AI and ML can be used to conduct Cyber-attacks against Autonomous Cars

Innovative automakers, software developers and tech companies are transforming the automotive industry. Today, drivers enjoy enhanced entertainment, information options and connection with the outer world. As cars move toward more autonomous capabilities, the stakes are increasing in terms of security. As per a report by the UN, Europol and cybersecurity company Trend Micro, cyber-criminals could exploit disruptive technologies, including artificial intelligence (AI) and machine learning (ML) to conduct attacks against autonomous cars, drones and IoT-connected vehicles.

The rapid increase in these technologies inevitably creates a rich target for hackers looking to get access to personal information and control the essential automotive functions and features. The possibility to access information on driver habits for both commercial and criminal purposes, without knowledge and consent, means attitudes towards prevention, understanding and response to potential cyber-attacks require changing.

For instance, stealing personally identifiable information comes into sharper focus when considering virtually all new vehicles on the road today come with embedded, tethered or smartphone mirroring capabilities. Geolocation, personal trip history, and financial details are some examples of personal information that can potentially be stolen through a vehicle’s system using AI and ML.

How Cybercriminals Attack Connected Vehicles

Cybercriminals could conduct attacks abusing machine learning. The technologies are evolving so fast that today autonomous vehicles have ML implemented in them to recognise the environment around them and obstacles like pedestrians must be avoided.

However, these algorithms are still evolving, and hackers could exploit them for malicious purposes, to aid crime or create chaos. For instance, AI systems that manage autonomous vehicles and regular traffic could be manipulated by cybercriminals if they gain access to the networks that control them.

Understanding the threats to connected cars requires knowledge of what cybercriminals are trying to achieve. Hackers will try out different kinds of attacks to achieve unique goals. The most dangerous objective might be to bypass controls in crucial safety systems like steering, brakes and transmission. But cybercriminals might also be interested in obtaining valuable pieces of data that are managed within the car software like personal details and performance statistics. Wherein data can be protected with cryptography, this only shifts the problems from preventing data directly to protecting the cryptographic keys.

If the cybercriminal is trying to steal sensitive data like cryptographic keys, they have to know where to search for them. It usually involves a plethora of reverse-engineering techniques. For instance, the hacker might introduce faults into the compiled code to see how it breaks. Or the individual might look for a string corresponding to an error message related to ‘engine failure’ or ‘anti-lock brake system disabled,’ and trace where that string is used. The individual leverages sophisticated AI techniques to understand the overall structure of the code, where the functions are located.

On the other side, physical access to a device means bad actors can tamper with the application itself. The way this is often done is by making one small change to the application code so it can be bypassed in any number of ways, generally at the assembly language level like inverting the logic of a conditional jump, replacing the test with a tautology or changing function calls to those of the attacker’s own design.

It’s not just road vehicles that cybercriminals could hack by exploiting new technologies such as AI and ML algorithms and increased connectivity; there’s the potential for attackers to abuse machine learning to impact airspace too. Attackers might also consider autonomous drones because they have the potential to carry ‘interesting’ payloads like intellectual property.

Hacking autonomous drones also provide cybercriminals with a potentially easy route to making money by hijacking delivery drones used by retailers and redirecting them to a new location- taking the package and selling it on them.

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UC Berkeley AI researchers say they’ve created AI for autonomous vehicles driving in unseen, real-world landscapes that outperforms leading methods for delivery robots driving on sidewalks. Called LaND, for Learning to Navigate from Disengagements, the navigation system studies disengagement events, then predicts when disengagements will happen in the future. The approach is meant to provide what the researchers call a needed shift in perspective about disengagements for the AI community.

A disengagement describes each instance when an autonomous system encounters challenging conditions and must turn control back over to a human operator. Disengagement events are a contested, and some say outdated, metric for measuring the capabilities of an autonomous vehicle system. AI researchers often treat disengagements as a signal for troubleshooting or debugging navigation systems for delivery robots on sidewalks or autonomous vehicles on roads, but LaND treats disengagements as part of training data.

Doing so, according to engineers from Berkeley AI Research, allows the robot to learn from datasets collected naturally during the testing process. Other systems have learned directly from training data gathered from onboard sensors, but researchers say that can require a lot of labeled data and be expensive.

“Our results demonstrate LaND can successfully learn to navigate in diverse, real world sidewalk environments, outperforming both imitation learning and reinforcement learning approaches,” the paper reads. “Our key insight is that if the robot can successfully learn to execute actions that avoid disengagement, then the robot will successfully perform the desired task. Crucially, unlike conventional reinforcement learning algorithms, which use task-specific reward functions, our approach does not even need to know the task — the task is specified implicitly through the disengagement signal. However, similar to standard reinforcement learning algorithms, our approach continuously improves because our learning algorithm reinforces actions that avoid disengagements.”

LaND utilizes reinforcement learning, but rather than seek a reward, each disengagement event is treated as a way to learn directly from input sensors like a camera while taking into account factors like steering angle and whether autonomy mode was engaged. The researchers detailed LaND in a paper and code published last week on preprint repository arXiv.

