TL;DR
- Researchers at Yale have developed a robotic fabric, a breakthrough that could lead to such innovations as adaptive clothing, self-deploying shelters, or lightweight shape-changing machinery.
- Engineers have built a squid-like robot that can swim untethered, propelling itself by generating jets of water. The robot carries its own power source inside its body. It can also carry a sensor, such as a camera, for underwater exploration.
- An international team of Johannes Kepler University researchers is developing robots made from soft materials. A new article in the journal Communications Materials demonstrates how these kinds of soft machines react using weak magnetic fields to move very quickly — even grabbing a quick-moving fly that has landed on it.
- Robots can be amazing tools for search-and-rescue missions and environmental studies, but eventually they must return to a base to recharge their batteries and upload their data. That can be a challenge if your robot is an autonomous underwater vehicle (AUV) exploring deep ocean waters.
- Researchers from TU Delft have now developed a new model that describes driving behaviour on the basis of one underlying ‘human’ principle: managing the risk below a threshold level. This model can accurately predict human behaviour during a wide range of driving tasks. In time, the model could be used in intelligent cars, to make them feel less ‘robotic’.
- A dose of artificial intelligence can speed the development of 3D-printed bioscaffolds that help injuries heal, according to researchers at Rice University.
- Vanderbilt University engineers have determined that their back-assist exosuit, a clothing-like device that supports human movement and posture, can reduce fatigue by an average of 29–47 percent in lower back muscles. The exosuit’s functionality presents a promising new development for individuals who work in physically demanding fields and are at risk for back pain, including medical professionals and frontline workers.
- A Lockheed Martin Robotics Seminar on “Socially Assistive Mobile Robots,” by Yi Guo from Stevens Institute of Technology.
- Panasonic has released plans for an Internet of Things system for hamsters.
- Check out robotics upcoming events below. And more!
Robotics market
The global market for robots is expected to grow at a compound annual growth rate (CAGR) of around 26 percent to reach just under 210 billion U.S. dollars by 2025. It is predicted that this market will hit the 100 billion U.S. dollar mark in 2020.
Size of the global market for industrial and non-industrial robots between 2018 and 2025(in billion U.S. dollars):
Size of the global market for industrial and non-industrial robots between 2018 and 2025(in billion U.S. dollars). Source: Statista
Latest Researches
Roboticizing fabric by integrating functional fibers
by Trevor L. Buckner et al. in Proceedings of the National Academy of Sciences
Researchers at Yale have developed a robotic fabric, a breakthrough that could lead to such innovations as adaptive clothing, self-deploying shelters, or lightweight shape-changing machinery.
The lab of Prof. Rebecca Kramer-Bottiglio has created a robotic fabric that includes actuation, sensing, and variable stiffness fibers while retaining all the qualities that make fabric so useful — flexibility, breathability, small storage footprint, and low weight. They demonstrated their robotic fabric going from a flat, ordinary fabric to a standing, load-bearing structure. They also showed a wearable robotic tourniquet and a small airplane with stowable/deployable fabric wings. The results are published this week in Proceedings of the National Academy of Sciences.
The researchers focused on processing functional materials into fiber-form so they could be integrated into fabrics while retaining its advantageous properties. For example, they made variable stiffness fibers out of an epoxy embedded with particles of Field’s metal, an alloy that liquifies at relatively low temperatures. When cool, the particles are solid metal and make the material stiffer; when warm, the particles melt into liquid and make the material softer.
“Our Field’s metal-epoxy composite can become as flexible as latex rubber or as stiff as hard acrylic, over 1,000 times more rigid, just by heating it up or cooling it down,” said Trevor Buckner, a graduate student in Kramer-Bottiglio’s lab and lead author on the paper. “Long fibers of this material can be sewn onto a fabric to give it a supportive skeleton that we can turn on and off.” These on-demand support fibers allow a robotic fabric to be bent or twisted and then locked into shape, or hold loads that would otherwise collapse a typical fabric.
To create sensors that detect internal or environmental changes and allow the fabric to respond appropriately, the researchers developed a conductive ink based on a Pickering emulsion, which lowers the ink viscosity and also enables the use of non-toxic solvents. With this ink, the researchers can paint the sensors directly onto the fabric.
