UConn engineers are using insects as platforms for small robots. Their microcircuit could improve control of futuristic biobots. (Getty Images)
A cockroach no bigger than a large paper clip scurries across the floor of Abhishek Dutta’s lab at the University of Connecticut.
Some scientists might be shocked to see such a notorious visitor occupying their research space.
But not Dutta. He watches intently as the roach moves left, and then right, then left again, as it traverses the cool tile floor. His interest is well-founded, for he is the one initiating the tiny creature’s movements with a small handheld device about 15 feet away.
The Madagascar hissing cockroach in this lab is not just any old member of the order Blattodea. It is a robot-roach hybrid, a hardwired biological insect – a cyborg if you will – and its future high-tech brethren may one day save your life.
“The use of insects as platforms for small robots has an incredible number of useful applications, from search and rescue to national defense,” says Dutta, an assistant professor of electrical and computer engineering who specializes in control system optimization and cyber-physical systems.
Cockroach robots aren’t new, however. Researchers have been exploring biorobotic platforms for insects for the better part of the past decade. But building robotic systems at such miniature scale isn’t easy, and the technology seems to work only about half the time.
In a paper soon to be published in Proceedings of the Conference on Cognitive Computational Neuroscience, Philadelphia 2018, Dutta, and undergraduate Evan Faulkner, a junior working in his lab, report their creation of a microcircuit that they say allows more reliable and precise control of robotic insect motion.
A cockroach with an implanted neurocontroller. (Image courtesy of the Dutta Lab)
To improve control of the insect, Dutta’s microcircuit incorporates a 9-axis inertial measurement unit that can detect the roach’s six degrees of free motion, its linear and rotational acceleration, and its compass heading. Another feature that Dutta and Faulkner added is the ambient temperature surrounding the creature, because tests have shown that the temperature of the environment in which a roach is moving can affect how and where the insect moves. Roaches, for the record, are more likely to go for walks when it’s warm.
The microcircuit Dutta and Faulkner created is part of a small electronic ‘backpack’ that can be strapped to the back of a cockroach. Wires from the device are attached to the insect’s antennae lobes. A tiny Bluetooth transmitter and receiver allows a nearby operator to control the roach’s movements via an ordinary cellphone. Sending tiny electrical impulses to the nerve tissue in the insect’s right or left antenna lobe makes the insect believe it has encountered an obstacle. A small charge to the left antenna makes the insect move away to the right. Likewise, a charge sent to the right antenna makes the insect move left. It’s power steering redefined.
While other labs have developed similar control systems, UConn’s microcircuit is distinctive in that it offers operators a greater degree of control of the insect’s movement, real-time feedback of the insect’s neuromuscular response to artificial stimuli, and multi-channel avenues for stimulating the insect’s nerve tissue. The result is a more informed and precise system of control.
The UConn system’s microcontroller and built-in potentiometer lets operators vary the output voltage, frequency, and cycle of the stimuli sent to the insect. (A potentiometer, if you’re wondering, is the proper name of an electronic device that adjusts voltage. It’s the thing that makes light dimmer switches possible, and allows you to adjust the volume on your stereo.) The stimulus that resulted in the most robust response from the cockroach was around 1.2V amplitude, 55 Hz frequency, and 50 percent duty cycle. (No roaches were hurt by these experiments, by the way.)
One interesting tidbit the researchers noticed was that the roach’s movements left or right in response to artificial stimulation decreased in intensity after the initial stimulus. So if the roach made a hard left after the first electronic pulse hit its right antenna lobe, its turn was less dramatic with each subsequent pulse to that lobe. The researchers aren’t sure why this happens, but it is handy information to know when you’re the one doing the steering.
Most importantly, Dutta says, the system allowed users to utilize the real-time feedback sent over the Bluetooth system to set specific parameters for stimulating the insect’s antennae lobes, and that allowed them to steer the insect in a desired direction.
“Our microcircuit provides a sophisticated system for acquiring real-time data on an insect’s heading and acceleration, which allows us to extrapolate its trajectory,” says Dutta. “We believe this advanced closed loop, model-based system provides better control for precision maneuvering, and overcomes some of the technical limitations currently plaguing today’s micro robots.”
While the new microcircuit is certainly a step forward for robot insect technology, Dutta acknowledges much more research is needed. Insect-driven biobots, you might say, are still in their larval stage. Ongoing advances in micro-hardware design and micro-control systems could lead to a new generation of devices that work even better.
Funding for this research was provided by a UConn startup grant, and in part by the United Technologies Corporation – Institute of Advanced Systems Engineering.