The team collected training data to build LaND by driving a Clearpath Jackal robot on the sidewalks of Berkeley. A human safety driver escorted the robot to reset its course or take over driving for a short period if the robot drove into a street, driveway, or other obstacle. In all, nearly 35,000 data points were collected and nearly 2,000 disengagements were produced during the LaND training on Berkeley sidewalks. Delivery robot startup Kiwibot also operates at UC Berkeley and on nearby sidewalks.

Compared with a deep reinforcement learning algorithm (Kendall et al.) and behavioral cloning, a common method of imitation learning, initial experiments showed that LaND traveled longer distances on sidewalks before disengaging.

In future work, authors say LaND can be combined with existing navigation systems, particularly leading imitation learning methods that use data from experts for improved results. Investigating ways to have the robot alert its handlers when it needs human monitoring could lower costs.

In other recent work focused on keeping training costs down for robotic systems, in August a group of UC Berkeley AI researchers created a simple method for training grasping systems that uses a $18 reacher-grabber and GoPro to collect training data for robotic grasping systems. Last year, Berkeley researchers including Pieter Abbeel, a coauthor of LaND research, introduced Blue, a general purpose robot that costs a fraction of existing robot systems.

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When discussing Artificial Intelligence (AI), a common debate is whether AI is an existential threat. The answer requires understanding the technology behind Machine Learning (ML), and recognizing that humans have the tendency to anthropomorphize.  We will explore two different types of AI,  Artificial Narrow Intelligence (ANI) which is available now and is cause for concern, and the threat which is most commonly associated with apocalyptic renditions of AI which is Artificial General Intelligence (AGI).

Artificial Narrow Intelligence Threats

To understand what ANI is you simply need to understand that every single AI application that is currently available is a form of ANI. These are fields of AI which have a narrow field of specialty, for example autonomous vehicles use AI which is designed with the sole purpose of moving a vehicle from point A to B. Another type of ANI might be a chess program which is optimized to play chess, and even if the chess program continuously improves itself by using reinforcement learning, the chess program will never be able to operate an autonomous vehicle.

With its focus on whatever operation it is responsible for, ANI systems are unable to use generalized learning in order to take over the world. That is the good news; the bad news is that with its reliance on a human operator the AI system is susceptible to biased data, human error, or even worse, a rogue human operator.

AI Surveillance

There may be no greater danger to humanity than humans using AI to invade privacy, and in some cases using AI surveillance to completely prevent people from moving freely.  China, Russia, and other nations passed through regulations during COVID-19 to enable them to monitor and control the movement of their respective populations. These are laws which once in place, are difficult to remove, especially in societies that feature autocratic leaders.

In China, cameras are stationed outside of people’s homes, and in some cases inside the person’s home. Each time a member of the household leaves, an AI monitors the time of arrival and departure, and if necessary alerts the authorities. As if that was not sufficient, with the assistance of facial recognition technology, China is able to track the movement of each person every time they are identified by a camera. This offers absolute power to the entity controlling the AI, and absolutely zero recourse to its citizens.

Why this scenario is dangerous, is that corrupt governments can carefully monitor the movements of journalists, political opponents, or anyone who dares to question the authority of the government. It is easy to understand how journalists and citizens would be cautious to criticize governments when every movement is being monitored.

There are fortunately many cities that are fighting to prevent facial recognition from infiltrating their cities. Notably, Portland, Oregon has recently passed a law that blocks facial recognition from being used unnecessarily in the city. While these changes in regulation may have gone unnoticed by the general public, in the future these regulations could be the difference between cities that offer some type of autonomy and freedom, and cities that feel oppressive.

Autonomous Weapons and Drones

Over 4500 AI researches have been calling for a ban on autonomous weapons and have created the Ban Lethal Autonomous Weapons website. The group has many notable non-profits as signatories such as Human Rights Watch, Amnesty International, and the The Future of Life Institute which in itself has a stellar scientific advisory board including Elon Musk, Nick Bostrom, and Stuart Russell.

Before continuing I will share this quote from The Future of Life Institute which best explains why there is clear cause for concern: “In contrast to semi-autonomous weapons that require human oversight to ensure that each target is validated as ethically and legally legitimate, such fully autonomous weapons select and engage targets without human intervention, representing complete automation of lethal harm. ”

Currently, smart bombs are deployed with a target selected by a human, and the bomb then uses AI to plot a course and to land on its target. The problem is what happens when we decide to completely remove the human from the equation?

When an AI chooses what humans need targeting, as well as the type of collateral damage which is deemed acceptable we may have crossed a point of no return. This is why so many AI researchers are opposed to researching anything that is remotely related to autonomous weapons.

There are multiple problems with simply attempting to block autonomous weapons research. The first problem is even if advanced nations such as Canada, the USA, and most of Europe choose to agree to the ban, it doesn’t mean rogue nations such as China, North Korea, Iran, and Russia will play along. The second and bigger problem is that AI research and applications that are designed for use in one field, may be used in a completely unrelated field.

For example, computer vision continuously improves and is important for developing autonomous vehicles, precision medicine, and other important use cases. It is also fundamentally important for regular drones or drones which could be modified to become autonomous.  One potential use case of advanced drone technology is developing drones that can monitor and fight forest fires. This would completely remove firefighters from harms way. In order to do this, you would need to build drones that are able to fly into harms way, to navigate in low or zero visibility, and are able to drop water with impeccable precision. It is not a far stretch to then use this identical technology in an autonomous drone that is designed to selectively target humans.