“The conductive composite self-coagulates around the individual fibers and does not notably change the porosity of the fabric,” said Kramer-Bottiglio, the John J. Lee Assistant Professor of Mechanical Engineering & Materials Science. “The sensors are visible, but don’t change the texture or breathability of the fabric, which is important for comfort in wearable applications.”
To make the fabric move, the researchers used shape-memory alloy (SMA) wire, which can return to a programmed shape after being deformed. SMA wire is usually programmed into coils or meshes to generate contracting motion, but this approach was not desirable as it caused the fabric to bunch up unpredictably.
“Instead of using the coil technique, we flattened the wires out into ribbons to give them a geometry much more suited to smooth bending motion, which is perfect for robotic fabrics,” said Buckner.
As the project was funded by the Air Force Office of Science Research, the researchers envision applications such deployable and adaptive structures, active compression garments, smart cargo webbing, and reconfigurable RF antennas. “We believe this technology can be leveraged to create self-deploying tents, robotic parachutes, and assistive clothing,” says Kramer-Bottiglio. “Fabrics are a ubiquitous material used in a wide range of products, and the ability to ‘roboticize’ some of these products opens up many possibilities.”
Untethered and ultrafast soft-bodied robots
by Xu Wang et al. in Communications Materials
An international team of Johannes Kepler University researchers is developing robots made from soft materials. A new article in the journal Communications Materials demonstrates how these kinds of soft machines react using weak magnetic fields to move very quickly — even grabbing a quick-moving fly that has landed on it.
When we imagine a moving machine, such as a robot, we picture something largely made out of hard materials, says Martin Kaltenbrunner. He and his team of researchers at the JKU’s Department of Soft Matter Physics and the LIT Soft Materials Lab have been working to build a soft materials-based system. When creating these kinds of systems, there is a basic underlying idea to create conducive conditions that support close robot-human interaction in the future — without the solid machine physically harming humans.
In June, scientists presented a new approach for electromagnetic motors. Instead of copper wire and iron, elastic materials and liquid metal now form the basic ingredients for the so-called actuator. Scientists also recently introduced a new type of bio-gel in a journal article in Nature Materials which is elastic, flexible, and stable enough to be combined with electronic components in order to create a kind of “soft robot.”
A team led by Kaltenbrunner and Denys Makarov (Helmholtz Center Dresden-Rossendorf) is now taking the development of these kinds of machines one step further. The two researchers noted that before, a drawback was that these wirelessly designed soft robots were only able to change shape very slowly. Their new idea is based on using the flexible plastic polydimethylsiloxane and mixing in magnetic microparticles such as an alloy of neodymium, iron and boron.
The researchers gave their small, soft robots different shapes. Depending on the shape, where the microparticles were placed, and on the thickness of the materials used, the robots were able to move in different ways when exposed to a changing magnetic field in their environment. These actuators are just a few micrometers thin and a few micrograms in weight so they require little energy to move. In addition, the components can repeat the movements millions of times without any changes.
By influencing and changing the magnetic field, Kaltenbrunner and his colleagues have managed to build tiny robots that could hover, swim and — in the broadest sense — even walk. They also showed that in just a few milliseconds, their flower-shaped robots could catch a fly that had landed on it.
Scientists say this now paves the way to new opportunities to develop soft robots that also move very quickly. The long-term idea is to primarily produce more complex mini-machines that could, for example, help unblock blood vessels in the human body. In order to do this, the materials used would have to be biodegradable and easy to control.
a Illustration of the preparation steps for NdFeB/PDMS membrane-based magnetic soft robots. A magnetic composite is fabricated by mixing NdFeB microparticle fillers with PDMS at high loading ratios of 70 wt% magnetic solids. Subsequent spin-coating yields thin (down to 7 μm) elastomeric membranes that are magnetized by an out-of-plane magnetic field induced by an electromagnet. A ready-to-use magnetic soft robot is obtained by high throughput engraving or cutting of the magnetic membranes. b, c Schematic diagram showing different types of multi-arm robots, as well as the setup (two electromagnetic coils) used to achieve untethered robot actuation. d Performance (energy density and specific energy density) of our magnetic soft robots compared to other approaches. e Simulated (top row) and experimental (bottom row) results of a flower-shaped magnetic soft robot highlight the predictive nature of our dynamic model and the versatile applicability of the robot due to its fast and silent actuation. It closes around a living fly momentarily and releases it again without harming the fragile insect (Supplementary Movie 2). Scale bars, 1 cm.