ECE Seminar Fall 2018
September 10, 1pm-2pm, ITE 336
Game-Theoretic Methods for Cyber-Physical Control and Security of Distributed Microgrids
Department of Electrical and Computer Engineering
New York University
Abstract: Game-theoretic methods have been widely used to model interactions of agents in complex systems. This talk aims to provide an overview of game-theoretic applications in the control and cybersecurity of microgrids. The first part of the talk introduces a non-cooperative game-theoretic power flow framework to develop distributed control of renewable-based microgrids. The solution concept of Nash equilibrium characterizes the outcome of distributed generation and plug-and-play integration with the power grid. The game-theoretic analysis leads to a fully distributed PMU-enabled algorithm which only needs local information of voltage angle at the bus. The talk also presents the Stackelberg equilibrium solution to capture the leader-and-follower relationships between the existing grid and the microgrids. The second part of the talk introduces game-theoretic models to understand the Stuxnet-type of threats on the power plants. A Bayesian dynamic game framework is first introduced to model the strategic interactions between an attacker and a defender under incomplete information. The attacker aims to achieve her objective stealthily through a combination of social engineering, lateral movement, and cyber-physical attacks. The defender aims to learn, detect, and mitigate the impact of the attack on the power plant and the consequential cascading failures. The talk will conclude with open questions and general discussions on game-theoretic frameworks for cyber-physical security and resilience.
Short bio: Quanyan Zhu received B. Eng. in Honors Electrical Engineering from McGill University in 2006, M.A.Sc. from University of Toronto in 2008, and Ph.D. from the University of Illinois at Urbana-Champaign (UIUC) in 2013. After a short stint at Princeton University, he joined the Department of Electrical and Computer Engineering at New York University (NYU) as an assistant professor in 2014. His research interest is game theory, smart grid, network security and privacy, resilient critical infrastructures, cyber-physical systems and cyber deception. He is a recipient of best paper awards at the International Conference on Information Fusion (Fusion 2015), ACM CCS Workshop on Managing Insider Security Threats (MIST 2015), and the International Symposium on Resilient Control Systems (ISRCS 2011). He spearheaded INFOCOM Workshop on Communications and Control on Smart Energy Systems (CCSES), Midwest Workshop on Control and Game Theory (WCGT) and New York Multidisciplinary Symposium on Security and Privacy. His current research has been funded by NSF, DOE, DHS, and DARPA.
Managing large herds efficiently would be difficult, perhaps even impossible, without the latest advances in computing and automation. Most dairies now have milking parlors and associated free-stall housing, which double or triple production per man-hour. Milking units automatically detach to reduce udder health problems and improve milk quality, while cow ID transponders let farmers automatically record production data.
The most recent major technological advance influencing the U.S. dairy industry is the development of automatic milking systems – or “robotic” milkers.
At the University of Connecticut’s Kellogg Dairy Center, we’re using robotic milkers as well as other sensors to monitor 100 cows and their physical environment. Through this work, launched this spring, we hope to monitor individual cow’s behavior and health in real time to improve production efficiency and animal well-being.
Robotic milkers can harvest milk without human involvement. In fact, the cows decide when to be milked, entering the machine without direct human supervision. The robotic system automatically identifies the cow and applies a sanitizing teat spray before a robotic arm attaches the teat cup for milking.
That’s very different from parlor milking, where managers decide when to milk cows, usually three times a day. Each robotic milking unit serves 50 to 55 cows.
Given the high price of early versions of the robotic milkers and the large size of U.S. herds, American dairies had minimal interest in robotic milkers before 2010. However, the number of automatic milking systems in the country increased to over 2,500 units in 2013, mainly due to improvements in design in the newer models. Worldwide, there are currently over 35,000 automatic milking systems in operation.
A row of cows being milked
Not only have these newer machines improved in harvesting milk efficiently, they have the added ability to collect a greater amount of information about production, milk composition and cow behavior. That allows producers to make more informed management decisions.
With robotic milking systems, the cows run the show. They decide when to eat, ruminate, rest or be milked. They also need to spend less than an hour per day actually being milked; before robotic milkers, milking often took up three to five hours per day.
We wanted to know: What are they doing with the rest of their day? How does that behavior affect production or serve to indicate health status? By themselves, the milking units can’t gather that kind of information, which would be very useful in finding out early on whether a particular cow is developing a health problem.
Our “cow-CPS” – a cyber-physical system that includes the cows, robotic milkers, video cameras and other sensors – will track data on our cows at all times. That will tell us, among other things, where the cows go when not being milked; when they decide to eat, rest or do other activities; and the composition of their milk. Sensors placed inside the body will even tell us the pH inside one of their stomachs, which could be a key indicator of any digestive problems.
We hope that all of this data will allow us to make timely decisions at the level of the individual cow, something that’s not easy to do in large herds. This “precision dairying” could help us understand how an individual cow’s activities – eating, standing, resting, milking – affects her milk production, milk quality and health.
We plan to analyze the data with the help of machine learning, a type of artificial intelligence that can find patterns in large amounts of information. The computer will compare the data against a model of how the dairy should operate under ideal conditions. Our model captures critical performance characteristics – milk quality and productivity – as well as relevant constraints, such as individual health and reproductive status.
As the dairy operates, the real-time data will allow us to assess how far away our real farm is from the ideal one. We can then combine this information with a mathematical optimization algorithm to determine how exactly we should modify or adjust the process. For example, the algorithm may suggest adjusting the type of teat drip, the nutritional content of the feed or the amount of time each cow spends feeding.