It is a dangerous predicament and at this point in time, no one fully understands the implications of advancing or attempting to block the development of autonomous weapons. It is nonetheless something that we need to keep our eyes on, enhancing whistle blower protection may enable those in the field to report abuses.

Rogue operator aside, what happens if AI bias creeps into AI technology that is designed to be an autonomous weapon?

AI Bias

One of the most unreported threats of AI is AI bias. This is simple to understand as most of it is unintentional. AI bias slips in when an AI reviews data that is fed to it by humans, using pattern recognition from the data that was fed to the AI, the AI incorrectly reaches conclusions which may have negative repercussions on society. For example, an AI that is fed literature from the past century on how to identify medical personnel may reach the unwanted sexist conclusion that women are always nurses, and men are always doctors.

A more dangerous scenario is when AI that is used to sentence convicted criminals is biased towards giving longer prison sentences to minorities. The AI’s criminal risk assessment algorithms are simply studying patterns in the data that has been fed into the system. This data indicates that historically certain minorities are more likely to re-offend, even when this is due to poor datasets which may be influenced by police racial profiling. The biased AI then reinforces negative human policies. This is why AI should be a guideline, never judge and jury.

Returning to autonomous weapons, if we have an AI which is biased against certain ethnic groups, it could choose to target certain individuals based on biased data, and it could go so far as ensuring that any type of collateral damage impacts certain demographics less than others. For example, when targeting a terrorist, before attacking it could wait until the terrorist is surrounded by those who follow the Muslim faith instead of Christians.

Fortunately, it has been proven that AI that is designed with diverse teams are less prone to bias. This is reason enough for enterprises to attempt when at all possible to hire a diverse well-rounded team.

Artificial General Intelligence Threats

It should be stated that while AI is advancing at an exponential pace, we have still not achieved AGI. When we will reach AGI is up for debate, and everyone has a different answer as to a timeline. I personally subscribe to the views of Ray Kurzweil, inventor, futurist, and author of ‘The Singularity is Near” who believes that we will have achieved AGI by 2029.

AGI will be the most transformational technology in the world. Within weeks of AI achieving human-level intelligence, it will then reach superintelligence which is defined as intelligence that far surpasses that of a human.

With this level of intelligence an AGI could quickly absorb all human knowledge and use pattern recognition to identify biomarkers that cause health issues, and then treat those conditions by using data science. It could create nanobots that enter the bloodstream to target cancer cells or other attack vectors. The list of accomplishments an AGI is capable of is infinite. We’ve previously explored some of the benefits of AGI.

The problem is that humans may no longer be able to control the AI. Elon Musk describes it this way: ”With artificial intelligence we are summoning the demon.’ Will we be able to control this demon is the question?

Achieving AGI may simply be impossible until an AI leaves a simulation setting to truly interact in our open-ended world. Self-awareness cannot be designed, instead it is believed that an emergent consciousness is likely to evolve when an AI has a robotic body featuring multiple input streams. These inputs may include tactile stimulation, voice recognition with enhanced natural language understanding, and augmented computer vision.

The advanced AI may be programmed with altruistic motives and want to save the planet. Unfortunately, the AI may use data science, or even a decision tree to arrive at unwanted faulty logic, such as assessing that it is necessary to sterilize humans,  or eliminate some of the human population in order to control human overpopulation.

Careful thought and deliberation needs to be explored when building an AI with intelligence that will far surpasses that of a human. There have been many nightmare scenarios which have been explored.

Professor Nick Bostrom in the Paperclip Maximizer argument has argued that a misconfigured AGI if instructed to produce paperclips would simply consume all of earths resources to produce these paperclips. While this seems a little far fetched,  a more pragmatic viewpoint is that an AGI could be controlled by a rogue state or a corporation with poor ethics. This entity could train the AGI to maximize profits, and in this case with poor programming and zero remorse it could choose to bankrupt competitors, destroy supply chains, hack the stock market, liquidate bank accounts, or attack political opponents.

This is when we need to remember that humans tend to anthropomorphize. We cannot give the AI human-type emotions, wants, or desires. While there are diabolical humans who kill for pleasure, there is no reason to believe that an AI would be susceptible to this type of behavior. It is inconceivable for humans to even consider how an AI would view the world.

Instead what we need to do is teach AI to always be deferential to a human. The AI should always have a human confirm any changes in settings, and there should always be a fail-safe mechanism. Then again, it has been argued that AI will simply replicate itself in the cloud, and by the time we realize it is self-aware it may be too late.

This is why it is so important to open source as much AI as possible and to have rational discussions regarding these issues.

Summary

There are many challenges to AI, fortunately, we still have many years to collectively figure out the future path that we want AGI to take. We should in the short-term focus on creating a diverse AI workforce, that includes as many women as men, and as many ethnic groups with diverse points of view as possible.

We should also create whistleblower protections for researchers that are working on AI, and we should pass laws and regulations which prevent widespread abuse of state or company-wide surveillance. Humans have a once in a lifetime opportunity  to improve the human condition with the assistance of AI, we just need to ensure that we carefully create a societal framework that best enables the positives, while mitigating the negatives which include existential threats.

