Automotives

<div class="textimage basecomponent"><div class="textimage ">The future of mobility is measured in milestones: this February, Google subsidiary Waymo announced that its fleet of self-driving cars had covered over 8 million kilometres on public roads. This came shortly after Uber&rsquo;s announcement that it had completed 3 million kilometres of autonomous driving. If industry has its way, then we will soon be sharing all our streets with vehicles controlled by algorithms instead of drivers. But is that a realistic scenario? Or simply a rose-tinted vision of a tech-driven future?We asked one of the leading experts in this field, Italian researcher Emilio Frazzoli, Professor of Dynamic Systems and Control at ETH Zurich since October 2016. &ldquo;It all depends what level of autonomous mobility you have in mind,&rdquo; he replies. &ldquo;I would say it will be at least another 15 years before you can buy a self-driving car from a dealer. But if you mean a limit- ed kind of car sharing, then it&rsquo;s already happening.&rdquo; In fact, this latter concept is a core part of Frazzoli&rsquo;s own research. In mid-2018, the ride sharing company Lyft launched a service on the Las Vegas Strip that allows anyone to book one of a fleet of 30 BMWs through its app. The driverless cars are controlled by algorithms developed by vehicle technology company Aptiv, which acquired NuTonomy &ndash; the start-up founded by Frazzoli &ndash; in October 2017.<h2>Rethinking urban mobility</h2>Before joining ETH, Frazzoli spent ten years as a professor at the prestig- ious MIT in Boston. Autonomous systems &ndash; initially aircraft and drones &ndash; were the focus of his work right from the start. &ldquo;The technical side of that was generally pretty cool, but it wasn&rsquo;t really doing much to help solve the challenges society is facing.&rdquo; In 2009, he found himself pondering a fundamental question: &ldquo;Back then, the main argument for conducting research into self-driving cars was the idea that they would make road traffic safer.&rdquo; While acknowledging the truth of that statement, at least in the long term, Frazzoli realised there was potentially a much larger, medium-term benefit to be gained from completely rethinking the issue of individual mobility for city dwellers.&ldquo;The goal of my research group is a form of mobility that combines the convenience of a private car with the sustainability of public transport.&rdquo; In other words, a kind of Uber, but driverless and therefore much more economical and available. Plus &ndash; thanks to electrification and better capacity utilisation &ndash; a solution that offers significantly lower energy consumption and CO2 emissions. Right now, people use private cars, on average, just 5 percent of the time, which means the cars spend the remaining 95 percent of the time standing idle in car parks and garages or on the street. This makes no sense in terms of sustainability, urban development or resource efficiency.Frazzoli&rsquo;s start-up NuTonomy, which develops control software for autonomous vehicles, began drawing up plans to test self-driving cars in Singapore back in 2014. At around the same time, the professor published an article in which he investigated how replacing all the private vehicles in the 719-square-kilometre city-state with shared, self-driving vehicles would affect traffic volumes. His results showed that the mobility needs of Singapore&rsquo;s entire population could be met with some 40 percent of the vehicles (350,000 instead of 800,000).One year later, Prime Minister Lee Hsien Loong unveiled his vision of a &ldquo;car-lite future&rdquo; based on autonomous vehicles, the expansion of public transport and the fostering of slow traffic such as walking and cycling. With 5.5 million inhabitants and a population density of 7,697 people per square kilometre &ndash; compared with Switzerland&rsquo;s figure of 203 &ndash; Singapore is more dependent on efficient transport than any other major metropolitan area.That is why Singapore has spent years trying to crack down on demand for private cars by imposing high taxes and charging up to 70,000 dollars for the certificates of entitlement required to own a vehicle. More than ten companies are current- ly testing their systems in a 2-hectare test facility at Nanyang Technological University in the western part of Singapore Island. And plans are already in place to operate the first self- driving buses outside rush hours in three of the city&rsquo;s suburbs starting in 2022.