he says.As robots move into broader deployment over the next several years, he believes functional safety and safe-motion architectures will become increasingly important.“There are not yet many complete solutions on the market today,” he notes. “But a lot of strong ideas are emerging, and they will become highly relevant very quickly.”While AI continues to improve perception and autonomy, translating intelligence into safe and dependable physical behavior remains one of the industry’s biggest engineering hurdles.From your perspective, what are the clearest signs that robotics as an industry is maturing from exciting prototypes into dependable deployed systems?Mauerer believes the industry is still in the early stages of a larger transition.“Robotics is moving from controlled and repeatable settings toward unstructured environments and more general-use applications,” he explains.He points to examples such as autonomous cleaning systems, boardwalk delivery robots, and autonomous vehicles as early indicators of that shift.“But there is still so much more to come,” he says. “Robots will increasingly become part of everyday environments, powered by autonomous machine-learning systems.”Even with the momentum around robotics and AI, Mauerer emphasizes that widespread deployment remains a long-term engineering challenge rather than a solved problem.“It’s an exciting time,” he says, “but there is still a tremendous amount of work ahead.”<h2>Motion-System Design in Practice</h2>From a drive-system perspective, what are the first questions robotics engineers should ask when moving from concept to system architecture?Mauerer believes robotics teams should begin by defining the broader system architecture before selecting individual components.“What gearing technologies will be used? What communication architecture? What power supply structure? What thermal-management concept? What peak and continuous performance levels are required?” he asks.Only after those system-level decisions are made should teams begin selecting actuators.“At maxon, we focus heavily on highly compact and cost-effective actuator integration,” he explains. “Combined with flexible firmware, that allows robotics teams to build systems that truly fit their architectural needs.”Because maxon works across many robotics sectors, Mauerer says the company often serves as a close engineering partner during actuator-system integration.“We have a lot of experience helping customers create and integrate robotics actuation systems effectively.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1781112100921-1781112100921.jpeg" width="602" height="339" class="fr-fic fr-dii" />Once those requirements are defined, how should robotics teams think about the trade-offs between velocity, precision, energy efficiency, thermal limits, packaging constraints, and overall system performance?According to Mauerer, those trade-offs must be considered as part of the complete robot architecture rather than solved independently.“Teams first need to understand the trade-offs themselves,” he says, “and then select actuators that align with the intended design philosophy.”Simulation has become an increasingly valuable tool in that process.“Our simulation models help customers make these decisions in a data-driven way,” Mauerer explains. “One of our core ideas is: ‘Simulate a lot, build once.’”By improving early-stage modeling and reducing the sim-to-real gap, robotics teams can minimize redesign cycles while extracting significantly more performance from final hardware.What tends to go wrong when motion-system decisions are made too late in the design process, or when they are treated as a component choice rather than a system-level design decision?Mauerer’s response is immediate.“Everything,” he says.Because actuators influence nearly every aspect of robot behavior, including packaging, thermal management, structural dynamics, power distribution, and controllability, late-stage changes often require extensive redesigns.“It’s absolutely critical to consider actuators from the beginning,” he explains. “Once hardware is locked in, changing these decisions becomes extremely difficult.”How important is it to evaluate the full drive system motor, gearhead, encoder, controller, and software behavior together, and how has Maxon’s system-level work shaped the way you think about that integration?Mauerer sees robotics steadily moving away from isolated subsystem components and toward tightly integrated actuator platforms.“This greatly simplifies system-level discussions and supply-chain complexity,” he says. “It also allows robotics companies to move faster with development and deployment.”One of maxon’s goals, he explains, is to abstract much of the underlying motion-system complexity away from customers.“That allows robotics developers to focus more directly on their actual value proposition instead of dealing with every low-level motion detail,” Mauerer says.