We hope that our work will allow dairy farmers across the U.S. to better manage individual cows in a group setting – not only to improve milk production, but to bolster cow health.
The UConn School of Engineering is pleased to announce that Dr. Bahram Javidi, Board of Trustees Distinguished Professor in Electrical and Computer Engineering, has been awarded the prestigious Joseph Fraunhofer Award / Robert M. Burley Prize from The Optical Society.
The award, given to one person in the country every year, was bestowed upon Javidi for his “seminal contributions to passive and active multi-dimensional imaging from nano- to micro- and macro-scales,” according to the award citation.
The award is one of a long line of accomplishments during his career, which include: Being named one of the top 160 engineers between the ages of 30-45 by the National Academy of Engineering (NAE); the Quantum Electronics and Optics Prize for Applied Aspects by the European Physical Society; the Dennis Gabor Award in Diffractive Wave Technologies from The International Society for Optics and Photonics (SPIE); the John Simon Guggenheim Foundation Fellowship; the Alexander von Humboldt Prize for senior US Scientists in all disciplines; the SPIE Technology Achievement Award; the National Science Foundation Presidential Young Investigator Award; and the George Washington University Distinguished Alumni Scholar Award.
At UConn, he has received the American Association for University Professors (AAUP) Research Excellence Award; the University of Connecticut Board Of Trustees Distinguished Professor Award; the UConn Alumni Association Excellence in Research Award; and the Chancellor’s Research Excellence Award, among others.
He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), Fellow of the American Institute for Medical and Biological Engineering (AIMBE), Fellow of the Optical Society of America (OSA), Fellow of the European Optical Society (EOS), Fellow of The International Society for Optics and Photonics (SPIE), Fellow of the Institute of Physics (IoP), and Fellow of The Society for Imaging Science and Technology (IS&T). Javidi has over 900 publications and 19 patents, some of which have been licensed by industry.
Javidi is also the director of the MOSIS Lab (Multidimensional Optical Sensing and Imaging Systems), which is focused on advancing the science and technology of imaging, by centering on the fields of optics, photonics, and computational algorithms and systems, from nano to macro scales. MOSIS works with, and finds solutions for, partners in the defense, manufacturing, healthcare, and cybersecurity industries.
Click here to learn more about the Joseph Fraunhofer Award / Robert M. Burley Prize from The Optical Society.
Dr. John Enderle and his wife, Laurie Enderle. (photo courtesy of Laurie Enderle)
Originally by: Heidi Douglas, Director of Alumni Relations, UConn School of Engineering
Professor Emeritus John D. Enderle, 65, of Ashford, CT passed away on April 2, 2018 after a long and courageous battle with pancreatic cancer. A loving husband, father, brother, friend, colleague and mentor, Dr. Enderle enjoyed a long and illustrious career as a professor and inspirational leader, admired for his unfailing dedication and support for students, a legacy honored by his family establishing the John Enderle Fund memorial scholarship.
UConn Electrical and Computer Engineering department head from 1995-1997, John was founding director of the undergraduate Biomedical Engineering program in 1997. His passion for research and advising his students are storied with commendations describing him as “the greatest professor,” having a “major influence in my life over the past 20 years,” and “always had patience to help me pursue my goals.”
John earned his B.S., M.E., and Ph.D. degrees in Biomedical Engineering, and an M.E. degree in Electrical Engineering from Rensselaer Polytechnic Institute. He worked at the National Science Foundation (NSF) and was as a professor at North Dakota State University (NDSU) prior to joining UConn.
In addition to his teaching and research, John also served in many capacities for several professional societies, was a member of the Connecticut Academy of Science and Engineering, a former Accreditation Board for Engineering and Technology (ABET) Program Evaluator for Bioengineering Programs and member of the Engineering Accreditation Commission. He was Editor of the NSF Book Series on NSF Engineering Senior Design Projects to Aid Persons with Disabilities. At the time of his death, John was working on a fourth edition of his seminal undergraduate textbook for biomedical engineering, Introduction to Biomedical Engineering.
A particularly metaphorical tribute to John celebrates his passion for gardening. A painting by former student Dr. G. Alexander Korentis depicts an espalier apple tree with John’s name followed by all his Ph.D. students’ names in an upward succession of branches. The frame bears a plaque with a quote from Warren Buffett, “Someone is sitting in the shade today because someone planted a tree a long time ago.”
John Enderle planted a tree for all of his students.
Donations may be made in memory of John Enderle to the “John Enderle Fund” in the UConn School of Engineering. Please make checks payable to: The UConn Foundation, Inc. and forward to the following address: 2390 Alumni Drive Unit 3206, Storrs, Connecticut 06269.