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When some people hear the term ‘artificial intelligence’ their initial reaction is to imagine a dystopian future where robots have risen up and overthrown humanity. The truth is, application of AI technology in our day-to-day lives is a lot less sinister. It might not be long before these technologies become common in our everyday lives.

It’s currently assisting with medical diagnosis, the creation of autonomous cars and to help improve businesses by analysing data and creating accurate forecasts of client or market behaviour. The application of AI is becoming more and more popular in businesses worldwide, with the potential to improve our lives in unexpected ways.

A Helping Hand

You may not have realised it, but AI technology could already be playing a role in your life. If you happen to use one of the various virtual assistants currently available on the market, like Amazon’s Alexa, Apple’s Siri or Google Assistant, you are directly engaging with AI. These assistants are able to process data fed to them by their users, and use machine learning to tailor responses that fit their owner’s needs.

In 2019, OpenAI trained a pair of neural networks to solve a Rubik’s Cube with a human-like robot hand. This was achieved by teaching the AI through reinforcement learning, exposing it to a series of randomised simulations. On the surface, solving a Rubik’s Cube may seem like a weird application of AI, but the development and results from this technology can help with the creation of robots that have human-level dexterity.

According to IBM Developer Advocate Steve Coochin, AI systems – such as IBM’s Watson – have the potential to be applied to “virtually any scenario, industry or organisation to streamline processes, increase effectiveness, and accelerate progress.”

“One example is Prometeo, from our Call for Code hackathon,” Coochin explains, “an AI solution which uses machine learning to monitor and support firefighter health and safety in real-time while they are putting out fires.”

“Using Watson Machine Learning predicative model, the technology can reply in real time with a green, yellow, or red firefighter status to the fire station.”

AI-Assisted Agriculture

Artificial intelligence technology is currently being used to help keep Australia’s bee industry alive through biosecurity. Bega’s recent Purple Hive Project is designed to help stop entire hives from being wiped out by using AI technology to detect whether bees are carrying the Varroa mite. This tiny parasite attaches itself to unsuspecting bees, feeding on their blood and can spread viruses that can devastate entire colonies.

These bright purple hives are equipped with 360-degree cameras that scan each bee that enters. Using AI technology, it can detect whether or not a bee is carrying the Varroa mite. If a bee is a confirmed carrier, the hive will send an automatic alert to the beekeepers so they can quarantine the hive.

By making sure Australia’s bee population remains healthy, this AI technology is also helping to keep our local agriculture industry alive as, according to the CSIRO, “one in every three mouthfuls of food that we consume comes to us through the aid of pollination by honeybees.”

AI has the great potential to help improve pre-existing software and information technologies, but there’s also the possibility of it being used for entertainment purposes, too. One of the more popular, recent examples of this would be AI Dungeon. It’s a free text-based adventure game that creates unique stories by auto-generating responses based on what the player types.

AI Dungeon uses a language model known as Generative Pre-trained Transformer (GPT-3), which allows for deep learning to produce human-like text. Depending on which genre setting you choose at the start of the game, AI Dungeon will procedurally generate a story based on previous learnings and your inputs, which, for the most part, will read as a coherent story.

One of the stranger applications of it is the website DeepArt, which creates AI-assisted art. This platform uses an algorithm to pull stylistic elements from a chosen image and apply it to another. For example, you can upload a normal photo of your bedroom and use DeepArt’s algorithm to make it look like Vincent van Gogh’s ‘The Starry Night’ or Paul Cézanne’s ‘The Large Bathers’.

Get Involved in the Future of AI

These various applications of AI technology may sound interesting, but how can you become a part of this expanding frontier? Enrolling in one of Billy Blue College of Design at Torrens University Australia’s software engineering or digital transformation-based courses, such as a Bachelor Of Software Engineering (Artificial Intelligence) or Graduate Certificate of Digital Transformation and Creative Intelligence, will let you begin your adventure into creative technology, opening you up to the world of AI development.

To make sure you get the most from these courses, they’ve been developed in collaboration with IBM, who are currently leading the pack when it comes to AI. According to the IDC Market Share, IBM has been ranked the market share leader for the third year running.

IBM have become the frontrunners in the world of artificial intelligence for a few reasons. Firstly, the sturdy information architecture of their Cloud Pak for Data helps businesses by using automated AI to collect, organise and analyse data, making it easy to manage and access. They’ve also aimed for transparency when it comes to the development of AI technologies. Their Watson OpenScale platform allows business’ to measure and track the deployment of AI technology to meet set outcomes, allowing for a clearer explanation of how they work.

Billy Blue will teach you important skills used in AI development, such as design thinking, creative intelligence and human centred design, as well as giving you the tools to learn about topics like computer vision, natural language processing and machine learning. Through their collaboration with IBM, they hope to put this technology into your hands. With these tools — along with your imagination — you’ll be able to discover the vast possibilities within the field of AI technology.

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Molecules are the building blocks of everyday life. Many materials are composed of them, a little like a LEGO model consists of a multitude of different bricks. But while individual LEGO bricks can be simply shifted or removed, this is not so easy in the nanoworld. Atoms and molecules behave in a completely different way to macroscopic objects and each brick requires its own “instruction manual”. Scientists from Jülich and Berlin have now developed an artificial intelligence system that autonomously learns how to grip and move individual molecules using a scanning tunnelling microscope. The method, which has been published in Science Advances, is not only relevant for research but also for novel production technologies such as molecular 3D printing.