<h2>Reliability and Real-World Constraints</h2>Many robots today are expected to operate for long hours, in tight spaces, and under demanding duty cycles. What design considerations matter most when reliability and continuous operation are essential?Mauerer believes actuator robustness is one of the defining factors behind reliable robotics deployment.He points specifically to:<ul><li>Overload Tolerance</li><li>Thermal  Resilience</li><li>Ingress Protection</li></ul>“If one actuator fails, the entire robot often goes down,” he explains.That challenge becomes especially significant given the scale of modern robotic systems.“Many robots today contain between 12 and 30 high-torque actuators operating continuously under mechanical and environmental stress.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1781112100925-1781112100925.jpeg" width="602" height="339" class="fr-fic fr-dii" />How do environmental realities shock, vibration, contamination, temperature, or limited installation space, change the way you think about actuator and joint design?According to Mauerer, those environmental realities must be considered during the earliest stages of actuator development.“These factors need to be engineered into the actuator itself,” he says.At maxon, many drive systems undergo testing for shock, vibration, ingress protection, and temperature performance during development.“That allows customers to avoid having to solve many of those challenges independently,” Mauerer explains.For engineers building robots that interact closely with people, equipment, or sensitive processes, how do precision and controllability influence overall system safety and usability?Interestingly, Mauerer believes overall safety architecture matters far more than precision alone.“The broader system-level safety architecture is significantly more important than the precision or controllability of the drive itself,” he explains.What matters most at the actuator level is whether drives can support functional safety concepts such as safe motion.“That requires very careful hardware and software decisions,” he says.How valuable are simulation and early system modelling today in reducing integration risk before hardware is finalized?Mauerer sees simulation as one of the industry’s most important development accelerators.“Teams should iterate much less on hardware and much more in simulation,” he says.Again, he returns to the same guiding principle:“Simulate often, build once.”Accurate actuator models, he explains, are especially important for reducing the gap between simulation and real-world robotic performance.“That’s how teams extract the final 20 percent of performance from a robot.”<h2>Cross-Industry Lessons and What Comes Next</h2>When you look across different robotics sectors, what lessons has maxon been able to carry from one application area to another, and where do requirements become fundamentally different?maxon supports robotics applications ranging from humanoids and AGVs to surgical robots and space systems.“Every application has unique requirements,” Mauerer says. “What makes maxon special is the ability to engineer and manufacture systems that can support such diverse robotics needs.”That broad exposure allows the company to transfer lessons between industries while adapting actuator technologies to highly specialized environments.Logistics and mobile robotics demand high uptime, compactness, and precise positioning. What does that application area reveal about broader robotics design priorities?Mauerer sees logistics and mobile robotics as reinforcing many of the same reliability priorities discussed earlier.High uptime, robust actuator design, thermal resilience, and compact integration all become increasingly important when robots must operate continuously in demanding environments.As robotics deployments scale, those requirements are becoming common across many application categories not just logistics.Looking ahead, what do you think will matter most for robotics teams over the next few years as systems move from impressive prototypes to dependable, scalable deployments?Mauerer believes long-term robotics success will depend heavily on disciplined execution rather than technology hype alone.“Teams need to build strong supply chains, make smart make-versus-buy decisions, and focus on the actual value generated by the robot,” he says.He also emphasizes the importance of selecting dependable engineering partners that can scale from prototyping into high-volume production.“maxon aims to be that type of global actuation partner,” Mauerer explains, “supporting robotics companies from early development through scalable manufacturing.”As robotics systems continue moving into less structured and more dynamic environments, Mauerer believes the industry’s next phase will be defined not by demonstrations, but by dependable real-world performance.<a href="https://www.maxongroup.com/en-us/market-solutions/mobility-solutions/robotics">See what maxon can do.</a>