Ortega-Hernandez, Tshipamba, and Biron look over their EMRAX motor in the Castleman Building Machine Shop (Christopher Larosa/UConn Photo)
Twenty years ago, if you stood on a sidewalk and watched cars go by, chances are high that you would see little-to-no electric cars driving down the street. In 2017, electric car sales were higher than ever, with nearly 200,000 all-electric cars sold in the U.S. With the popularity of models from Tesla, BMW, and Chevy, consumers are starting to warm to the idea of charging their car, rather than filling it with gasoline. Because of that popularity, four Senior Design teams, including an electrical and computer engineering team featuring seniors Daryl Biron; Ernesto Ortega-Hernandez; and Alain Tshipamba, are working to complete an all-electric car for a national competition in June.
The portion of the car that Biron, Ortega-Hernandez, and Tshipamba are working on is the “heart and soul” of the vehicle—the powertrain. The sponsor of the project, the UConn Electric Motorsports, was originally formed in the spring of 2017, with the intention of getting like-minded students together to build a car that could compete in Formula North, a collegiate competition taking place during in the summer of 2018. The advisor of the team is Professor Ali Bazzi.
Biron said he originally got involved after having interest in the club in the spring 2017 semester:
“I got involved with the club personally, in the spring semester last year, and at that time there were only two electrical engineers involved, and with an electric car, you need a lot more than just two,” Biron said. “So, I was pretty much thrown onto the powertrain team, which is essentially everything that the motor controls, and I didn’t really know much, so I had to do a lot of research on my own, which became easier when Ernesto and Alain came onboard.”
The EMRAX motor provides 80 kilowatts of power, equivalent to 107 horsepower (Christopher Larosa/UConn Photo)
The car itself will have a chassis made of inch-thick aluminum honeycomb sheets, which will make it one of the lightest and most torsionally rigid chassis seen in completion, according to the UCEM website. The car will also use pieces and materials that will make the car extremely flexible and ergonomic, with components like adjustable pedals and a removable seat.
The permanent magnet motor, which the group only received recently, after a wait of a few months, was designed by EMRAX, and provides 80 kilowatts of power, equivalent to 107 horsepower. Ortega-Hernandez said they had to jump through a lot of hoops before the motor arrived at its destination:
“Initially we had some funding problems, which were later solved, but when we went to order the motor, we ran into issues, because it was coming from Europe, EMRAX required a wire transfer for payment, and they weren’t an approved UConn vendor. So, luckily, EnviroPower, the company where Daryl interns, offered to become a vendor, and then ordered the motor through their channels—but when all was said and done, the entire purchasing process took three months.”
The rest of the powertrain consists of an emDrive300H Controller from Emsiso, and several other components, which will connect to a battery apparatus being constructed by another ECE team.
Tshipamba also said that getting all the calculations fined-tuned was one of their biggest early challenges:
“Luckily, when we went to our advisor, he really helped us find out what we were doing wrong. Originally, we had issues with the simulation models, which were due to using parts from different libraries that didn’t communicate well, and were also not adjusted to our parameters regardless of tuning, so we realized that we had to create our own parts based on the mathematical model.”
But Biron said that a lot of these impediments eventually turned into worthwhile accomplishments:
“Actually, getting the motor and fixing our mathematical models was a huge breakthrough for us,” Biron said. “At one point, we were talking about ordering the motor the beginning of November, but obviously things got in the way.”
Now that they have the motor, all their components, and all of the modeling squared away, the group is looking forward to getting to work, and putting their focus towards putting all the pieces together for April and beyond.
Biron holds the EMRAX motor, which sits next to the Emsiso emDrive300H Controller. (Amanda Wright/UConn Photo)
With good intentions in mind, the Electrical and Computer Engineering Senior Design team of Daryl Biron, Ernesto Ortega-Hernandez, and Alain Tshipamba set out to build not only the powertrain of an electric car, but also realize their dream of seeing it race in the summer.
The portion of the car that Biron, Ortega-Hernandez, and Tshipamba are working on is the “heart and soul” of the vehicle—the powertrain. The sponsor of the project, the UConn Electric Motorsports Club, was originally formed in the spring of 2017, with the intention of getting like-minded students together to build a car that could compete in Formula North, a collegiate competition taking place during in the summer of 2018. The advisor of the team is Professor Ali Bazzi.
Unfortunately, due to multiple factors, Biron said that the car will not be ready for Formula North this summer:
“There’s a lot that has gone into this car, and funding has been an issue, so even this upcoming summer, when Senior Design finishes, the club probably won’t be closing in on finishing the car, but will be thinking about the overall design, and how we can make it better,” Biron said. “So, odds are it won’t be fully built until January 2019.”
When the car is officially completed, it will have a chassis made of inch-thick aluminum honeycomb sheets, which will make it one of the lightest and most torsionally rigid chassis seen in competition, according to the UCEM website. The car will also use pieces and materials that will make the car extremely flexible and ergonomic, with components like adjustable pedals and a removable seat.
The permanent magnet motor, which the group only received in February, after a wait of a few months, was designed by EMRAX, and provides 80 kilowatts of power, equivalent to 107 horsepower.