Cephalopod-inspired robot capable of cyclic jet propulsion through shape change
by Caleb Michael Christianson, Yi Cui, Michael Ishida, Xiaobo Bi, Qiang Zhu, Geno Pawlak, Michael T. Tolley in Bioinspiration & Biomimetics
Engineers have built a squid-like robot that can swim untethered, propelling itself by generating jets of water. The robot carries its own power source inside its body. It can also carry a sensor, such as a camera, for underwater exploration.
“Essentially, we recreated all the key features that squids use for high-speed swimming,” said Michael T. Tolley, one of the paper’s senior authors and a professor in the Department of Mechanical and Aerospace Engineering at UC San Diego. “This is the first untethered robot that can generate jet pulses for rapid locomotion like the squid and can achieve these jet pulses by changing its body shape, which improves swimming efficiency.”
This squid robot is made mostly from soft materials such as acrylic polymer, with a few rigid, 3D printed and laser cut parts. Using soft robots in underwater exploration is important to protect fish and coral, which could be damaged by rigid robots. But soft robots tend to move slowly and have difficulty maneuvering.
The research team, which includes roboticists and experts in computer simulations as well as experimental fluid dynamics, turned to cephalopods as a good model to solve some of these issues. Squid, for example, can reach the fastest speeds of any aquatic invertebrates thanks to a jet propulsion mechanism.
Their robot takes a volume of water into its body while storing elastic energy in its skin and flexible ribs. It then releases this energy by compressing its body and generates a jet of water to propel itself.
At rest, the squid robot is shaped roughly like a paper lantern, and has flexible ribs, which act like springs, along its sides. The ribs are connected to two circular plates at each end of the robot. One of them is connected to a nozzle that both takes in water and ejects it when the robot’s body contracts. The other plate can carry a water-proof camera or a different type of sensor.
Engineers first tested the robot in a water testbed in the lab of Professor Geno Pawlak, in the UC San Diego Department of Mechanical and Aerospace Engineering. Then they took it out for a swim in one of the tanks at the UC San Diego Birch Aquarium at the Scripps Institution of Oceanography.
They demonstrated that the robot could steer by adjusting the direction of the nozzle. As with any underwater robot, waterproofing was a key concern for electrical components such as the battery and camera.They clocked the robot’s speed at about 18 to 32 centimeters per second (roughly half a mile per hour), which is faster than most other soft robots.
“After we were able to optimize the design of the robot so that it would swim in a tank in the lab, it was especially exciting to see that the robot was able to successfully swim in a large aquarium among coral and fish, demonstrating its feasibility for real-world applications,” said Caleb Christianson, who led the study as part of his Ph.D. work in Tolley’s research group. He is now a senior medical devices engineering at San Diego-based Dexcom.
Researchers conducted several experiments to find the optimal size and shape for the nozzle that would propel the robot. This in turn helped them increase the robot’s efficiency and its ability to maneuver and go faster. This was done mostly by simulating this kind of jet propulsion, work that was led by Professor Qiang Zhu and his team in the Department of Structural Engineering at UC San Diego. The team also learned more about how energy can be stored in the elastic component of the robot’s body and skin, which is later released to generate a jet.
Collaborative Mission Planning for Long-Term Operation Considering Energy Limitations
by Bingxi Li, Brian R. Page, Barzin Moridian, Nina Mahmoudian in IEEE Robotics and Automation Letters
Robots can be amazing tools for search-and-rescue missions and environmental studies, but eventually they must return to a base to recharge their batteries and upload their data. That can be a challenge if your robot is an autonomous underwater vehicle (AUV) exploring deep ocean waters.
Now, a Purdue University team has created a mobile docking system for AUVs, enabling them to perform longer tasks without the need for human intervention.