Mario Mauerer, maxon's Global Business Development Manager, Robotics, discusses what it takes for robotic deployments to be successful in complex real-world environments.

Robots in the Real World: What It Takes to Build Motion Systems That Deliver

As robotics rapidly shifts toward systems that can safely assist, collaborate, and adapt in human environments, the ability to perform coordinated, human-like actions has become a defining frontier. From precision assembly to remote intervention, bimanual manipulation and intuitive teleoperation are unlocking new levels of dexterity and autonomy in modern robots.In this webinar, hosted by Franka Robotics, Prof. Fei Chen, one of the leading voices in collaborative and humanoid robotics—shares how the CURI human-like robot is advancing this frontier through innovative control, interaction, and teleoperation strategies. Join us to explore the technologies shaping the next generation of intelligent, versatile robotic systems. <a href="https://wevlv.co/frank_webinar_lp_direct" target="_blank" rel="noopener noreferrer" class="button-main-action">Register now</a><h2>Event Details</h2><h3 id="isPasted">📅 Date: December 10, 2025</h3><table><tbody><tr><td>Location</td><td>Virtual (Join from anywhere)</td></tr><tr><td>Time</td><td>10 AM (CET)</td></tr></tbody></table><h2> </h2><h2 id="isPasted">Who should attend</h2><h2> </h2><ul><li>Industrial automation leaders exploring next-generation collaborative robotic systems</li><li>Robotics engineers and researchers working on manipulation, teleoperation, or human–robot interaction</li><li>Developers and integrators interested in dexterous, human-like robotic capabilities</li><li>Academics and students studying advanced control, bimanual manipulation, or humanoid robotics</li><li>Innovation teams assessing emerging robotic technologies for real-world applications</li></ul><h2>About the Speaker</h2><table><tbody><tr><td><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzYzNjM4MzUwODkxLUZlaV9fLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width:100%px" class="fr-fic fr-dib" /></td><td>Prof. Fei Chen, Head of the Collaborative and Versatile Robots Laboratory at The Chinese University of Hong Kong, focuses his research on humanoid bimanual manipulation and human-robot collaboration. Prior to joining CUHK, he led research efforts at the Italian Institute of Technology and contributed to numerous European, Italian, and Hong Kong projects. With more than 100 publications, he is also deeply engaged in the IEEE community, serving in key leadership roles across major international robotics conferences and technical committees. In his talk on Bimanual Manipulation and Teleoperation of the CURI Human-like Robot, he highlights how advanced control and interaction strategies are shaping the next generation of intelligent, versatile robotic systems. </td></tr></tbody></table><h2 id="isPasted">Why you should attend</h2><ul><li>Learn how bimanual manipulation enables more complex, human-level tasks in collaborative robotics</li><li>Understand cutting-edge control and interaction strategies used in the CURI human-like robot</li><li>Discover how researchers working on advanced manipulation can benefit from using a highly sensitive <a href="https://franka.de/franka-research-3" target="_blank" id="isPasted">cobot arm</a> that complements bimanual teleoperation studies. <a href="https://wevlv.co/frank_webinar_lp_direct" target="_blank" rel="noopener noreferrer"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzYzNjM4Mjg2MjE2LVdlYmluYXIgcG9zdCAxMCBEZWMgKDEpLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width:100%px" class="fr-fic fr-dib" /></a></li></ul>

Join Prof. Fei Chen as he explores advanced bimanual manipulation and teleoperation techniques shaping the future of intelligent human-like robots in this expert-led robotics session.