Biron holds the EMRAX motor, which sits next to the Emsiso emDrive300H Controller. (Amanda Wright/UConn Photo)
The rest of the powertrain consists of an emDrive300H Controller from Emsiso, and several other components, which will connect to a battery apparatus being constructed by another ECE team. Unlike the ordering of the motor, which was long and drawn out, Biron said the ordering of the controller was extremely easy:
“The ordering of the motor controller was much smoother, and it actually came in a couple of weeks earlier than we anticipated,” Biron said. “The only problem we encountered was that on the website, it said we would be able to use any USB-to-CAN adapter to connect to our computer to program it, but after we ordered it we found out that we needed to order a special adapter from the company.”
Biron said if they used a standard adapter, then they wouldn’t have been able to see any of the real-time graphs or data logging, so they attempted to jerry-rig it, but, unfortunately, they found out that they ultimately need to buy the special adapter.
But the powertrain, which was the team’s focus for Senior Design, will absolutely be ready to go for Senior Design Demonstration Day on April 27, according to Biron:
“We’re going to put all the parts completely together into one system before Senior Design Day, and then we’ll begin testing,” Biron said. “Then, since I don’t think we’ll be able to integrate the system into the car, we’ll have to work on a test demo for Senior Design Day, showing how we control the motor and get it to spin correctly.”
Asked about the whole year-long process, Ortega-Hernandez said that it’s definitely been a long road, but has also been a great experience:
“I think I’ll feel accomplished when I actually see that motor spin, and we confirm that we’re seeing the correct speed and torque,” Ortega-Hernandez said. “It’s been a frustrating and tough process, but we’re definitely proud of the work we’ve put in and the final result.”
The team’s final product will be presented on Senior Design Day, April 27, from 1-4 p.m., in Gampel Pavillion. To attend the event, please RSVP by clicking this link.
ECE-C2E2 Joint Seminar Spring 2018
April 10, 11am-12noon, ITE 401
Technology Overview at United Technology Research Center(UTRC): Power Electronics and Systems
Suman Dwari and Zak Sorchini
United Technology Research Center
Abstract: United Technologies Research Center (UTRC) is the innovation engine of United Technologies (UTC) and all of its business units, UTC Aerospace, Pratt and Whitney, Otis and UTC Climate Control and Security. These business units recognize UTRC as defining what’s next, and ready to solve the toughest problems. Part of that engine is collaborating with a network of partners to move the world forward. These include not only UTC’s business units, but government agencies, national laboratories, universities, commercial and aerospace companies and private organizations. In this talk, at first the overview of UTRC will be presented which will be followed by discussions on current research activities and projects in Systems and Power Electronics areas.
Short bio: Dr. Suman Dwari received Ph.D. degree from Rensselaer Polytechnic Institute, NY, in Electrical Engineering. At present, he is a Staff Research Scientist with United Technology Research Center, Hartford, USA. He has been PI or Co-PI of many government research projects for DoD, ESTCP, DOE, DARPA and contributed in the areas of distributed power systems, sustainable energy resources, high performance power electronics systems and control techniques. He has also researched on various commercial applications in the area of very high density power electronics converters using advanced materials and devices. He is author of over 30 publications and has several US patents. His current research interests include: high performance power converters, wireless power transfer, advanced machines, and advanced control of PE systems.
Dr. Zak Sorchini received M.S. and Ph.D. degrees in electrical engineering from the University of Illinois at Urbana-Champaign. He has more than 10 years of power electronics and electric machine industry experience acquired under various roles of increasing responsibility at Delphi Automotive, Caterpillar, GE Aviation and United Technologies Aerospace Systems. Since 2017 he has been with the United Technologies corporate research center as group leader for Power Electronics Systems. Mr. Sorchini holds one U.S. patent.
Illustration of network concept. A UConn researcher at the National Institute for Undersea Vehicle Technology is developing a ‘smart sensor network’ that is both energy-efficient and resilient, to track targets such as enemy submarines. (Getty Images)
A team of UConn engineers is developing an energy-efficient “smart sensor network” to track targets of interest, such as the proximity of enemy submarines or ships to Navy vessels.
The U.S. Navy currently uses underwater Intelligence, Surveillance, & Reconnaissance (ISR) sensor networks that run on full power, which can be a problem for long-term operations. The more accurate the sensor, the more power they consume.
The sensor networks currently being used could consist of several multi-modal sensor nodes, called sensor buoys, where each node acts independently and contains a diverse sensor suite, a data-processing unit, a transmitter and receiver, and a GPS device. The sensor suite can be composed of different types of sensors to detect and track targets, such as underwater microphones and active sonars.
Traditionally, these sensor nodes operate on full power, running all devices simultaneously, but the batteries that power them typically burn out within a few days of operation, just as cell phones suck up more power when running multiple operations. This causes sensing failures which, in turn, leads to holes in coverage and affects tracking performance.
This poses a challenge to the Navy, since it deploys thousands of acoustic sensor networks throughout the ocean, where battery replacement can be time-consuming or impossible.
To address the challenge, Shalabh Gupta, a UConn engineer and researcher at the National Institute for Undersea Vehicle Technology, devised the concept of a “smart sensor network” that is energy-efficient as well as resilient to failures.