Rapid prototyping, the fast and cost-effective production of prototypes or models – better known as 3D printing – has long since established itself as an important tool for industry. “If this concept could be transferred to the nanoscale to allow individual molecules to be specifically put together or separated again just like LEGO bricks, the possibilities would be almost endless, given that there are around 1060 conceivable types of molecule,” explains Dr. Christian Wagner, head of the ERC working group on molecular manipulation at Forschungszentrum Jülich.

There is one problem, however. Although the scanning tunnelling microscope is a useful tool for shifting individual molecules back and forth, a special custom “recipe” is always required in order to guide the tip of the microscope to arrange molecules spatially in a targeted manner. This recipe can neither be calculated, nor deduced by intuition – the mechanics on the nanoscale are simply too variable and complex. After all, the tip of the microscope is ultimately not a flexible gripper, but rather a rigid cone. The molecules merely adhere lightly to the microscope tip and can only be put in the right place through sophisticated movement patterns.

“To date, such targeted movement of molecules has only been possible by hand, through trial and error. But with the help of a self-learning, autonomous software control system, we have now succeeded for the first time in finding a solution for this diversity and variability on the nanoscale, and in automating this process,” says a delighted Prof. Dr. Stefan Tautz, head of Jülich’s Quantum Nanoscience institute.

The key to this development lies in so-called reinforcement learning, a special variant of machine learning. “We do not prescribe a solution pathway for the software agent, but rather reward success and penalize failure,” explains Prof. Dr. Klaus-Robert Müller, head of the Machine Learning department at TU Berlin. The algorithm repeatedly tries to solve the task at hand and learns from its experiences. The general public first became aware of reinforcement learning a few years ago through AlphaGo Zero. This artificial intelligence system autonomously developed strategies for winning the highly complex game of Go without studying human players – and after just a few days, it was able to beat professional Go players.

“In our case, the agent was given the task of removing individual molecules from a layer in which they are held by a complex network of chemical bonds. To be precise, these were perylene molecules, such as those used in dyes and organic light-emitting diodes,” explains Dr. Christian Wagner. The special challenge here is that the force required to move them must never exceed the strength of the bond with which the tip of the scanning tunnelling microscope attracts the molecule, since this bond would otherwise break. “The microscope tip therefore has to execute a special movement pattern, which we previously had to discover by hand, quite literally,” Wagner adds. While the software agent initially performs completely random movement actions that break the bond between the tip of the microscope and the molecule, over time it develops rules as to which movement is the most promising for success in which situation and therefore gets better with each cycle.

However, the use of reinforcement learning in the nanoscopic range brings with it additional challenges. The metal atoms that make up the tip of the scanning tunnelling microscope can end up shifting slightly, which alters the bond strength to the molecule each time. “Every new attempt makes the risk of a change and thus the breakage of the bond between tip and molecule greater. The software agent is therefore forced to learn particularly quickly, since its experiences can become obsolete at any time,” Prof. Dr. Stefan Tautz explains. “It’s a little as if the road network, traffic laws, bodywork, and rules for operating the vehicle are constantly changing while driving autonomously.” The researchers have overcome this challenge by making the software learn a simple model of the environment in which the manipulation takes place in parallel with the initial cycles. The agent then simultaneously trains both in reality and in its own model, which has the effect of significantly accelerating the learning process.

“This is the first time ever that we have succeeded in bringing together artificial intelligence and nanotechnology,” emphasizes Klaus-Robert Müller. “Up until now, this has only been a ‘proof of principle’,” Tautz adds. “However, we are confident that our work will pave the way for the robot-assisted automated construction of functional supramolecular structures, such as molecular transistors, memory cells, or qubits – with a speed, precision, and reliability far in excess of what is currently possible.”

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The 100-year-old locksmith’s company partners with Balyo to offer robotic solutions such as autonomous forklift trucks for the material handling industry.

Yale Europe Materials Handling says it now offers “sophisticated robotic solutions” which can adjust to changes in their surroundings.

All trucks in the Yale robotics range can be linked to a warehouse management system and feature a touchscreen interface to give instructions to the robot.

Yale adds that its robotic solutions also offer manual operation at the touch of a button.

Working with the robots

Robotics and humans working together, the workforce of the future – or is it? For the materials handling industry, this is already possible.

Ron Farr, warehouse solutions manager for Yale, says: “Many people might believe robots in logistics are in their infancy.

“At Yale, we’ve implemented the technology and software in order to offer sophisticated robotic solutions that can adjust to changes in their surroundings, for greater flexibility than solutions that require dedicated navigation infrastructure.”

How the robot interacts with its environment

The Yale robotics MC-10-15 counterbalance stacker can interact with and access pallets at height – for example, on conveyor belts or second or third shelves up to a height of 1.8 meters.

The front laser allows the robot to sense the pallet, and the barcode scanner identifies the correct pallet to fulfil the instruction.

Farr says: “The Yale robots, driven by Balyo geoguidance technology, are fitted with advanced obstacle detection technology which enables it to react to the situation.