The team also has published papers on ways to adapt this docking system for AUVs that will explore extraterrestrial lakes, such as those of Jupiter and Saturn’s moons.
“My research focuses on persistent operation of robots in challenging environments,” said Nina Mahmoudian, an associate professor of mechanical engineering. “And there’s no more challenging environment than underwater.”
Once a marine robot submerges in water, it loses the ability to transmit and receive radio signals, including GPS data. Some may use acoustic communication, but this method can be difficult and unreliable, especially for long-range transmissions. Because of this, underwater robots currently have a limited range of operation.
“Typically these robots perform a pre-planned itinerary underwater,” Mahmoudian said. “Then they come to the surface and send out a signal to be retrieved. Humans have to go out, retrieve the robot, get the data, recharge the battery and then send it back out. That’s very expensive, and it limits the amount of time these robots can be performing their tasks.”
Mahmoudian’s solution is to create a mobile docking station that underwater robots could return to on their own.
“And what if we had multiple docks, which were also mobile and autonomous?” she said. “The robots and the docks could coordinate with each other, so that they could recharge and upload their data, and then go back out to continue exploring, without the need for human intervention. We’ve developed the algorithms to maximize these trajectories, so we get the optimum use of these robots.”
A paper on the mission planning system that Mahmoudian and her team developed has been published in IEEE Robotics and Automation Letters. The researchers validated the method by testing the system on a short mission in Lake Superior.
“What’s key is that the docking station is portable,” Mahmoudian said. “It can be deployed in a stationary location, but it can also be deployed on autonomous surface vehicles or even on other autonomous underwater vehicles. And it’s designed to be platform-agnostic, so it can be utilized with any AUV. The hardware and software work hand-in-hand.”
Mahmoudian points out that systems like this already exist in your living room. “An autonomous vacuum, like a Roomba, does its vacuum cleaning, and when it runs out of battery, it autonomously returns to its dock to get recharged,” she said, “That’s exactly what we are doing here, but the environment is much more challenging.”
If her system can successfully function in a challenging underwater environment, then Mahmoudian sees even greater horizons for this technology.
Human-like driving behaviour emerges from a risk-based driver model
by Sarvesh Kolekar, Joost de Winter, David Abbink in Nature Communications
Researchers from TU Delft have now developed a new model that describes driving behaviour on the basis of one underlying ‘human’ principle: managing the risk below a threshold level. This model can accurately predict human behaviour during a wide range of driving tasks. In time, the model could be used in intelligent cars, to make them feel less ‘robotic’.
Risk threshold
Driving behaviour is usually described using models that predict an optimum path. But this is not how people actually drive. ‘You don’t always adapt your driving behaviour to stick to one optimum path,’ says researcher Sarvesh Kolekar from the Department of Cognitive Robotics. ‘People don’t drive continuously in the middle of their lane, for example: as long as they are within the acceptable lane limits, they are fine with it.’
Models that predict an optimum path are not only popular in research, but also in vehicle applications. ‘The current generation of intelligent cars drive very neatly. They continuously search for the safest path: i.e. one path at the appropriate speed. This leads to a “robotic” style of driving,’ continues Kolekar. ‘To get a better understanding of human driving behaviour, we tried to develop a new model that used the human risk threshold as the underlying principle.’
Driver’s Risk Field
To get to grips with this concept, Kolekar introduced the so-called Driver’s Risk Field (DRF). This is an ever-changing two-dimensional field around the car that indicates how high the driver considers the risk to be at each point. Kolekar devised these risk assessments in previous research. The gravity of the consequences of the risk in question are then taken into account in the DRF. For example, having a cliff on one side of the road boundary is much more dangerous than having grass. ‘The DRF was inspired by a concept from psychology, put forward a long time ago (in 1938) by Gibson and Crooks. These authors claimed that car drivers ‘feel’ the risk field around them, as it were, and base their traffic manoeuvres on these perceptions.’ Kolekar managed to turn this theory into a computer algorithm.