Intelligent Energy-efficient Sensor Network. (Illustration by Hayley Joyal ’18 (SFA))
In a smart sensor network, sensor nodes adapt their sensing modalities based on the information about the targets’ whereabouts. Thus, the nodes around the target, such as a ship or submarine, activate their high-power sensing devices to track the target accurately, pinpointing its location, velocity, and trajectory.
On the other hand, the nodes that are located farther away from the target cycle between low-power sensing and sleep states to minimize energy consumption while still remaining aware.
Thus, if a low-power sensor detects a target, the node switches to high-power sensing to track it. Similarly, the high-power sensing devices that are tracking the target predict the target’s trajectory and alert other sensors within range of the target’s path, so that they switch to high power. Once the target has passed outside of a sensor’s range, it reverts to low-power mode.
The smart sensor networks also provide resilience. If a few nodes in the network fail, then the nodes surrounding the hole in coverage formed by the failed nodes jointly optimize to expand their sensing ranges to cover the gap.
“These networks have to contain built-in, distributed intelligence,” says Gupta, an assistant professor of electrical and computer engineering.
With this advance, crews on ships and submarines will be able to track enemy watercraft with batteries that last about 60 to 90 percent longer, Gupta says.
Gupta’s lab has prototypes of the sensors for ground use, and has been talking with Navy personnel about using them for the underwater acoustic sensor network. He is currently seeking funding to build underwater sensors.
There’s a great scene in the movie “Iron Man” where Robert Downey Jr.’s character Tony Stark (aka Iron Man) is crawling across his lab, desperately trying to reach the small arc reactor he needs to keep his heart beating and stay alive.
Weakened by a run-in with arch villain Obadiah Stane, Stark can’t reach the gizmo where it sits on a tabletop. Defeated, he rolls onto his back, exhausted and pondering his inevitable doom.
But the very moment that we think our intrepid hero’s a goner, a metallic hand appears at Stark’s shoulder, holding the lifesaving device. “Good boy,” Stark says weakly as he takes the device from his robot assistant, Dum-E.
And just like that, our hero is saved.
From the dutiful shuffling of C-3PO to the terrorizing menace of The Terminator, Hollywood has made millions tantalizing audiences with far-out robot technology. Scenes like the one in “Iron Man” make for good entertainment, but they also are based, to some degree, in reality.
Dum-E’s interaction with Stark is called collaborative robotics, where robots with advanced artificial intelligence, or A.I., not only work alongside us humans but also are able to anticipate our actions and even grasp what we need.
Collaborative robotics represents the frontier of robotics and A.I. research today. And it’s happening at UConn.
Three thousand miles away from the klieg lights of Hollywood, Ashwin Dani, director of UConn’s Robotics and Controls Lab, or RCL, stands in the stark fluorescent light of his Storrs office staring at a whiteboard covered in hastily scrawled diagrams and mathematical equations.
Here, in the seemingly unintelligible mishmash of numbers and figures, are the underlying mathematical processes that are the lifeblood of collaborative robotics.
If robots are going to interact safely and appropriately with humans in homes and factories across the country, they need to learn how to adapt to the constantly changing world around them, says Dani, a member of UConn’s electrical and computer engineering faculty.
“We’re trying to move toward human intelligence. We’re still far from where we want to be, but we’re definitely making robots smarter,” he explains.
All of the subconscious observations and moves we humans take for granted when we interact with others and travel through the world have to be taught to a robotic machine.
When you think about it, simply getting a robot to pick up a cup of water (without crushing it) and move it to another location (without spilling its contents or knocking things over) is an extraordinarily complex task. It requires visual acuity, a knowledge of physics, fine motor skills, and a basic understanding of what a cup looks like and how it is used.
“We’re teaching robots concepts about very specific situations,” says Harish Ravichandar, the senior Ph.D. student in Dani’s lab and a specialist in human-robot collaboration. “Say you’re teaching a robot to move a cup. Moving it once is easy. But what if the cup is shifted, say, 12 inches to the left? If you ask the robot to pick up the cup and the robot simply repeats its initial movement, the cup is no longer there.”
Repetitive programs that work so well for assembly-line robots are old school. A collaborative robot has to be able to constantly process new information coming in through its sensors and quickly determine what it needs to do to safely and efficiently complete a task. If that robot is part of an assembly line, the line has to shut down and the robot has to be reprogrammed to account for the change, an inefficient process that costs manufacturers money. Hence the thinking robot this team is trying to create.
While the internet is filled with mesmerizing videos of robots doing backflips, jumping over obstacles, and even making paper airplanes, the UConn team’s effort at controlling robots through advanced artificial intelligence is far less flashy but potentially far more important.
Every move the UConn team wants its test robot to make starts here, says Dani, with control theory, engineering, whiteboards, and math.
“We’re writing algorithms and applying different aspects of control theory to take robot intelligence to a higher level,” says Ravichandar. “Rather than programming the robot to make one single movement, we are teaching the robot that it has an objective — reaching for and grabbing the cup. If we succeed, the robot should be able to make whatever movements are necessary to complete that task no matter where the cup is. When it can do that, now the robot has learned the task of picking something up and moving it somewhere else. That’s a very big step.”
