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Category: robotics/AI – Page 185

AI-Driven Robots Are Rewriting The Factory Rulebook

Planning for a future of intelligent robots means thinking about how they might transform your industry, what it means for the future of work, and how it may change the relationship between humans and technology.

Leaders must consider the ethical issues of cognitive manufacturing such as job disruption and displacement, accountability when things go wrong, and the use of surveillance technology when, for example, robots use cameras working alongside humans.

The cognitive industrial revolution, like the industrial revolutions before it, will transform almost every aspect of our world, and change will happen faster and sooner than most expect. Consider for a moment, what will it take for each of us and our organizations to be ready for this future?

Engineers introduce human-like driving technology for autonomous vehicles

Self-driving cars will soon be able to “think” like human drivers under complex traffic environments, thanks to a cognitive encoding framework built by a multidisciplinary research team from the School of Engineering at the Hong Kong University of Science and Technology (HKUST).

This innovation significantly enhances the safety of autonomous vehicles (AVs), reducing overall traffic risk by 26.3% and cutting potential harm to high-risk such as pedestrians and cyclists by an impressive 51.7%. Even the AVs themselves benefited, with their risk levels lowered by 8.3%, paving the way for a new framework to advance the automation of vehicle safety.

Existing AVs have one common limitation: their decision-making systems can only make pairwise risk assessments, failing to holistically consider interactions among multiple road users. This contrasts with a proficient driver who, for example, can skillfully navigate an intersection by prioritizing pedestrian protection while slightly compromising the safety of nearby vehicles. Once pedestrians are confirmed to be safe, the driver can then shift focus to nearby vehicles. Such risk management ability exhibited by humans is known as “social sensitivity.”

‘Optical neural engine’ can solve partial differential equations

Partial differential equations (PDEs) are a class of mathematical problems that represent the interplay of multiple variables, and therefore have predictive power when it comes to complex physical systems. Solving these equations is a perpetual challenge, however, and current computational techniques for doing so are time-consuming and expensive.

Now, research from the University of Utah’s John and Marcia Price College of Engineering is showing a way to speed up this process: encoding those equations in light and feeding them into their newly designed “optical neural engine,” or ONE.

The researchers’ ONE combines diffractive optical neural networks and optical matrix multipliers. Rather than representing PDEs digitally, the researchers represented them optically, with variables represented by the various properties of a light wave, such as its intensity and phase. As a wave passes through the ONE’s series of optical components, those properties gradually shift and change, until they ultimately represent the solution to the given PDE.

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

Coordinated behaviors like swarming—from ant colonies to schools of fish—are found everywhere in nature. Researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have given a nod to nature with a next-generation robot system that’s capable of movement, exploration, transport and cooperation.

A study in Science Advances describing the new soft robotic system was co-led by L. Mahadevan, the Lola England de Valpine Professor of Applied Mathematics, Physics, and Organismic and Evolutionary Biology in SEAS and the Faculty of Arts and Sciences, in collaboration with Professor Ho-Young Kim at Seoul National University. Their work paves new directions for future, low-power swarm robotics.

The new robots, called link-bots, are comprised of centimeter-scale, 3D-printed particles strung into V-shaped chains via notched links and are capable of coordinated, life-like movements without any embedded power or control systems. Each particle’s legs are tilted to allow the bot to self-propel when placed on a uniformly vibrating surface.

Machine learning helps ease the jitters of high-power lasers

Researchers at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have made a breakthrough in laser technology by using machine learning (ML) to help stabilize a high-power laser.

This advancement, spearheaded by Berkeley Lab’s Accelerator Technology & Applied Physics (ATAP) and Engineering Divisions, promises to accelerate progress in physics, medicine, and energy. The researchers report their work in the journal High Power Laser Science and Engineering.

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