Predictions
Kolekar then tested the model in seven scenarios, including overtaking and avoiding an obstacle. ‘We compared the predictions made by the model with experimental data on human driving behaviour taken from the literature. Luckily, a lot of information is already available. It turned out that our model only needs a small amount of data to ‘get’ the underlying human driving behaviour and could even predict reasonable human behaviour in previously unseen scenarios. Thus, driving behaviour rolls out more or less automatically; it is ‘emergent’.
Elegant
This elegant description of human driving behaviour has huge predictive and generalising value. Apart from the academic value, the model can also be used in intelligent cars. ‘If intelligent cars were to take real human driving habits into account, they would have a better chance of being accepted. The car would behave less like a robot.’
a This row illustrates Näätänen and Summala’s formulation of perceived risk. The consequence of an event (e.g., colliding with a tree) and the driver’s subjective belief about the probability of that event occurring, form the driver’s perceived risk. The driver in the ego car is indicated using the black marker. b This row illustrates the proposed quantification of this perceived risk. The cost of each element in the driving scene is multiplied with the Driver’s Risk Field (DRF) that represents the driver’s belief of the probability of being in a position. This product summed over all grid points generates the estimate of quantified risk.
Machine Learning Guided 3D Printing of Tissue Engineering Scaffolds
by Anja Conev, Eleni Litsa, Marissa Perez, Mani Diba, Antonios G. Mikos, Lydia Kavraki in Tissue Engineering Part A
A dose of artificial intelligence can speed the development of 3D-printed bioscaffolds that help injuries heal, according to researchers at Rice University.
A team led by computer scientist Lydia Kavraki of Rice’s Brown School of Engineering used a machine learning approach to predict the quality of scaffold materials, given the printing parameters. The work also found that controlling print speed is critical in making high-quality implants.
Bioscaffolds developed by co-author and Rice bioengineer Antonios Mikos are bonelike structures that serve as placeholders for injured tissue. They are porous to support the growth of cells and blood vessels that turn into new tissue and ultimately replace the implant.
Mikos has been developing bioscaffolds, largely in concert with the Center for Engineering Complex Tissues, to improve techniques to heal craniofacial and musculoskeletal wounds. That work has progressed to include sophisticated 3D printing that can make a biocompatible implant custom-fit to the site of a wound.
That doesn’t mean there isn’t room for improvement. With the help of machine learning techniques, designing materials and developing processes to create implants can be faster and eliminate much trial and error.
“We were able to give feedback on which parameters are most likely to affect the quality of printing, so when they continue their experimentation, they can focus on some parameters and ignore the others,” said Kavraki, an authority on robotics, artificial intelligence and biomedicine and director of Rice’s Ken Kennedy Institute.
The study identified print speed as the most important of five metrics the team measured, the others in descending order of importance being material composition, pressure, layering and spacing.
Mikos and his students had already considered bringing machine learning into the mix. The COVID-19 pandemic created a unique opportunity to pursue the project.
“This was a way to make great progress while many students and faculty were unable to get to the lab,” Mikos said.
Kavraki said the researchers — graduate students Anja Conev and Eleni Litsa in her lab and graduate student Marissa Perez and postdoctoral fellow Mani Diba in the Mikos lab, all co-authors of the paper — took time at the start to establish an approach to a mass of data from a 2016 study on printing scaffolds with biodegradable poly(propylene fumarate), and then to figure out what more was needed to train the computer models.
“The students had to figure out how to talk to each other, and once they did, it was amazing how quickly they progressed,” Kavraki said.
From start to finish, the COVID-19 window let them assemble data, develop models and get the results published within seven months, record time for a process that can often take years.
The team explored two modeling approaches. One was a classification method that predicted whether a given set of parameters would produce a “low” or “high” quality scaffold. The other was a regression-based approach that approximated the values of print-quality metrics to come to a result. Kavraki said both relied upon a “classical supervised learning technique” called random forest that builds multiple “decision trees” and “merges” them together to get a more accurate and stable prediction.
Ultimately, the collaboration could lead to better ways to quickly print a customized jawbone, kneecap or bit of cartilage on demand.
“A hugely important aspect is the potential to discover new things,” Mikos said. “This line of research gives us not only the ability to optimize a system for which we have a number of variables — which is very important — but also the possibility to discover something totally new and unexpected. In my opinion, that’s the real beauty of this work.