</div><h2>Simulating transformation</h2>Pieter Fourie&rsquo;s &ldquo;laboratory&rdquo; is located in the western part of Singapore. He works in a sunlit office on the sixth floor of the CREATE Tower, a building encased in vertical foliage at the National University of Singapore (NUS). Here, he conducts research into the cities of the future on behalf of ETH Zurich&rsquo;s Future Cities Laboratory. Fourie heads up the Engaging Mobility project, which brought together government authorities and universities at a preliminary workshop in July 2017. The goal was to define the basic conditions required to implement city-wide, on-demand mobility using autonomous cars and buses.The researchers used the results of the workshop to formulate key research questions such as: What do we do with the current supply of parking spaces if the majority of vehicles are constantly on the road? Do we need to redefine the layout of our roads? And what effect will auto- mated, electrified transport have on existing public transport, energy requirements and safety?Fourie explores these and similar issues using the MATSim simulation platform developed by a group led by Professor Kay Axhausen at ETH Zurich&rsquo;s Institute for Transport Planning and Systems. MATSim is agent-based, which means the simulation is driven by the behaviour of individual agents rather than by overarching rules. &ldquo;On the basis of Singapore&rsquo;s most recent demographics, we are modelling a synthetic population that is as close as possible to the real one,&rdquo; Fourie says.Within this population, each individual agent exhibits a certain mobility behaviour and has a specific destination based on real-life traffic data. Fourie is now at the stage of tinkering with the underlying conditions, including the number of vehicles employed, their size, the maximum permissible waiting times for passengers, the availability of parking spaces and a variety of different traffic flows. He then lets the synthetic population loose on the simulation for 24 hours. The system automatically evaluates how efficiently the individual agents were able to reach their destinations in each scenario.Right now, Fourie&rsquo;s team is programming these kinds of simulations for the waterfront area of Tanjong &shy;Pagar, a district of some 2 square kilometres in the western part of Singapore. This site is currently being converted from a container terminal into a residential and commercial area. Fourie has already simulated more than 200,000 trips involving 60,000 individual agents. This included calculating how big the fleet of autonomous vehicles would need to be and how many kilometres the vehicles would have to cover to achieve an equivalent level of service in three different street typologies.The researchers also simulated four different parking strategies for a fleet of 4-, 10- and 20-seater vehicles. Preliminary results suggest that the transport system is at its most efficient if the shared vehicles are allowed to park in the street when they stop receiving requests for pick-ups. That holds true even if it means temporarily reducing the roadway capacity by one lane. The researchers&rsquo; findings also suggest that having fewer, but correspondingly larger, pick-up and drop-off stations has a favourable impact on traffic flow by reducing the detours cars have to take to collect passengers. The stations also need to be big enough to accommodate different vehicle sizes. Fourie is hoping to have these kinds of simulations up and running for the entire island as early as next year.<h2>Decision-making dilemmas</h2>Despite these rapid developments in Singapore and the fledgling services coming online in Las Vegas, Emilio Frazzoli still sees plenty of challenges ahead, especially when it comes to dealing with chaotic environments. &ldquo;We still don&rsquo;t know exactly how autonomous vehicles should behave in traffic,&rdquo; he says, explaining that this involves dozens of decision-making dilemmas that are an integral part of everyday traffic situations. For example, should a self-driving car cross a double line in order to avoid a potential collision? And what if an innocent road user is injured as a result of a manoeuvre designed to save a culpable driver from a fatal crash? These are the kinds of decisions that have to be defined when programming control algorithms. One key focus of Frazzoli&rsquo;s current research is therefore the &ldquo;rulebooks&rdquo; that should be used to prioritise these various decision- making criteria in control algorithms. At the top of the hierarchy are rules designed to ensure road users&rsquo; safety. At the bottom are rules designed to enhance passenger comfort.In a recent article, Frazzoli and his team estimated that it would take 200 rules in 12 hierarchy groups to prepare vehicles for every possible scenario, including low-priority rules such as not frightening animals on the edge of the road. Frazzoli feels the time has come for a broader public &shy;debate on autonomous driving: &ldquo;The coding of safety and liability rules is not something we should simply leave in the hands of engineers working for private companies&rdquo;. Ultimately, he argues, it is in everyone&rsquo;s interest to incorporate our new, virtual drivers into urban traffic as smoothly as possible &ndash; much like we do with new human drivers, but with the greater levels of safety, predictability and efficiency that autonomous vehicles offer.&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;</div>