“Once the robot has detected something ahead, it can control its speed in a smooth and efficient movement to minimise stops and shocks, slowing down to a complete stop if needed.”

An additional rear scanner is installed for instances when the counterbalance stacker is travelling in the forks-forward direction. A curtain laser scans for additional above-ground obstacles, while side lasers provide a full 360 degree coverage at all times.

Farr says: “The robot also informs those working in the environment of its status, emitting an audio warning and projecting a blue LED spotlight on to the ground when it is in motion.

“A light mounted at eye-level flashes if the truck is about to turn, which flashes more rapidly while the truck is completing a turn.”

Ease of communication

All trucks in the Yale robotics range, which includes the MO50-70T robotic tow tractor and MO10-25 low level order picker, feature a touchscreen interface to give instructions to the robot, and can all be switched to manual mode at the touch of a button to complete tasks outside of the truck’s pre-programmed parameters.

Logistics operators can interact with the robot with real-time truck management. The software can integrate with existing Enterprise Resource Planning (ERP) and Warehouse Management Systems (WMS).

Farr says: “The software can be used to assign tasks to individual trucks and is used to control the flow of traffic.

“The robotic solutions can be linked to other equipment in the warehouse, for example the conveyers can call the robot to remove a product. Fire alarms can tell the truck to stop in a safe area that does not hinder pedestrians exiting.

“Warehouse Managers can schedule the charging of the robots, which enables the use of cheaper night time rates.

“The trucks can go on charge on rotation, rather than all the robots arriving at lunchtime. This can help with the running costs of the vehicle, as well as maintenance, as it’s all very predictable.”

Reduced training time

Training new employees can be time-consuming, but by integrating robotics solutions into applications companies can reduce the time it takes new‑hires to get up to speed.

Adopting automated solutions can help applications simplify tasks reserved for employees and foster a collaborative environment. In goods-to-operator fulfilment workflows for example, employees can focus on picking and packing orders as quickly as possible from inventory, brought to them by a robotic solution.

The Yale MO50-70T robotic tow tractor offers horizontal transportation over short and long distances, and brings individual items together as one unit to employees that require them.

Interconnectivity expands to the wider infrastructure too – sensors on conveyor belts can be used to detect pallets at the end of the line and call for the robotic solution to collect the pallet to transport it to its next location.

Robotics trucks are ideal for performing repetitive tasks such as movement of pallets in the warehouse environment and loading and unloading.

The MO25 low level order picker offers cost efficient transfer and a regular, sustained constant flow, taking care of stock replenishment and transporting goods.

Farr says: “Robotics can liberate employees to conduct tasks that humans do best.

“Having robots working alongside humans leverages the strengths of both to make repetitive tasks and more complex, value-added functions more efficient.

“It can also provide new opportunities for people with physical limitations to serve as integral parts of the process, as robots can move inventory to pickers and help keep the operation flowing.

“Autonomous solutions drive proven cost savings by increasing labour efficiency, reducing turnover, extending asset life and increasing throughput. What really solidifies them as a smart investment is their flexibility.

“It enables practical accommodation for manual intervention, minimises ongoing costs in the event of minor layout adjustments and the need to supplement future initiatives such as Industry 4.0.”

Yale says that, working in unison, the company’s “sophisticated robotics solutions are ready to be deployed in appropriate applications, working in harmony with humans to drive productivity”.

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Locus Robotics, the market leader in autonomous mobile robots (AMR) for fulfillment warehouses, today announced a strategic partnership with Balloon One, a London-based provider of software and supply chain applications for distribution, manufacturing and e-commerce companies. Together, Balloon One and Locus Robotics will provide customers with a more efficient, cost-effective solution to meet the dramatically increasing demand for e-commerce fulfillment, further driving the adoption of the innovative warehouse technologies offered by both companies.

“As e-commerce continues to explode across all channels, warehouse fulfillment has become a critical part of the economy,” said Rick Faulk, CEO of Locus Robotics. “Our partnership will deliver cutting-edge robotics technology to Balloon One customers and drive significant operational efficiency and productivity gains, and a faster time to value.”

Through the partnership, Balloon One will offer Locus Robotics’ award-winning, multi-bot solution for warehouse fulfillment alongside Körber/HighJump WMS, enabling customers to achieve consistent efficiency gains of 200-300% without the need for expensive or time-consuming infrastructure changes. In addition, the Locus Robotics-as-a-Service (RaaS) model ensures that Balloon One customers can address the challenges of the labor market at a very low start-up cost.

“Balloon One is pleased to announce an exciting new partnership with the industry’s most technologically advanced autonomous mobile robot (AMR) provider, Locus Robotics,” said Craig Powell, Managing Director, Balloon One. “The Locus system can be deployed in as little as four (4) weeks and provides two to three (2X-3X) times picker productivity gains. Based on our internal assessment, we believe this technology will become an essential part of our warehouse operations and will provide our customers with a unique and significant advantage in today’s increasingly demanding e-commerce landscape.”