“It’s a great example of convergence,” he said. “We have a lot to learn from advances in computer science and artificial intelligence, and this study is a perfect example of how they will help us become more efficient.”
“In the long run, labs should be able to understand which of their materials can give them different kinds of printed scaffolds, and in the very long run, even predict results for materials they have not tried,” Kavraki said. “We don’t have enough data to do that right now, but at some point we think we should be able to generate such models.”
Kavraki noted The Welch Institute, recently established at Rice to enhance the university’s already stellar reputation for advanced materials science, has great potential to expand such collaborations.
“Artificial intelligence has a role to play in new materials, so what the institute offers should be of interest to people on this campus,” she said. “There are so many problems at the intersection of materials science and computing, and the more people we can get to work on them, the better.”
Low-profile elastic exosuit reduces back muscle fatigue
by Lamers, E.P., Soltys, J.C., Scherpereel, K.L. et al. in Sci Rep 10, 15958
Vanderbilt University engineers have determined that their back-assist exosuit, a clothing-like device that supports human movement and posture, can reduce fatigue by an average of 29–47 percent in lower back muscles. The exosuit’s functionality presents a promising new development for individuals who work in physically demanding fields and are at risk for back pain, including medical professionals and frontline workers.
The research, led by Assistant Professor of Mechanical Engineering Karl Zelik and recent Ph.D. graduate and primary author Erik Lamers, used surface electromyography techniques to measure changes in low back muscle fatigue in male and female participants, who were given physical tasks to perform both with and without the exosuit.
The wearable technology developed by Zelik’s team may conjure images of Iron Man’s suit, but it does not rely on motors or batteries. Instead, the low-profile, elastic exosuit applies assistive forces that cooperate with the low back extensor muscles, to relieve strain on the muscles and spine, and to help reduce injury risks.
This study showed that wearing the exosuit made holding a 35-pound weight (the average weight of a 4-year-old child) less tiring on the back than holding a 24-pound weight (the average weight of an 18-month-old baby) without the exosuit.
“These findings show how exosuits could provide valuable back relief to frontline and essential workers who have been taking a physical toll and supporting all of us throughout this pandemic. What we learned has the potential to shape the biomechanical and industrial standards of future wearable technologies,” said Zelik, who holds secondary appointments in biomedical engineering and in physical medicine and rehabilitation.
The work also demonstrated a sharpened understanding of how the latissimus dorsi muscles (the “lats”) — those that adduct, extend and medially rotate the shoulder joint — affect low back mechanics. While previous research has shown that the lats generally do not contribute much to supporting the low back, the investigators discovered that, as people get tired, they may suddenly recruit the lats to offload the main back extensor muscles to a significant degree.
“The lats act sort of like an exosuit. When a person’s low back muscles become over-strained and fatigued, they summon extra assistance from their lats to relieve this back strain and fatigue. The elastic bands in our exosuit work the same way to help sustain endurance and strength,” said Lamers, a National Science Foundation Graduate Research Fellow who worked in Vanderbilt’s Center for Rehabilitation Engineering and Assistive Technology.
Zelik, a former collegiate athlete who competed in the long jump and triple jump, knows firsthand how intensive physical activity can fatigue the body. He also understands the importance of ensuring that the exosuit and its utility are built with inclusive design practices. “Wearables are going to change the way we work and live, and we want to improve safety, health and well-being for everyone. One of the critical challenges moving forward will be to ensure that all wearable technology is developed to serve and protect both women and men. We are thrilled that this research helped lead to the first commercial exosuit or exoskeleton designed with both male- and female-fits,” Zelik said.
Videos
A Lockheed Martin Robotics Seminar on “Socially Assistive Mobile Robots,” by Yi Guo from Stevens Institute of Technology.
Smile Robotics describes this as “(possibly) world’s first full-autonomous clear-up-the-table robot.”
Panasonic has released plans for an Internet of Things system for hamsters.
Interbotix has this “research level robotic crawler,” which both looks mean and runs ROS, a dangerous combination.
A literal in-depth look at Engineered Arts’ Mesmer android.
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