Bimanual Manipulation and Teleoperation of the CURI Human-like Robot

'Link-bots' can move, explore, cooperate without sensing or computation

Robots made of linked particle chains

<h2 dir="ltr" id="isPasted">Introduction</h2>Service robotics is a fast-growing field, with intelligent, adaptive, and autonomous systems becoming increasingly prevalent across industries. As these robots become more sophisticated, engineers face more complex challenges when it comes to achieving real-time perception, precise motion control, and environmental awareness. TDK Corporation, a leading Japanese electronics company, has developed a comprehensive sensor ecosystem to address these challenges. The result is a modular and scalable platform for next-generation robots.TDK’s sensor ecosystem is integrated with the ROS 1 and ROS 2 framework, and is a unified solution for robotic developers. Drawing inspiration from the sensory capabilities often imagined in anime and manga, this technology brings us closer to creating robots that are highly perceptive and responsive. By combining cutting-edge sensor technologies and flexible algorithms, TDK is leading the way for a new era of intelligent robotics that can interact with humans and their environment. &nbsp;<h2 dir="ltr">The TDK Sensor Ecosystem: A Comprehensive Robotics Solution</h2>The core of TDK’s ecosystem is a collection of modular sensors. Each sensor is engineered to perform a specific function, yet they are designed to work together to improve robot performance.Motion sensing is handled by 6-axis and 9-axis MEMS-based Inertial Measurement Units (IMUs), like the ICM-42688P. These units provide the high-precision tracking necessary for balance, navigation, and posture correction. Environmental factors are monitored through gas, humidity, and barometric pressure sensors. For example, the IPC-10111 capacitive barometric pressure sensor detects changes in air quality and altitude, which is particularly valuable in industrial contexts.Robots also gain a sense of touch and proximity through capacitive and piezoelectric touch sensors. These components allow machines to detect objects and interact safely with humans by responding with tactile feedback that makes interactions more intuitive. Additionally, a layer of acoustic and spatial awareness is provided by digital microphones (like the ICS-43434) and ultrasonic Time-of-Flight sensors (including CH101 and CH201). These components enable functions like obstacle avoidance and voice interaction.1This comprehensive sensor suite enables flexible hardware configurations, which allows developers to customize robots for specific tasks. The plug-and-play integration with ROS also reduces development time and improves cross-compatibility between components. TDK's multi-sensor fusion techniques merge data from different types of sensors to give robots a holistic perception of their environment, like the multi-sensory awareness often portrayed in the futuristic robots of pop culture.<h2 dir="ltr">Motion Sensing and Adaptive Locomotion</h2>TDK’s IMUs play a very important role in robotic motion control by enabling precise movement and posture management. These sensors allow robots to move smoothly, maintain balance, and even prevent falls, particularly in humanoid designs.When IMUs are integrated with other sensing technology, like LiDAR and vision-based SLAM (Simultaneous Localization and Mapping), it improves robotic positioning and mapping. This sensor fusion approach allows robots to navigate complex environments with greater accuracy and adaptability.Robotic systems benefit from advanced algorithms like Kalman filtering, which compensates for sensor drift and ensures fluid transitions between movements. In AI-enhanced applications, IMUs also support predictive trajectory planning, so that robots can adapt to chaning environments in real time. These capabilities are demonstrated in robots like ANYmal, developed by ANYbotics (a TDK Ventures portfolio company), which uses TDK sensor tech to operate autonomously in industrial settings and cluttered environments.2These capabilities are especially apparent in robots like ANYmal, developed by ANYbotics, a company backed by TDK Ventures. ANYmal demonstrates advanced locomotion and navigation in industrial environments and is a real-world application for TDK’s sensor technologies.<h2 dir="ltr">Environmental Awareness: Enhancing Robot Sensing Capabilities</h2>TDK’s environmental sensors also have real-world applications. These sensors significantly enhance robots’ ability to detect and interact with their surroundings and operate safely around humans. TDK’s environmental sensors support this by providing real-time data on air quality, humidity and atmospheric pressure:<ul><li dir="ltr">Gas Sensors&nbsp;— The TCE-11101 MEMS-based CO2 gas sensor detects harmful substances with high accuracy, which is important in industrial and healthcare environments.</li><li dir="ltr">Humidity Sensors — TDK's humidity sensors, featuring ±5% RH accuracy, enable adaptive climate control in smart homes and workplaces.