Researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and the Harvard Wyss Institute for Biologically Inspired Engineering have developed an extremely dexterous microrobot. HAMR-JR is a half-scale version of the previously created cockroach-inspired Harvard Ambulatory Microrobot (HAMR).
The HAMR-JR is just the size of a penny, but it is capable of performing all of the tasks of the larger HAMR. It is one of the most dexterous microbots to be created.
The research titled “Scaling down an insect-size microrobot, HAMR-VI into HAMR-Jr” was presented virtually at the International Conference on Robotics and Automation (ICRA 2020).
Kaushik Jayaram is a former postdoctoral fellow at SEAS and Wyss, as well as the first author of the paper. Jayaram is also an Assistant Professor at the University of Colorado, Boulder.
“Most robots at this scale are pretty simple and only demonstrate basic mobility,” said Jayaram. “We have shown that you don’t have to compromise dexterity or control for size.“
One of the tasks going into the project was to figure out whether the pop-up manufacturing process, which was used to build previous versions of HAMR and microbots like the RoboBee, could be used to construct robots at multiple scales. This could be as small as surgical bots or as large as industrial robots.
The process used to build HAMR-JR is called printed circuit microelectromechanical systems, or PC-MEMS. In this process, the robot’s components are put into a 3D structure after being etched into a 2D sheet. For the HAMR-JR, the 2D sheet design of the robot, actuators, and onboard circuitry were shrunk down to create a smaller robot with the same functionalities.
“The wonderful part about this exercise is that we did not have to change anything about the previous design,” said Jayaram. “We proved that this process can be applied to basically any device at a variety of sizes.”
Scaling Down HAMR-JR
HAMR-JR is only 2.25 centimeters in body length with a weight of 0.3 grams. On top of being one of the smallest microrobots, it is also one of the fastest, capable of moving 14 body lengths per second.
Principles such as stride length and joint stiffness do get affected when the robot is scaled down. To get around this, the researchers also developed a model that is able to predict locomotion metrics such as running speeds, foot forces, and payload depending on target size. With this model, a system can be developed with the right specifications.
Robert Wood is co-author of the paper and a Charles River Professor of Engineering and Applied Sciences in SEAS. He is also a Core Faculty Member of the Wyss.
“This new robot demonstrates that we have a good grasp on the theoretical and practical aspects of scaling down complex robots using our folding-based assembly approach,” Wood said.
The research was supported by DARPA and the Wyss Institute.
Other co-authors included Jennifer Shum, Samantha Castellanos, and E. Farrell Helbling.
Researchers Develop New Theory on Animal Sensing Which Could be Used in Robotics
All animals, from insects to humans, rely on their senses as some of the most important tools for survival. Sensory organs like eyes, ears and noses are used while searching for food or detecting threats. However, the actual position and orientation of the sense organs are not intuitive, and the currently deployed theories are not able to make predictions about the position and orientation.
That is now changing with new developments coming out of Northwestern University. A team of researchers has come up with a new theory that is in fact able to predict the movement of an animal’s sensory organs, specifically when that animal is searching for something important like food.
The research was published Sept. 22 in the journal eLife.
Energy-Constrained Proportional Betting
The newly developed theory, termed energy-constrained proportional betting, was applied to four different species of animals, and it involved three different senses, including vision and smell. The team demonstrated how the theory could predict the observed sensing behavior of each animal.
This new theory could have implications within the field of robotics, possibly improving robot performance when it comes to collecting information. It could also make a difference in the development of autonomous vehicles, specifically improving their response to uncertainty.
Malcolm A. Maclver led the promising research. He is also a professor of biomedical and mechanical engineering in Northwestern’s McCormick School of Engineering, as well as a professor of neurobiology in the Weinberg College of Arts and Sciences.
“Animals make their living through movement,” Maclver said. “To find food and mates and to identify threats, they need to move. Our theory provides insight into how animals gamble on how much energy to expend to get the useful information they need.”
The new theory sheds light into the different motions of sensory organs, and the resulting algorithm generated simulated sensory organ movements. These generated movements agreed with the real-life sensory organ movements from fish, mammals and insects.
Chen Chen is a Ph.D student in Maclver’s lab and the first author, while Todd D. Murphey, professor of mechanical engineering at McCormick, is a co-author.
Movement costs a lot of energy for animals, and they spend that energy while gambling that the locations they are moving to will be informative. The amount of food-derived energy that they are willing to spend is proportional to the expected value of those locations, according to the researchers.
“While most theories predict how an animal will behave when it largely already knows where something is, ours is a prediction for when the animal knowns very little — a situation in life and critical to survival,” Murphey says.
The research focused on the gymnotid electric fish from South America, and experiments were performed in Maclver’s lab. It was not all new data however, as the team utilized past published datasets on the blind eastern American mole, the American cockroach and the hummingbird hawkmoth.
The three senses that were focused on include electrosense with the electric fish, vision with the moth and smell with the mole and roach.
The newly-developed theory leads to more energy and time being preserved when moving around to gather information. At the same time, there is enough information to guide tracking and other exploratory behaviors common among animals.
“When you look at a cat’s ears, you’ll often see them swiveling to sample different locations of space,” Maclver said. “This is an example of how animals are constantly positioning their sensory organs to help them absorb information from the environment. It turns out there is a lot going on below the surface in the movement of sense organs like ears and eyes and noses.”
Human Brain’s Light Processing Ability Could Lead to Better Robotic Sensing
The human brain often serves as inspiration for artificial intelligence (AI), and that is the case once again as a team of Army researchers has managed to improve robotic sensing by looking at how the human brain processes bright and contrasting light. The new development can help lead to the collaboration between autonomous agents and humans.