While most of us are familiar with the robots of science fiction, actual robots have existed for centuries. Leonardo da Vinci wowed friends at a Milan pageant in 1495 when he unveiled a robotic knight that could sit, stand, lift its visor, and move its arms. It was a marvel of advanced engineering, using an elaborate pulley and cable system and a controller in its chest to manipulate and power its movements.
But it wasn’t until Connecticut’s own Joseph Engelberger introduced the first industrial robotic arm, the 2,700-pound Unimate #001, in 1961 that robots became a staple in modern manufacturing.
Unimates were first called into service in the automobile industry, and today, automobile manufacturers like BMW continue to be progressive leaders using robots on the factory floor. At a BMW plant in Spartanburg, South Carolina, for example, collaborative robots help glue down insulation and water barriers on vehicle doors while their human counterparts hold the material in place.
The advent of high-end sensors, better microprocessors, and cheaper and easily programmable industrial robots is transforming industry today, with many mid-size and smaller companies considering automation and the use of collaborative robots.
Worldwide use of industrial robots is expected to increase from about 1.8 million units at the end of 2016 to 3 million units by 2020, according to the International Federation of Robotics. China, South Korea, and Japan use the most industrial robots, followed by the United States and Germany.
Anticipating further growth in industrial robotics, the Obama administration created the national Advanced Robotics Manufacturing Institute, bringing together the resources of private industry, academia, and government to spark innovations and new technologies in the fields of robotics and artificial intelligence. UConn’s Robotics and Controls Lab is a member of that initiative, along with the United Technologies Research Center, UTC Aerospace Systems, and ABB US Corporate Research in Connecticut.
Manufacturers see real value in integrating collaborative robots into their production lines. The biggest concern, clearly, is safety.
There have been 39 incidents of robot-related injuries or deaths in the U.S. since 1984, according to the federal Occupational Safety and Health Administration. To be fair, none of those incidents involved collaborative robots and all of them were later attributed to human error or engineering issues.
The first human known to have been killed by a robot was Robert Williams in 1979. Williams died when he got tired of waiting for a part and climbed into a robot’s work zone in a storage area in a Ford Motor plant in Flat Rock, Michigan. He was struck on the head by the robot’s arm and died instantly. The most recent incident happened in January 2017, when an employee at a California plastics plant entered a robot’s workspace to tighten a loose hose and had his sternum fractured when the robot’s arm suddenly swung into action.
“When you have a human and a robot trying to do a joint task, the first thing you need to think about of course is safety,” says Dani. “In our lab, we use sensors that, along with our algorithms, not only allow the robot to figure out where the human is but also allow it to predict where the human might be a few seconds later.”
One way to do that is to teach robots the same assembly steps taught to their human counterparts. If the robot knows the order of the assembly process, it can anticipate its human partners’ next moves, thereby reducing the possibility of an incident, Dani says. Knowing the process would also allow robots to help humans assemble things more quickly if they can anticipate an upcoming step and prepare a part for assembly, thus improving factory efficiency.
“Humans are constantly observing and predicting each other’s movements. We do it subconsciously,” says Ravichandar. “The idea is to have robots do the same thing. If the robot sees its human partner performing one step in an assembly process, it will automatically move on to prepare for the next step.”
Which brings us back to the whiteboards. And the math.
Failure is always an option. But when the math finally works, Ravichandar says, the success is exhilarating.
“Once you have the math figured out, it’s the best feeling because you know what you want the robot to do is going to work,” Ravichandar says with an excited smile.
“Implementing it is a whole other challenge,” he adds quickly, his passion for his work undiminished. “Things never work the first time. You have to constantly debug the code. But when you finally see the robot move, it is great because you know you have translated this abstract mathematical model into reality and actually made a machine move. It doesn’t get any better than that.”
With an eye on developing collaborative robotics that will assist with manufacturing, Dani and his team spent part of the past year teaching their lab’s test robot to identify tools laid out on a table so it can differentiate between a screwdriver, for example, and a crescent wrench, even when the tools’ initial positions are rearranged. Ultimately, they hope to craft algorithms that will help the robot work closely with a human counterpart on basic assembly tasks.
Another member of the team, Ph.D. candidate Gang Yao, is developing programs that help a robot track objects it sees with its visual sensors. Again, things we humans take for granted, such as being able to tell the difference between a bird and a drone flying above the trees, a robot has to learn.
Building advanced artificial intelligence doesn’t happen overnight. Ravichandar has been working on his projects for more than three years. It is, as they say, a process. Yet the team has learned to appreciate even the smallest of advances, and late last year, he flew to California to present some of the lab’s work to an interested team at Google.
“C-3PO is a protocol droid with general artificial intelligence,” says Ravichandar. “What we are working on is known as narrow artificial intelligence. We are developing skills for the robot one task at a time and designing algorithms that guarantee that whatever obstacles or challenges the robot encounters, it will always try to figure out a safe way to complete its given task as efficiently as it can. With generalized intelligence, a robot brings many levels of specific intelligence together and can access those skills quickly on demand. We’re not at that point yet. But we are at a point where we can teach a robot a lot of small things.”