</li><li dir="ltr">Pressure Sensors — The ICP-10111 capacitive barometric pressure sensor, integrated into TDK's RoboKit1, allows for altitude detection and atmospheric pressure monitoring.</li></ul>Beyond atmospheric sensing, TDK sensors enhance object detection and give robots a reliable, non-contact method for navigation and interaction:<ul><li dir="ltr">Object Detection — TDK's SmartSonic ultrasonic Time-of-Flight (ToF) sensors, such as the ICU-20201, provide non-contact object detection and navigation up to 5 meters in any lighting condition.</li><li dir="ltr">Voice Detection&nbsp;— The ICS-43434 digital I²S microphones enhance spatial awareness in voice-activated robots.3</li></ul>With these advanced sensing capabilities, robots can better adapt to their environment and interact with humans in a more intuitive way.<h2 dir="ltr">Intelligent Human-Robot Interaction with TDK’s Sensor Fusion</h2>Human-machine interaction is more engaging with sensors that understand gestures, proximity, and touch. TDK’s sensor fusion technology also facilitates more natural and intuitive robot-human interactions. Touch and proximity sensing is especially relevant for consumer-facing robotics, where intuitive feedback helps build using trust:<ul><li dir="ltr">Haptic Touch Sensors — TDK's piezoelectric actuators provide high-definition haptic feedback for enhanced touch interactions in robotic applications.</li><li dir="ltr">Gesture-Based Control — The CH101-00ABR ultrasonic ToF sensor enables gesture-based control and user input recognition with a configurable 180° field of view.</li></ul>Through TDK’s RobotKit 1 development platform, integrated with ROS and TDK’s adaptive learning models, robots can tailor their responses over time, learning from repeated interactions. This personalized behavior improves functionality and provides more dynamic human-machine interaction:<ul><li dir="ltr">Holistic Awareness — The ICM-42688-P 6-axis IMU, combined with other sensors in the RoboKit1, provides real-time situational awareness.</li><li dir="ltr">Adaptive Learning&nbsp;— TDK's RoboKit software, integrated with ROS, enables personalized interactions using adaptive learning models.</li><li dir="ltr">Social Applications&nbsp;— The combination of IMUs, microphones, and ToF sensors in the RoboKit1 platform enhances social robotics applications.</li></ul>These advancements bring us closer to the kind of seamless human-robot interactions often portrayed in science fiction, where robots can understand gestures, emotions, and intentions with remarkable accuracy.<h2 dir="ltr">ROS 1 and ROS 2: The Software Framework Unifying TDK’s Sensor Ecosystem</h2>The Robot Operating System (ROS) is a critical standardized platform for robotic perception, planning, and control. TDK’s ROS 1 and ROS 2 extension includes pre-configured sensor libraries and real-time data synchronization, streamlining development and performance.This software foundation supports critical functions like real-time sensor processing, AI-enhanced decision-making, and cross-platform compatibility. Developers can use the same ecosystem to build robots for very different applications, from smart retail kiosks to autonomous healthcare aides, without having to reinvent the platform for each use case.<h2 dir="ltr">Real-World Applications of TDK’s Robotics Sensor Platform</h2>As highlighted throughout this series, TDK’s sensor platform is already making a big impact in the real world. Its technologies are used in robotics systems for healthcare, retail, personal assistance, and more. The investment by TDK Ventures in companies like ANYbotics further underlines the company’s commitment to driving innovation in robotics.By enabling smarter, safer, and more adaptable machines, TDK is solving today’s engineering problems and laying the groundwork for human-robot coexistence.<h2 dir="ltr">Conclusion</h2>TDK's sensor ecosystem is more than just a set of components—it's a modular, scalable, and AI-driven solution that is already shaping real-world robotics. As innovation continues and ethical considerations evolve, we are closer than ever to a future where intelligent, responsive robots integrate seamlessly into our daily lives—much like the imagined robotic companions of anime and manga.Learn more about TDK’s innovative advancements at <a href="https://www.tdk.com/en/index.html">https://www.tdk.com/en/index.html</a>.<hr>Resources:<ol><li>Qualcomm Robotics RB3 Development Kit. Accessed from <a href="https://invensense.tdk.com/technology/robotics/" id="isPasted">https://invensense.tdk.com/technology/robotics/</a></li><li>TDK IMU Product Page. Accessed from <a href="https://product.tdk.com/en/products/sensor/mortion-inertial/imu/index.html" target="_blank" rel="noopener noreferrer">https://product.tdk.com/en/products/sensor/mortion-inertial/imu/index.html</a></li><li>“Sensor solutions that enable advanced control of service robots”. Accessed from <a href="https://product.tdk.com/en/techlibrary/solutionguide/sensor-for-service-robots.html" id="isPasted">https://product.tdk.com/en/techlibrary/solutionguide/sensor-for-service-robots.html</a></li></ol>

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