Emilio Frazzoli’s start-up NuTonomy develops control software for autonomous vehicles and uses Singapore as a test bed. (Photograph: NuTonomy)

Car sharing with autonomous vehicles could improve cities in many different ways. Singapore is taking a pioneering role, working with ETH researchers to explore the potential of personalised, electrified and automated public transport.

Algorithms take the wheel

Some call it a marvel of technology. Some call it a fad. Self-driving cars are constantly making the headlines. These vehicles, designed to carry passengers from point A to B without a human maneuver, are promised to bring greater mobility, reduce street congestion and fuel consumption, and create safer roads.Driverless vehicles are hardly a novel concept anymore, but what is really going on in the autonomous car industry? How do self-driving cars work? Who are making these cars? Why is it taking this long? This article will answer these questions.<h1 data-selectable-paragraph="">How do self-driving cars work</h1>To reach a destination, a driverless car needs to know the route, understand its surrounding, observe traffic rules, and make correct judgments when interacting with other vehicles and pedestrians on the road. To accomplish all this, it relies on the following key technologies:<ul><li data-selectable-paragraph="">Camera: to see things like lanes and traffic lights</li><li data-selectable-paragraph="">Radar: to sense objects, especially the big ones, and determine their distances</li><li data-selectable-paragraph="">Lidar: also to sense objects as Radar, but it’s much better in detecting small objects and mapping 3D objects. Unfortunately, <a href="https://www.wired.com/story/lidar-self-driving-cars-luminar-video/?source=post_page---------------------------" target="_blank">it is expensive and can still be a little unreliable at times.</a></li><li data-selectable-paragraph="">AI: the brain of the car. It combines the car’s sensors and camera visions to understand the path ahead and determine how the car should be maneuvered.</li></ul><h1 data-selectable-paragraph="">Leveling up</h1>The progression of driverless technology doesn’t go from no automation at all to full-on self-driving with no human intervention. In fact, SAE International, an automotive standardization organization classified autonomous cars to 5 levels:<ul><li data-selectable-paragraph="">Level 0 — No Automation:&nbsp;Human driver controls everything — steering, braking, and accelerating.</li><li data-selectable-paragraph="">Level 1 — Driver Assistance:&nbsp;The car provides some help with either steering OR accelerating. Cars with cruise controls fall into this category.</li><li data-selectable-paragraph="">Level 2 — Partial Automation: The car can control both steering and accelerating at the same time, with human drivers monitoring the environment and keeping their hands on the wheel. Tesla’s autopilot is an example of Level 2 automation.</li><li data-selectable-paragraph="">Level 3 — Conditional Automation: The car will control the car and monitor the environment, but may request human drivers to intervene in certain situations. Audi A8’s Traffic Jam Pilot was able to achieve level 3.</li><li data-selectable-paragraph="">Level 4 — High Automation: Compared to level 3, the autonomous car can handle more complex driving tasks. It may still ask for human intervene in rare situations such as extreme weather.</li><li data-selectable-paragraph="">Level 5 — Full Automation:&nbsp;A truly self-sufficient car. It will not ask for human intervene and can carry out all driving tasks. Google’s Waymo is an example of that.</li></ul><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1563614129520-self-driving_tech_levels.jpg" style="width: 766px;" class="fr-fic fr-dib"><h1 data-selectable-paragraph="">Key players in the market</h1>Many companies recognized that whether they like it or not, the disruption is inevitable. Hoping to get ahead of the competition, they have invested billions of dollars in the R&amp;D of key technologies behind self-driving cars.The transportation revolution attracted both big corporations and startups. <a href="https://blog.cometlabs.io/263-self-driving-car-startups-to-watch-8a9976dc62b0?source=post_page---------------------------" target="_blank">Over 260 startups</a> are ready to tackle every aspect of the ecosystem while forming strategic partnerships with others. Big corporations are also investing billions of dollars to aggressively establish and implement roadmaps. Some of the leading and high-profile companies include the following.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1563614178106-436da7b4-2493-11e9-8ce6-5db4543da632.webp" style="width: 766px;" class="fr-fic fr-dib"><h2 data-selectable-paragraph="">General Motors</h2>As one of the leading car manufacturer, GM has spent a lot of money to maintain its lead position in driverless cars. In 2016, it acquired self-driving tech company Cruise Automation for over $1billion. This unit received $2.25 billion investment from SoftBank and an additional $1.1 billion from GM itself in 2018. Furthermore, eager to dominate the market, GM bought its own LIDAR manufacturer so that it can rely less on partnerships. GM has been testing in San Francisco with a plan to expand to New York City. It plans to commercialize its self-driving car with ride-hailing services in 2019.