According to the researchers, it is important for machine sensing to be effective across changing environments, which leads to developments in autonomy.
The research was published in the Journal of Vision.
100,000-to-1 Display Capability
Andre Harrison is a researcher at the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory.
“When we develop machine learning algorithms, real-world images are usually compressed to a narrower range, as a cellphone camera does, in a process called tone mapping,” Harrison said. “This can contribute to the brittleness of machine vision algorithms because they are based on artificial images that don’t quite match the patterns we see in the real world.”
The team of researchers developed a system with 100,000-to-1 display capability, which enabled them to gain insight into the brain’s computing process in the real-world. According to Harrison, this allowed the team to implement biological resilience into sensors.
The current vision algorithms still have a long way to go before becoming ideal. This has to do with the limited range in luminance, at around 100-to-1 ratio, due to the algorithms being based on human and animal studies with computer monitors. The 100-to-1 ratio is less-than-ideal in the real world, where the variation can go all the way up to 100,000-to-1. This high ratio is termed high dynamic range, or HDR.
Dr. Chou Po Hung is an Army researcher.
“Changes and significant variations in light can challenge Army systems — drones flying under a forest canopy could be confused by reflectance changes when wind blows through the leaves, or autonomous vehicles driving on rough terrain might not recognize potholes nor other obstacles because the lighting conditions are slightly different from those of which their vision algorithms were trained,” Hung said.
The Human Brain’s Compressing Capability
The human brain is capable of automatically compressing the 100,000-to-1 input into a narrower range, and this is what allows humans to interpret shape. The team of researchers set out to understand this process by studying early visual processing under HDR. The team looked toward simple features such as HDR luminance.
“The brain has more than 30 visual areas, and we still have only a rudimentary understanding of how these areas process the eye’s image into an understanding of 3D shape,” Hung continued. “Our results with HDR luminance studies, based on human behavior and scalp recordings, show just how little we truly know about how to bridge the gap from laboratory to real-world environments. But, these findings break us out of that box, showing that our previous assumptions from standard computer monitors have limited ability to generalize to the real world, and they reveal principles that can guide our modeling toward the correct mechanisms.”
By discovering how light and contrast edges interact in the brain’s visual representation, algorithms will be more effective at reconstructing the 3D world under real-world luminance. When estimating 3D shape from 2D information, there are always ambiguities, but this new discovery allows for them to be corrected.
“Through millions of years of evolution, our brains have evolved effective shortcuts for reconstructing 3D from 2D information,” Hung said. “It’s a decades-old problem that continues to challenge machine vision scientists, even with the recent advances in AI.”
The team’s discovery is also important for the development of AI-devices like radar and remote speech understanding, which utilize wide dynamic range sensing.
“The issue of dynamic range is not just a sensing problem,” Hung said. “It may also be a more general problem in brain computation because individual neurons have tens of thousands of inputs. How do you build algorithms and architectures that can listen to the right inputs across different contexts? We hope that, by working on this problem at a sensory level, we can confirm that we are on the right track, so that we can have the right tools when we build more complex Als.”
Researchers Develop First Microscopic Robots Capable of “Walking”
In what is a breakthrough within the field of robotics, researchers have created the first microscopic robots capable of being controlled through their incorporated semiconductor components. The robots are able to “walk” with only standard electronic signals.
The microscopic robots are the size of a paramecium, and they will act as the foundation for further projects. Some of those could include complex versions with silicon-based intelligence, the mass production of such robots, and versions capable of moving through human tissue and blood.
The work was a collaboration led by Cornell University, which included Itai Cohen, professor of physics. Other members of the team included Paul McEuen, the John A. Newman Professor of Physical Science, as well as Marc Miskin, assistant professor at the University of Pennsylvania.
Their work was published Aug. 26 in Nature, titled “Electronically Integrated, Mass-Manufactured, Microscopic Robots.”
Previous Nanoscale Projects
The newly developed microscopic robots were built upon previous work done by Cohen and McEuen. Some of their previous nanoscale projects involved microscopic sensors and graphene-based origami machines.
The new microscopic robots are approximately 5 microns thick, 40 microns wide, and anywhere between 40 to 70 microns in length. One micron is just one-millionth of a meter.
Each robot has a simple circuit that is made from silicon photovoltaics and four electrochemical actuators. The silicon photovoltaics act as the torso and brain, while the electrochemical actuators act as the legs.
Controlling the Microscopic Robots
In order to control the robots, the researchers flash laser pulses at different photovoltaics, with each one making up a seperate set of legs. The robots are able to walk when the laser is toggled back and forth between the front and back photovoltaics.
The robots only operate at a low voltage of 200 millivolts, and they run on just 10 nanowatts of power. The material is strong for such a small object, and they are able to be fabricated parallel since they are constructed with standard lithographic processes. On just a four inch silicon wafer, there can be around 1 million bots.
The team is now looking at how to make the robots more powerful through electronics and onboard computation.
It is possible that future versions of microrobots could act in swarms and complete tasks like restructuring materials, suturing blood vessels, or be sent to the human brain.
“Controlling a tiny robot is maybe as close as you can come to shrinking yourself down. I think machines like these are going to take us into all kinds of amazing worlds that are too small to see,” said Miskin.
“This research breakthrough provides exciting scientific opportunity for investigating new questions relevant to the physics of active matter and may ultimately lead to futuristic robotic materials,” said Sam Stanton.
Stanton is program manager for the Army Research Office, which supported the microscopic robot research.
A video of Itai Cohen explaining the technology can be found here.
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