Like biological fat reserves store energy in animals, a new rechargeable zinc battery integrates into the structure of a robot to provide much more energy, researchers report.

“Batteries that can do double duty—to store charge and protect the robot’s ‘organs’—replicate the multifunctionality of fat tissues serving to store energy in living creatures.”

This approach to increasing capacity will be particularly important as robots shrink to the microscale and below—scales at which current stand-alone batteries are too big and inefficient.

“Robot designs are restricted by the need for batteries that often occupy 20% or more of the available space inside a robot, or account for a similar proportion of the robot’s weight,” says Nicholas Kotov, a professor of engineering at the University of Michigan who led the research.

Better batteries for robots

Applications for mobile robots are exploding, from delivery drones and bike-lane take-out bots to robotic nurses and warehouse robots. On the micro side, researchers are exploring swarm robots that can self-assemble into larger devices.

Multifunctional structural batteries can potentially free up space and reduce weight, but until now they could only supplement the main battery.

“Distributed energy storage, which is the biological way, is the way to go for highly efficient biomorphic devices.”

“No other structural battery reported is comparable, in terms of energy density, to today’s state-of-the-art advanced lithium batteries. We improved our prior version of structural zinc batteries on 10 different measures, some of which are 100 times better, to make it happen,” Kotov says.

The combination of energy density and inexpensive materials means that the battery may already double the range of delivery robots, he says.

“This is not the limit, however. We estimate that robots could have 72 times more power capacity if their exteriors were replaced with zinc batteries, compared to having a single lithium ion battery,” says Mingqiang Wang, a recent visiting researcher in Kotov’s lab who is now a postdoctoral researcher at Harbin Institute of Technology in China, and first author of the study in Science Robotics.

How it works

The new battery works by passing hydroxide ions between a zinc electrode and the air side through an electrolyte membrane. That membrane is partly a network of aramid nanofibers—the carbon-based fibers found in Kevlar vests—and a new water-based polymer gel. The gel helps shuttle the hydroxide ions between the electrodes.

Made with cheap, abundant and largely nontoxic materials, the battery is more environmentally friendly than those currently in use. The gel and aramid nanofibers will not catch fire if the battery is damaged, unlike the flammable electrolyte in lithium ion batteries. The aramid nanofibers could be upcycled from retired body armor.

To demonstrate their batteries, the researchers experimented with regular-sized and miniaturized toy robots in the shape of a worm and a scorpion. The team replaced their original batteries with zinc-air cells. They wired the cells into the motors and wrapped them around the outsides of the creepy crawlers.

“Batteries that can do double duty—to store charge and protect the robot’s ‘organs’—replicate the multifunctionality of fat tissues serving to store energy in living creatures,” says Ahmet Emre, a doctoral student in biomedical engineering in Kotov’s lab.

The downside of zinc batteries is that they maintain high capacity for about 100 cycles, rather than the 500 or more that we expect from the lithium ion batteries in our smartphones. This is because the zinc metal forms spikes that eventually pierce the membrane between the electrodes.

The strong aramid nanofiber network between the electrodes is the key to the relatively long cycle life for a zinc battery. And the inexpensive and recyclable materials make the batteries easy to replace.

Beyond the advantages of the battery’s chemistry, Kotov says that the design could enable a shift from a single battery to distributed energy storage, using graph theory approach.

“We don’t have a single sac of fat, which would be bulky and require a lot of costly energy transfer,” Kotov says. “Distributed energy storage, which is the biological way, is the way to go for highly efficient biomorphic devices.”

Funding for the research came from the Department of Defense, the National Science Foundation, and the Air Force Office of Scientific Research. The University of Michigan has applied for patent protection and is seeking commercial partners to bring the technology to market.

Source: University of Michigan

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Artificial brain gives robot the smarts for complex tasks

Engineers have developed an artificial brain system with touch and vision sensors to make a smarter robot.