<h2 data-selectable-paragraph="">Waymo</h2>Believe it or not, Waymo was established a decade ago. However, it is still considered to be the cool kid in the block. Valued at $175 billion, Waymo has accumulated over 8 million self-driving miles with a fleet of Chryslers, Hondas, and Jaguars. Just recently, Waymo announced its plan of purchasing 62,000 Fiat Chrysler vans for its ride-hailing service. Pledged to commercialize by the end of 2018, Waymo is leading the self-driving car race.<h2 data-selectable-paragraph="">Uber</h2>From the high-profile lawsuit against Waymo to the operation halt in Arizona and Pittsburgh, Uber has had a bumpy ride. However, Uber has not given up. With partners like Volvo and Daimler, Uber scored a $500 million investment from Toyota this year. Back on the streets of Pittsburg, Uber’s autonomous vehicle is driven manually to recalibrate its HD map of the city. It is also pumping money to its driverless car engineering hub in Toronto to continue its mission.<h2 data-selectable-paragraph="">Lyft</h2>Compared to Uber’s aggressive development and expansion strategy, Lyft’s approach is more controlled. Lyft partnered with Aptiv, a formerly bankrupted company that turned its tide by investing in driverless technology. Together they have completed over 5000 paid rides with 20 vehicles in Las Vegas, servicing popular destinations on the strip. When requesting Lyft rides, users can opt-in for driverless cars, and for now, there’s still a safety driver in the vehicle to monitor the ride.<h2 data-selectable-paragraph="">Tesla</h2>Tesla takes a completely different approach to driverless technologies. Instead of using LIDAR, Elon Musk believes the advancement of camera and image recognition technology are sufficient for automation. Although its current vehicles are equipped with auto-driving features, they still require human attention, and we have seen accidents reported due to the lack of human interventions. It’s yet to be seen how far Tesla can push its self-driving technology.<h2 data-selectable-paragraph="">Baidu</h2>Baidu leads the biggest driverless car effort in China since 2014. In 2017, it announced the Apollo, an <a href="https://github.com/ApolloAuto/apollo?source=post_page---------------------------" target="_blank">open source project</a> to facilitate the research and development of driverless cars. Baidu aimed to mass produce autonomous vehicles by 2019 to 2020, but their ability to successfully carry out this plan becomes doubtful when it lost multiple key AI talents and COO Lu Qi.<h1 data-selectable-paragraph="">What’s taking so long?</h1>Waymo was first established in 2009, and almost a decade later, its cars are finally getting ready to become commercialized. That’s quite a long time to release a product. Why does it take so long? Although eager to advance as quickly as possible, the self-driving car industry is facing key some challenges that they need to overcome in order to truly become common on streets. These issues include:LidarDriverless car experts agree that Lidar plays a key role in ensuring a safe trip. Similar to Radar, Lidar is also used to detect surrounding objects’ distance and shape. However, instead of using radio waves, Lidar sends infrared lights, which can better detect smaller objects (like rocks, or a cyclist and ) and objects in closer distance compared to Radar.So is Radar unnecessary for self-driving cars? Definitely not. Radar works better when detecting objects that are further away, and it’s much less sensitive to extreme weather than Lidar.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1563614240277-content_dam_lfw_print_articles_2018_11_1811lfw_jw_2.png" style="width: 766px;" class="fr-fic fr-dib"> Lidar measures distance to a target by illuminating the target with laser light and measuring the reflected light with a sensor. Image by LuminarAs you have probably figured, Radar and Lidar are a complementary pair and they can accomplish so much together for the driverless future.However, even though Lidar is not a new technology, it still needs further development. The bulky equipment comes with a hefty price tag of $75k per piece and is not ready for mass production. Driverless cars will also benefit greatly from higher resolutions and greater distance range than what the current Lidar technology can offer.Lidar’s room for improvement can be translated into a huge business opportunity. The trade secrets regarding Lidar technology was in the eye of the Waymo v. Uber storm in 2017. Now, over 50 companies are trying to improve Lidar and make it cheaper for mass production.AIDriverless cars rely on artificial intelligence to make judgments. AI identifies objects on the streets and determines how the car should behave in different situations. To train such intelligence, scientists use a huge amount of data so that the algorithm can learn objects on the road. For example, the algorithm can identify a bicyclist on the street because it has seen images of tens and thousands of bicyclists.We all have high expectations for what AI can accomplish, but it’s still far from perfect. For example, in the fatal accident caused by Uber’s self-driving car, the algorithm failed to recognize the victim, causing a delay in reactions. Furthermore, there are so many moving parts (literally) on the street, producing millions of possible scenarios. A tremendous amount of data is needed for the system to learn how to handle these scenarios, and we are just simply not there yet.MappingAnother race is happening in the mapping world. Driverless cars need more than the regular map we get on our phone. Although a self-driving car’s sensors and cameras can pick up objects on the street, it would be a lot safer to have an accurate 3D map of the route so that the car is familiar with all aspects of the street and can focus on detecting other elements like pedestrians. 