Inevitably, as robots gain more and more human characteristics, people tend to start worrying about how much influence robots may have on our future.
Robots certainly aren’t going away. Saudi Arabia recently granted a robot named Sophia citizenship. Tesla’s Elon Musk and Deep Mind’s Mustafa Suleyman are currently leading a group of scientists calling for a ban on autonomous weapons, out of concern for the eventual development of robots designed primarily to kill.
Although it doesn’t apply directly to their current research, Dani and Ravichandar say they are well aware of the ethical concerns surrounding robots with advanced artificial intelligence.
Ravichandar says the problem is known in the field as “value alignment,” where developers try to make sure the robot’s core values are aligned with those of humans. One way of doing that, Ravichandar says, is to create a safety mechanism, such as making sure the robot always understands that the best solution it can come up with for a problem might not always be the best answer.
“The time is coming when we will need to have consensus on how to regulate this,” says Ravichandar. “Like any technology, you need to have regulations. But I think it’s absolutely visionary to inject humility into robots, and that’s happening now.”
That’s good news for the rest of us, because killer robots certainly are not the droids we’re looking for.
Over 350 million people in the United States depend on the reliability and consistency of the 450,000 miles of high-voltage lines that form the U.S. power grid to do important daily tasks. With stronger weather events and an increasing number of cyber-attacks, reliable safeguards and technologies are needed to protect this very important utility. The Power and Energy Systems Laboratory, run by Dr. Peng Zhang, F.L. Castleman Associate Professor of Electrical and Computer Engineering and his graduate students, aims at tackling these important issues.
The lab, which focuses on smart grid technology, microgrids, and sustainable energy, has worked on several crucial projects over the past several years, including a dedicated approach to networking the grid system, determining risk assessment models for unintentional islanding of power generators, using ocean waves to generate a sustainable power source, and many other related areas of research.
The lab’s most recent research, which focuses on national infrastructure, is looking to enhance the connectedness of the fractured U.S. grid system:
“The goal of our research is to make our nation’s energy infrastructure resilient, reliable, secure, and sustainable,” Zhang said. “One of our main areas of focus now is large systems power stability, which is important, because there are no tools available to really assess and predict the status of the system. This tool is highly needed, especially for connected systems.”
That tool, which is being developed from a $1.05 million Department of Energy grant, is being worked on by Zhang and Ph.D. student Yan Li. The idea for the grant was inspired by the learnings Zhang and his students gathered from studying the tools used by local utility company, Eversource Energy:
“The whole U.S. and Canadian grid are connected together, and it’s a huge system, so it’s therefore very difficult to monitor, assess and control its stability ,” Zhang said. “If you look at Connecticut, companies like Eversource, for the most part, use off-line tools which run many scenarios and only look at snapshots of the system, but that kind of work is not great for analysis, it needs to be monitored and assessed in real-time.”
In particular, Li and Zhang will be forming a formal theory with mathematical rigor, which will be established for computing the bounds of all possible trajectories and estimating the stability margin for the entire system, including the integrated transmission and distribution network.
Furthermore, a new open-source tool via reachable set computations will be developed for real-time dynamic analysis and stability margin calculations. It will be applicable for not only forecasting and monitoring grid performance, but also formally verifying various resiliency enhancement strategies, such as new schemes for system integrity protection and automation to adapt to this evolution of electric networks.
Zhang and his students are also making significant contributions to knowledge advancement in the state of Connecticut and the region, with their work at the Eversource Energy Center at UConn, which conducts research related to advancing energy technology, as well as performs significant consulting work with Eversource Energy.
Zhang and his students have specifically performed work on research related to unintentional islanding, which is a phenomenon in which a distributed generator continues to be electrified and running, even when the electrical grid surrounding it is no longer active. Traditional methods of detection can often be fooled to think that grid conditions are normal, especially when multiple power generation devices are connected to the same line.
This kind of scenario is very dangerous to field workers, as the lack of knowledge could cause them to be electrocuted. Zhang said that the research that’s currently being done by himself and his team addresses the creation a risk assessment model to safely avoid danger:
“This kind of research is using machine learning to predict risk in the non-detection zone,” Zhang said. “By non-detection we mean that when an island occurs, there are certain scenarios where the utility company, but with this new risk assessment model, we’ll be able to accurately predict when this scenario is likely to occur.”
Most importantly though, Zhang is happy that the graduate students that he is mentoring, and providing hands-on opportunities to, are getting the necessary experience needed to launch their careers in research and academia.
Ph.D. student Taofeek Orekan, one of the members of the lab, is one of the students that will be using that experience, as he looks for post-grad opportunities in the next few months:
“This is a great lab to start off in,” Orekan said. “I know that any lab that I launch during my career in academia will absolutely be an extension of this lab.”
For more information on the lab, visit http://power.engr.uconn.edu.