Archive 09.06.2023

A team of material scientists and electronic engineers at MIT, has developed a way to create magnetic soft robots by combining fiber-based fabrication systems with mechanical and magnetic programming methods to provide locomotion under unidirectional magnetic fields. In their paper published in the journal Advanced Materials, the group describes how they overcame problems faced by others attempting to create magnetically controlled soft robots and outline the design of the robots they created.

In recent years, roboticists have created a growing number of autonomous systems based on different structures and designs. Among these are modular robots, which are composed of different elements or "modules" that can be reconfigured to carry out specific tasks more effectively.

Different people tend to have unique needs and preferences—particularly when it comes to cleaning or tidying up. Home robots, especially robots designed to help humans with house chores, should ideally be able to complete tasks in ways that account for these individual preferences.

One application for which robot arms are well suited is using a kind of deburring tool as a cutting instrument when working with relatively soft materials like plastics and composites.

Poems, essays and even books—is there anything the open AI platform ChatGPT can't handle? These new AI developments have inspired researchers at TU Delft and the Swiss technical university EPFL to dig a little deeper: For instance, can ChatGPT also design a robot? And is this a good thing for the design process, or are there risks? The researchers published their findings in Nature Machine Intelligence.

This easy-to-make sponge-jamming device can help stiff robots handle delicate items carefully by mimicking the nuanced touch, or variable stiffness, of a human.

Robots can skip, jump and do somersaults, but they’re too rigid to hold an egg easily. Variable-stiffness devices are potential solutions for contact compliance on hard robots to reduce damage, or for improving the load capacity of soft robots.

This study, published at the IEEE International Conference on Robotics and Automation (ICRA) 2023, shows that variable stiffness can be achieved by a silicone sponge.

Lead author Tianqi Yue from Bristol’s Department of Engineering Mathematics explained: “Stiffness, also known as softness, is important in contact scenarios.

“Robotic arms are too rigid so they cannot make such a soft human-like grasp on delicate objects, for example, an egg.

“What makes humans different from robotic arms is that we have soft tissues enclosing rigid bones, which act as a natural mitigating mechanism.

“In this paper, we managed to develop a soft device with variable stiffness, to be mounted on the end robotic arm for making the robot-object contact safe.”

Robot sponge in action. Video Credit: Tianqi Yue.

Silicone sponge is a cheap and easy-to-fabricate material. It is a porous elastomer just like the cleaning sponge used in everyday tasks.

By squeezing the sponge, the sponge stiffens which is why it can be transformed into a variable-stiffness device.

This device could be used in industrial robots in scenarios including gripping jellies, eggs and other fragile substances. It can also be used in service robots to make human-robot interaction safer.

Mr Yue added: “We managed to use a sponge to make a cheap and nimble but effective device that can help robots achieve soft contact with objects. The great potential comes from its low cost and light weight.

“We believe this silicone-sponge based variable-stiffness device will provide a novel solution in industry and healthcare, for example, tunable-stiffness requirement on robotic polishing and ultrasound imaging.”

The team will now look at making the device achieve variable stiffness in multiple directions, including rotation.

Paper: “A Silicone-sponge-based Variable-stiffness Device” by Tianqi Yue at the IEEE International Conference on Robotics and Automation (ICRA) 2023.

New algorithms will transform the foundations of computing

A simple sponge has improved how robots grasp, scientists from the University of Bristol have found.   

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If the OEM uses well-designed, proven components, the mobile platform will provide many years of service, handling cumbersome, heavy loads efficiently with minimal downtime for maintenance.

A research collaboration between Cornell and the Max Planck Institute for Intelligent Systems has found an efficient way to expand the collective behavior of swarming microrobots: Mixing different sizes of the micron-scale 'bots enables them to self-organize into diverse patterns that can be manipulated when a magnetic field is applied. The technique even allows the swarm to "cage" passive objects and then expel them.

Our newsfeeds are filled with talk about the rapid rise of artificial intelligence (AI) in software such as ChatGPT and Stable Diffusion, which can quickly—albeit haphazardly—generate works such as essays and photographs from a text prompt. Reading these, you might be excused for thinking that writers and photographers are soon to go the way of the elevator operator, automated out of existence.

Learning from one's past mistakes is not limited to humans. Computers do it, too. In industries, this is done via computer-based control systems that help operate production systems. For industrial robots that perform specific tasks in batches, say producing clothing, computer chips, or baked goods, the most commonly used control technique is iterative learning control (ILC). Most industries still rely on ILC systems that use a learning strategy called the proportional-type update rule (PTUR). This technique improves the performance of ILC systems by repeating the same task over and over and updating its control input based on errors encountered in previous iterations.

Many existing robotics systems draw inspiration from nature, artificially reproducing biological processes, natural structures or animal behaviors to achieve specific goals. This is because animals and plants are innately equipped with abilities that help them to survive in their respective environments, and that could thus also improve the performance of robots outside of laboratory settings.

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