3D maps also provide tremendous value during extreme weathers, when the data cameras and sensors provide are inadequate.Mapping is difficult because it requires a lot of data to produce an accurate representation of the world. Even Google map has low fidelity for rural areas. What’s more challenging is the constant need for continuously data update. Companies like Here, TomTom, DeepMap, and Google are all in the race to produce maps that can be an irreplaceable part of the ecosystem.InfrastructureJust as how new infrastructure was needed when automobile replaced horse carriages, self-driving cars also require updated infrastructure. Worn-off pavement markings and unclear street signages will all need to be upgraded for self-driving cars so that they can clearly see the road. This may sound easy, but the implementation calls for the creation of a uniformed standard, and collaboration between federal, state, and local agencies. We also need to build connected infrastructure as a way for driverless cars to communicate with nearby infrastructure. For example, in construction zones, rather than relying on worker’s hand gestures to direct traffic, devices should be installed so that signals can be transmitted to driverless vehicles.The infrastructure overhaul comes with a hefty price tag, but it is crucial to the safety of driverless cars. <a href="https://thehill.com/opinion/technology/353034-self-driving-cars-are-coming-but-us-roads-arent-ready-for-the-change?source=post_page---------------------------" target="_blank">States like Michigan</a> have begun testing new infrastructure with its modernization projects, and we will see more of them coming.Human CommunicationAutonomous vehicles may have programmed behaviors backed by AI to handle various scenarios on the street, but humans will also need to learn how to interact with autonomous vehicles.Frequently, drivers, pedestrians, bicyclists, and motorcyclists use gestures and eye contacts on streets to signal their intentions. But how will that work with driverless cars? One of the self-driving technology company Drive.ai <a href="https://medium.com/@andrewng/self-driving-cars-are-here-aea1752b1ad0?source=post_page---------------------------" target="_blank">tackled this problem by having these cars “talk to” pedestrians.</a> Embedded with LED screens on the front, rear and sides of the vehicle, these screens can display texts such as “waiting for you to cross”, providing a mean for the vehicle to demonstrates its intention.Human TrustWhile the industry has slowly got a grasp of human-vehicle communication, human trust is a more complex problem to solve, and it directly affects the adoption rate. In a <a href="https://www.reuters.com/article/us-autos-selfdriving-usa-poll/most-americans-wary-of-self-driving-cars-reuters-ipsos-poll-idUSKBN1FI034?source=post_page---------------------------" target="_blank">research conducted by the Reuters and Ipsos</a>, only 38% men and 17% women surveyed said they would feel comfortable riding a driverless car. <a href="https://www.researchgate.net/publication/324912188_Trust_in_driverless_cars_Investigating_key_factors_influencing_the_adoption_of_driverless_cars?source=post_page---------------------------" target="_blank">Another Research</a> shows that to gain people’s trust in autonomous vehicles, the industry needs to demonstrate the cars’ reliability, performance, privacy (e.g., location tracking), and security (e.g., hacking).The wariness is not surprising, given the novelty of the technology and the potential catastrophic consequence if things go wrong. These research results demonstrate that besides improving vehicle safety and performance, self-driving car manufacturers must establish trust with consumers by educating and promoting. Designing a compelling rider experience that addresses their needs and pain points is also important, as it highlights the benefits of riding self-driving cars.<h1 data-selectable-paragraph="">Buckle up for the future</h1>Human species have the <a href="http://www.slate.com/articles/health_and_science/science/2014/03/the_science_of_what_separates_us_from_other_animals_human_imagination_and.html?source=post_page---------------------------" target="_blank">exceptional talent of imagination</a>. What further sets us apart from other animals is the ability to build nested scenarios in our minds, share our fantasies with each other, and eventually work on a common goal that is based on imaginations. The rise of self-driving vehicles is precisely an outcome of collaborative imagination. We are witnessing an evolution based on rapid technology advancement, and sooner or later, human mobility may never be the same. We don’t know how many twists and turns there are ahead of us, but the journey to our destination sure is exciting.