Picking up a can of soft drink may be a simple task for humans, but this is a complex task for robots. It has to locate the object, deduce its shape, determine the right amount of strength to use, and grasp the object without letting it slip.

Most of today’s robots operate solely based on visual processing, which limits their capabilities. In order to perform more complex tasks, robots need an exceptional sense of touch and the ability to process sensory information quickly and intelligently.

The new sensory integrated artificial brain system mimics biological neural networks, and can run on a power-efficient neuromorphic processor, such as Intel’s Loihi chip.

The system also integrates artificial skin and vision sensors, equipping robots with the ability to draw accurate conclusions about the objects they are grasping based on the data the vision and touch sensors capture in real-time.

Fast and accurate sensing

“The field of robotic manipulation has made great progress in recent years,” says Benjamin Tee, assistant professor in the materials science and engineering department at the National University of Singapore.

“However, fusing both vision and tactile information to provide a highly precise response in milliseconds remains a technology challenge. Our recent work combines our ultra-fast electronic skins and nervous systems with the latest innovations in vision sensing and AI for robots so that they can become smarter and more intuitive in physical interactions,” he says.

Enabling a human-like sense of touch in robotics could significantly improve current functionality, and even lead to new uses. For example, on the factory floor, robotic arms fitted with electronic skins could easily adapt to different items, using tactile sensing to identify and grip unfamiliar objects with the right amount of pressure to prevent slipping.

For the new robotic system, the researchers applied an advanced artificial skin called asynchronous coded electronic skin, which Tee and colleagues developed in 2019. The sensor detects touches more than 1,000 times faster than the human sensory nervous system and can also identify the shape, texture, and hardness of objects 10 times faster than the blink of an eye.

“Making an ultra-fast artificial skin sensor solves about half the puzzle of making robots smarter. They also need an artificial brain that can ultimately achieve perception and learning as another critical piece in the puzzle,” says Tee.

Another puzzle piece for smart robots

To break new ground in robotic perception, the researchers explored neuromorphic technology—an area of computing that emulates the neural structure and operation of the human brain—to process sensory data from the artificial skin.

As Tee and colleague Harold Soh, an assistant professor in the computer science department, are members of the Intel Neuromorphic Research Community, they say it was a natural choice to use Intel’s Loihi neuromorphic research chip for their new robotic system.

In the initial experiments, the researchers fitted a robotic hand with the artificial skin, and used it to read Braille, passing the tactile data to Loihi via the cloud to convert the micro bumps the hand felt into a semantic meaning. Loihi achieved over 92% accuracy in classifying the Braille letters, while using 20 times less power than a normal microprocessor.

Soh’s team combined both vision and touch data in a spiking neural network to improve the robot’s perception capabilities. In their experiments, the researchers tasked a robot equipped with both artificial skin and vision sensors to classify various opaque containers containing differing amounts of liquid. They also tested the system’s ability to identify rotational slip, which is important for stable grasping.

In both tests, the spiking neural network that used both vision and touch data was able to classify objects and detect object slippage. The classification was 10% more accurate than a system that used only vision. Moreover, using a technique Soh’s team developed, the neural networks could classify the sensory data while it was being accumulated, unlike the conventional approach where data is classified after it has been fully gathered.

In addition, the researchers demonstrated the efficiency of neuromorphic technology: Loihi processed the sensory data 21%t faster than a top performing graphics processing unit, while using more than 45 times less power.

“We’re excited by these results,” Soh says. “They show that a neuromorphic system is a promising piece of the puzzle for combining multiple sensors to improve robot perception. It’s a step towards building power-efficient and trustworthy robots that can respond quickly and appropriately in unexpected situations.”

Moving forward, Tee and Soh plan to further develop their novel robotic system for applications in the logistics and food manufacturing industries where there is a high demand for robotic automation, especially moving forward in the post-COVID era.

The researchers presented the findings at the Robotics: Science and Systems conference in July 2020.

The National Robotics R&D Programme Office funded the work.

Source: National University of Singapore

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