The COVID-19 pandemic has quickly transformed the retail industry, making online and omnichannel purchasing the new normal across the globe. Locus Robotics’ industry-leading robotics fulfillment solution enables brands, retailers, and third-party logistics (3PL) operators to easily meet higher order volumes and increasing consumer demand for e-commerce, retail, omnichannel, and manufacturing order fulfillment. Locus’s proven, multi-bot solution for fulfillment incorporates collaborative, autonomous robots that work closely with human employees to improve fulfillment productivity and efficiency – consistently doubling or tripling fulfillment productivity, lowers labor costs, with near-100% accuracy, while also enabling users to save 30% or more in operating expenses.

Balloon One will be offering live, in-person demonstrations of the Locus Solution to prospective customers at their new demonstration suite in West London. Demos will provide a hands-on experience to showcase the value of the fully integrated Locus and Körber/HighJump solutions.

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Florida Atlantic University recently announced that the Dorothy F. Schmidt College of Arts and Letters, in collaboration with FAU’s Brain Institute, hired Susan Schneider, Ph.D., as William F. Dietrich Chair in Philosophy.

Dr. Schneider will bring her experience in analyzing the moral and ethical issues related to artificial intelligence and machine consciousness to FAU’s research in AI, brain and mind.

“Dr. Schneider’s work on the future of human intelligence is leading international conversations on human cognition, the future of the mind, and its relationship to the ever-growing presence of artificial intelligence in our society,” Michael J. Horswell, dean of the Dorothy F. Schmidt College of Arts and Letters, said in a statement. “Her research reminds us of how vital philosophy is to scientific advancements, and how the humanities must be in dialogue with science and engineering as we imagine and create the future.”

Dr. Schneider comes to FAU from the University of Connecticut, where she was a professor of philosophy and cognitive science and the director of the AI, Mind and Society Research Group. She also is the NASA-Baruch S. Blumburg Chair and a Distinguished Scholar Chair at the Library of Congress.

Her most recent book (2019), “Artificial You, AI and the Future of Your Mind,” considers the possibilities, definitions and moral/ethical issues related to machine consciousness.

“Dr. Schneider’s scholarship, and passion for student and community engagement, will accelerate FAU’s rise as a premier center for research and education into the mind’s origins and future,” Randy Blakely, Ph.D., director of FAU’s Brain Institute, said in the statement.

Dr. Schneider’s work in artificial intelligence has taken her to Washington, D.C., to testify before Congress on topics like data privacy, technological unemployment, autonomous weapons and more.

She also is a recipient of the National Endowment for the Humanities Public Scholar Award.

“I’m eager to help facilitate interdisciplinary connections between AI, neuroscience, philosophy and psychology,” Dr. Schneider said in the statement. “I’m currently writing a book about the future of intelligent systems (including human intelligence augmentation), so my interests range from deep learning systems to the octopus. I can’t think of a better place to engage with scholars on these issues.” ¦

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Robots have been deployed in manufacturing to fill several rule-based operations. Fully autonomous robots in manufacturing are utilized for high-volume, repetitive processes work which demands speed and accuracy for the process of lifting, holding and moving heavy pieces. Manufacturing robots automate rule-based repetitive tasks, enable a human workforce to shift its focus on more productive and critical areas of operations to reduce error margins to negligible rates.

Here are the manufacturing robots and cobots (collaborative robots that work alongside human beings) to steadfast Industry 4.0-

Articulated Robots

Articulated robots range from the simple two-jointed structures to systems with 10 or more interacting joints and materials. They are powered by a variety of means, including electric motors and are utilised for pick and place, dispensing, packaging, assembling and welding activities. Their multiple points of rotation with some devices give them as high as seven degrees of freedom. Articulated robots can move around obstacles that may block other types of robots and are most commonly used in assembling factories.

Delta Robots

Delta robots also known as parallel or spider robots have force and collision detection sensors, and uses the force sensors for intricate assembly applications. The high-speed delta robots are deployed in the packaging industry, medical and pharmaceutical industry. For its stiffness delta robots also used for surgery, high precision assembly operations for electronic components and 3D printing.

Cartesian Robots

The use of Cartesian or six-axis robots, in particular, is gaining prominence credit to its standardized components, and operator-friendly controls that lower cost and boost performance. Cartesian robots, also called gantry robots, are mechatronic devices that use motors and linear actuators to position a tool.

Cartesian robots can be used for pick-and-place, assembly, and even dispensation of materials such as adhesive. Cartesian-robot movements stay within the framework’s confines, but the framework can be mounted horizontally or vertically, or even overhead in certain gantry configurations.

COBOTs (Collaborative Robots)

The International Federation of Robotics (IFR), collaborative industrial robots (COBOTS) defines cobots to be designed to perform collaborative tasks with humans in industrial sectors in four categories, including in cases where human and robot work from different physical workspaces without any human-robot contact or synchronization.

Sequential cobot collaboration occurs when there is an intersection between the humans and the robot’s workspace.Cobot cooperation occurs when humans and robots work on the same part at the same time, while in responsive cobot cooperation, the robot responds in real-time to the human’s movements.

SCARA Robots

SCARA is an acronym for Selective Compliance Articulated Robot Arm, designed to handle a variety of material handling operations. SCARA was invented in 1978 by Professor Hiroshi Makino at Yamanashi University in Japan. SCARA robots were designed for assembly applications and they have been used in industrial assembly lines since 1981.