Everything you should know before your next conversation about self-driving cars

A Beginners Guide to Self-Driving Cars

What sensors do we need for building a fully autonomous robot?Autonomous Robots are equipped with numerous sensors to perceive its surroundings as well as be aware of its own movements. The first step for achieving autonomy of robots i.e., robots moving from point A to point B on its own without colliding with anything, is awareness of the surrounding environment. Autonomous robots have a stack of sensors, among them, Wheel Odometers, IMU, GPS, Lidar, and Multiple Cameras being the most common. The current developments in the autonomous vehicles space also make use of sensors like Radar, Stereo Cameras in combination with the above stack.&nbsp;What is localization?<a href="http://www.cs.cmu.edu/~rasc/Download/AMRobots5.pdf" rel="noopener noreferrer" target="_blank">Localization</a> means to determine the current position and orientation of a body with respect to some coordinate system.How humans localize, is it a difficult task?Humans very naturally tend to determine their &nbsp;current position with respect to the different environmental landmarks/features around them. So, when we localize ourselves we tend to understand we are at some given distance and at certain angle from some house/tree/lamp post or any other landmarks. Given an empty featureless space even humans cannot localize. Thus, features are important for this localization task and thus we need a map filled with features/landmark to localize ourselves in that map, else it is an impossible task to perform.Then how do robots do it?The SLAM algorithm has two parts; the first is mapping and the second is localization. Autonomous ground robots have visual sensors like Lidar and Camera which can pretty well map its environment around. Then with 3D reconstruction from their data, robots generate something called a HD map. HD maps differ from normal maps, the former has a lot more features than the later. Once this map is ready, the robot starts localizing itself in this map. <a href="http://robots.stanford.edu/papers/thrun.pf-in-robotics-uai02.pdf" rel="noopener noreferrer" target="_blank">Particle Filter</a>, <a href="https://www-users.cs.umn.edu/~isler/pub/tase10.pdf" rel="noopener noreferrer" target="_blank">Triangulation</a>, <a href="http://www.cs.toronto.edu/~urtasun/courses/CSC2541/03_odometry.pdf" rel="noopener noreferrer" target="_blank">Visual Odometry</a> are some methods used for this purpose.&nbsp;Another widely used method for localization is <a href="https://www.cs.cmu.edu/~motionplanning/lecture/Chap8-Kalman-Mapping_howie.pdf" rel="noopener noreferrer" target="_blank">Extended Kalman Filter</a>. An EKF is a non-linear version of the linear Kalman Filter. It's a state-space estimator, what that means is that it can help in estimating the current state given input of the previous state. It's is used as a data fusion filter for localization tasks in robotics. It takes inputs from the IMU, Wheel Odometers and GPS and performs its computation based on a CTRV (Constant Turn Rate Velocity) model for a wheeled ground vehicle, to estimate the current position and orientation of the vehicle. Often this localization information is fused with visual odometry to achieve an accuracy of 100mm in the localization task.Once the robots know where they are in a map they can now start planning their path for the destination point B. Thus, invoking another interesting field of research called Path Planning. &nbsp; More resources on SLAM :1. <a href="https://blog.cometlabs.io/teaching-robots-presence-what-you-need-to-know-about-slam-9bf0ca037553" rel="noopener noreferrer" target="_blank">Teaching robots presense</a>2. <a href="https://en.wikipedia.org/wiki/Simultaneous_localization_and_mapping" rel="noopener noreferrer" target="_blank">SLAM</a>3. <a href="https://ieeexplore.ieee.org/abstract/document/1638022" rel="noopener noreferrer" target="_blank">Simultaneous Localization and Mapping</a>&nbsp;

Image: Highway image using LIDAR - Oregon State University (CC BY-SA 2.0)

A brief introduction to localization and mapping, and how these two functions are performed simultaneously during SLAM (Simultaneous Localization and Mapping).

How autonomous robots know where they are

An illustrated deep dive into hybrid electric vehicle's architecture, system requirements, what that means for power semiconductors.

The electrified car's powertrain explained

This article is part of a series about the usage of robots in harsh environments, written by post-doctoral robotics researcher Matthew Nancekievill. Find the second article, on subsea environments,&nbsp;here.<hr>Lets face it, there are some jobs that we just don’t want to do, or CAN’T do that we will have to get robotic platforms to complete. This is especially true of harsh environments such as subsea, mining, space, arctic, often forgotten surgical robotics and nuclear decommissioning. In this article we are going to take a brief look at robotic platforms currently deployed within the nuclear industry, why they’re required and solutions that are still required for all you innovators out there. &nbsp;With decommissioning costing the UK £3billion/yr and estimated costs of £121 billion over the next 120 years, there is a requirement for increased efficiencies and decommissioning rates that robotics can provide.&nbsp;Hopefully this article acts as a summary of robotic solutions in nuclear decommissioning without being too much of an exhaustive list.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNTU5NjY4MDk3MDA3LUltYWdlIDIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib"><h1>A look at the Nuclear Decommissioning Environment</h1>Decommissioning happens around us all the time, old buildings get torn down, coal power plants shutdown and vehicles get scrapped. The nuclear industry is no different with the reactors, storage facilities and processing elements all requiring decommissioning when they reach the end of their useful life.Hazardous substances can be found in all the above environments with asbestos, chemicals used during refinement, rare earth metals and oils that can’t be put into our main waste stream all examples of hazardous substances. The Nuclear industry faces all the same problems but adds one to the list, “nuclear waste”.I’ve always felt that nuclear gets a bad rap in the press, with all the pollutants and oil spills throughout the years created from coal power plants seemingly ignored when nuclear waste is mentioned. However, the effects of nuclear radiation on the body can be very unforgiving.To give you a flavour, researchers working with weak radioactive sources, such as myself, are allowed a total yearly dose of &lt;1 mSv attributed to radiation work, registered radiation workers in the UK &lt;20 mSv. This is a large safety factor to account for medically proven effects of radiation at radiation levels &gt;250 mSv with 1,000 mSv likely to kill you within a few days to a couple of weeks. (Average background radiation dose in the UK is approximately 2.7 mSv)Compare this to radiation doses in some of the most radioactive places on earth, such as the UK’s Sellafield Site or the Fukushima accident, where in the most extreme cases (no really, the most extreme cases, the majority is not like this) dose-rates of around 10,000 mSv.h-1 are possible (check-out <a href="http://irid.or.jp/en/research/20150420-2/" rel="noopener noreferrer" target="_blank">these survey results</a> from the IRID and the shape-changing robot mentioned later.This is before you add in the difficulties of the physically constrained and tight working spaces, high temperatures, contaminated water used for cooling and, in the case of nuclear accidents such as Fukushima, an unknown physical layout “behind the door” due to explosions within the reactor buildings.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNTU5NjcyODIxNzYyLUZ1a3UuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib"><h1>Current Practices</h1>In less radiologically extreme environments, much of the work is carried out by human workers in air-fed suits with multiple layers of gloves, some of which could be lead-lined (lead-lined gloves) worn to avoid contamination and puncturing of the suit on sharp edges. This often means that with all the getting ready, decontamination at the end of the shift and the physical exertion required to operate in these suits, an 8-hour work shift could result in anywhere between 15 minutes to 2 hours of actual decommissioning with power tools cumbersome to use and movement awkward. Workers have been known to empty their boots at the end of their shift of all their sweat that has built up… Workers are, at the root of it, tasked with dismantling the facility. This means cleaning, chopping and moving all sorts of materials into the correct waste stream and then moving on to the next object or structure.<h2>Robotics in Nuclear</h2>It is clear that some of these repetitive tasks or highly radioactive areas lend themselves to the use of robotic platforms. There has been a large body of work conducted on developing solutions for just that. Aquatic robotic platforms for use within submerged nuclear waste storage tanks, robotic manipulators for laser cutting of structures, drone-based devices for site surveys and ground-based robots for automated warehouse characterisation and unknown environments.Examples of these are:The Forth AVEXIS, developed jointly by Forth Engineering and the University of Manchester. A submersible ROV designed to be deployed within the Magnox Swarf Storage Silo and fit through a 6” access port. More information within the video.