Due to their selective compliance, SCARAs are less rigid than Cartesian or gantry robots. However, they are more rigid than both 6-axis robots and Delta robots due to their rigid Z-axis. They are generally faster than 6-axis robots. The payload of SCARAs is generally quite low, but it is more than Delta robots which can lift between 0.3-8 kg. SCARAs are very well suited to high-speed assembly applications.

Robotics in the Future

With rapid advancements in artificial intelligence, machine learning and augmented reality the next generation of robots powered by AI and automation will usher a disruptive innovation of sorts. Research estimates forecast that by 2050, drones will be commonplace in homes, helping with daily chores such as cleaning and gaming.

Are we ready to embrace a scenario where robots will do most of the rule-based manual tasks not just in factories nut in our homes commanding equal rights just like we have?

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Autonomous vehicles are developed with a wide range of self-driving capabilities. Some vehicles provide basic automation, like cruise control and blind-spot detection, while other vehicles are reaching fully-autonomous capabilities. Many of these capabilities are being made possible by AI technology. 

However, before talking about big scale deployments for smart city transportation, more work is needed to improve the AI algorithms and mapping features powering autonomous vehicles. This article reviews innovations in Autonomous Vehicle AI and mapping, which might help secure a future in which autonomous vehicles are deployed city-wide.

1. Deep Reinforcement Learning (DRL)

Multiple types of machine learning are being applied to the development of autonomous vehicles, including DRL. This method combines the strategies of deep learning and reinforcement learning in an attempt to better automate the training of algorithms. 

When implementing DRL, researchers use reward functions to guide software-defined agents toward a specific goal. Throughout training, these agents learn either how to attain that goal or how to maximize the reward over subsequent steps. 

With the help of data collected from current autonomous vehicles, human drivers, and manufacturers, these agents can eventually be trained to operate independently. In the meantime, DRL has useful applications in lower level automation of vehicles. It can also provide value in vehicle manufacturing, where it can be applied to transform factory automation and vehicle maintenance.

2. Path Planning

Path planning is the decision-making process that autonomous vehicles use to determine safe, convenient, and economical routes. It requires taking into account street configurations, static and dynamic obstacles, and changing conditions. Currently, path planning is based on the combination of behavior-based models, feasible models, and predictive control models. 

The process occurs roughly as follows:

• The route planning mechanism determines a route from point A to B according to available roads or lanes.
• A behavioral layer is then applied to determine vehicle movement according to environmental variables, such as traffic or weather conditions.
• These determinations are applied to feasible and predictive control models to guide the operation of the vehicle.
• As the trip progresses, feedback from sensors and analyses is fed to these components so adjustments can be made in real-time to adjust for errors or unforeseen events.

In the above process, the relatively easy part is predicting how the vehicle itself will behave under certain conditions. What is more challenging is predicting what might happen in the environment the vehicle is operating in. For example, how can models predict when neighbor vehicles will swerve or pedestrians will enter the street. 

To improve these predictions, researchers are applying multi-model algorithms to simulate possible trajectories and speeds of objects. These models enable the autonomous system to prepare for multiple scenarios simultaneously. Then based on evaluated probabilities of each scenario occurring, the system can define how the vehicle responds.

3. SLAM

Simultaneous localization and mapping (SLAM) is a technology used to orient vehicles in real-time to the surroundings. While still in its early stages, eventually, this technology can enable vehicles to operate autonomously in areas where maps are not available or where available maps are incorrect. 

What makes this technology so challenging to implement is that currently, mapping is based on first knowing an object’s orientation. However, orientation is typically determined by comparing sensor data to pre-existing maps of surroundings. This dual reliance makes it difficult to achieve either goal when landmark information is unknown.

One of the ways this problem is overcome is by incorporating a rough map, based on GPS data, which is then refined as a vehicle moves through an environment. This requires vehicle sensors that constantly measure the environment and apply careful calculations to correct for vehicle movement and sensor accuracy.

An example of SLAM applications can be seen in Google’s autonomous vehicle used for generating Google Maps data. This vehicle uses a laser radar (LIDAR) assembly that is attached to the roof to measure its surroundings. 

Measurements are taken at up to 10 times a second depending on how fast the vehicle is moving. The data collected is then passed through an array of statistical models, including Bayesian filters and Monte Carlo simulations to accurately improve existing maps.

4. HD Maps

High-definition (HD) maps are maps that include minute environmental details, often down to a centimeter scale. These maps include the details that live drivers would be able to see and interpret in real-time while driving but which autonomous vehicles need ahead of time. For example, lane markings, curve angles, road boundaries, or pavement gradients. 

The level of detail provided by HD maps helps autonomous vehicles more accurately predict behavior and enables more accurate direction. This doesn’t eliminate the need to evaluate environmental changes in real-time. However, it does lighten the load of how thoroughly sensor data must be processed and analyzed. 

Conclusion

AI algorithms are just one part of the components needed to power fully autonomous vehicles. Growth is also driven by the integration of higher quality data. For example, data collected from advanced sensors or derived from more accurate maps. While deep learning models have greatly contributed to the improvement of autonomous vehicle AI, there are still many challenges these vehicles face, and which should be dealt with before true maturity can be achieved.

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