<iframe src="https://www.youtube.com/embed/OLvAQFz5wh8?&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" allowfullscreen="" class="fr-draggable" style="width: 766px; height: 443px;" frameborder="0" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-7="true" id="626921597" title="Avexis ROV" data-gtm-yt-inspected-29="true" data-gtm-yt-inspected-37="true"></iframe>The LaserSnake2, which combines a multi-jointed “snake-arm” manipulator with a powerful laser-cutter. Developed by OCRobotics, The Welding Institute, National Nuclear Laboratory, Laser Optical Engineering Ltd. And ULO Optics. It is designed to cut and reduce the size of objects and structures to fit in dedicated waste containers.<iframe src="https://www.youtube.com/embed/hqOfRhJsaEQ?&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" allowfullscreen="" class="fr-draggable" style="width: 766px; height: 436px;" frameborder="0" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-7="true" id="439090068" title="LaserSnake2 - Laser cutting for nuclear decommissioning at Sellafield - a world first." data-gtm-yt-inspected-29="true" data-gtm-yt-inspected-37="true"></iframe>Researchers from Bristol University have just recently flown a drone over the Chernobyl Red Forest highlighting the site survey capabilities of drones applicable to all nuclear sites.<iframe src="https://www.youtube.com/embed/_Eto98XTm-4?&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" allowfullscreen="" class="fr-draggable" style="width: 766px; height: 394px;" frameborder="0" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-7="true" id="334628213" title="YouTube video player" data-gtm-yt-inspected-29="true" data-gtm-yt-inspected-37="true"></iframe>Hitachi-GE have also developed a “shape-changing robot” to characterise the internals of Fukushima. Designed to be passed through small gaps in a long cylindrical shape, it is then capable of morphing into a “U” shape for stability. Equipped with a radiation tolerant camera and dosimeter, multiple robots were deployed with valuable data returned.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNTU5NjY4MzQ5NTkyLVNoYXBlLWNoYW5naW5nIElSSUQtSGl0YWNoaSByb2JvdCAtIDQ1MC5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib">However, one of them got stuck, which leads me to my next point.<h1>The problem with Robots</h1>I love robots, and I believe that robotic systems HAVE to and WILL have a huge part to play in harsh environments, but there are still things that have so far left deployment of robotic systems on the fringes of use. These include, but are not limited to:<ul><li>Robots are dumb: When compared to a human assessing a situation. The Hitachi shape-changing robot fell foul of a common problem with robotic systems, it got caught in a hole in the grating it was driving on it wasn’t expecting and that could not be seen by the human operator with the robot not “smart” enough to avoid or raise a flag. Robots are only as smart as we make them!</li><li>Radiation effects: Robots readily survive longer than humans but there are still failures of the electronics in high dose areas.</li><li>Tether entanglement:&nbsp;Most robots (not including drones) are often required to be connected via tether. This is for safety, in-case the robot breaks, meaning it can be unceremoniously dragged out but causes issues with entanglement.</li><li>Robots require a skilled pilot: That skill-set is not common.</li><li>The industry isn’t ready: When things have been done a certain way for a long time, it is difficult to change that. Not to mention all the legal licensing issues and who takes responsibility when something goes wrong?! Media storms surrounding the ethics of robots taking jobs and becoming our future overlords do not help (thanks a lot Terminator).</li></ul><h1>In Conclusion</h1>There is a great body of work being completed within industry and research institutions developing bespoke solutions for specific problems and there have been many successful deployments and also many unsuccessful deployments, which is all part of innovation! Not included above but deserve a mention with regards to innovation and deployments to Brokk and Seabotix robotic systems that are often used daily.To really get consistent robotic platform deployments in nuclear decommissioning, further innovation improving the capabilities of robotic platforms is required as well as upskilling of the workforce and events showcasing the technology to industry. Communication is key and the continued development of systems by all the engineers out there.<hr>This article is part of a series about the usage of robots in harsh environments, written by post-doctoral robotics researcher Matthew Nancekievill. Find the second article, on subsea environments,&nbsp;here.

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