CPS Events
CPSRC Seminar Series: Hybrid Systems Determined by Set-Value Dynamics and Random-in-Time Jumps with Control Applications
Abstract:
We discuss modeling and analysis for a class of stochastic hybrid systems with random-in-time jumps. In particular, we present natural Lyapunov-based sufficient conditions for asymptotic stability in probability and for an alternative property called recurrence.
We consider models that admit not necessarily unique flows or jumps. In this setting, we emphasize that the solutions must depend causally on the random inputs that affect solutions. Otherwise, Lyapunov functions may fail to be sufficient for establishing stability properties. After discussing general models, we show how the analysis results can be used in two different settings. The first application involves certifying the behavior of control systems where the control signal is updated at random times; this situation is in contrast to the situation where control signal updates are triggered by events or are periodic. The second application involves certifying the behavior of algorithms that set tolls in societal systems to induce optimal behavior, in the setting where actors in the society exhibit random behavior.
Bio:
Andrew R. Teel received his A.B. degree in Engineering Sciences from Dartmouth College in Hanover, New Hampshire, in 1987, and his M.S. and Ph.D. degrees in Electrical Engineering from the University of California, Berkeley, in 1989 and 1992, respectively. After receiving his Ph.D., he was a postdoctoral fellow at the Ecole des Mines de Paris in Fontainebleau, France. In 1992 he joined the faculty of the Electrical Engineering Department at the University of Minnesota, where he was an assistant professor until 1997. Subsequently, he joined the faculty of the Electrical and Computer Engineering Department at the University of California, Santa Barbara, where he is currently a Distinguished Professor and director of the Center for Control, Dynamical systems, and Computation. His research interests are in nonlinear and hybrid dynamical systems, with a focus on stability analysis and control design. He has received NSF Research Initiation and CAREER Awards, the 1998 IEEE Leon K. Kirchmayer Prize Paper Award, the 1998 George S. Axelby Outstanding Paper Award, and was the recipient of the first SIAM Control and Systems Theory Prize in 1998. He was the recipient of the 1999 Donald P. Eckman Award and the 2001 O. Hugo Schuck Best Paper Award, both given by the American Automatic Control Council, and also received the 2010 IEEE Control Systems Magazine Outstanding Paper Award. In 2016, he received the Certificate of Excellent Achievements from the IFAC Technical Committee on Nonlinear Control Systems. He is Editor-in-Chief for Automatica, and a Fellow of the IEEE and of IFAC.
From Hummingbirds to Honeybees: Algorithms for Agile Micro Aerial Vehicles with On-Board Perception
Abstract
Agile micro aerial vehicles will have a massive societal impact over the next decades, creating novel opportunities for large-scale precision agriculture, fast delivery of medical supplies, and disaster response, and providing new perspectives on environmental monitoring and artificial pollination. This future requires the design of lightweight and robust perception algorithms, which interpret sensor data into a coherent world representation, in the face of measurement noise and outliers, and enable on-board situational awareness and decision-making. In this talk, I present my work on lightweight and robust robot perception. I start by discussing an algorithm for fast visual-inertial navigation, which estimates the motion of the robot from visual and inertial cues, and demonstrate its use for agile flight of micro aerial vehicles. Then, I focus on robustness and show that fundamental insights from optimization and Riemannian geometry lead to the design of estimation techniques that are provably robust to large noise, providing the first certifiably correct algorithm for localization and mapping. I also discuss the challenges connected to scaling down perception to nano and pico aerial vehicles, where sensing and computation are subject to strict payload and power constraints. I argue that enabling autonomy on miniaturized platforms requires a paradigm shift in perception, sensing, and communication, and discuss how we can draw inspiration from nature in designing the next generation of flying robots.
Bio
Luca Carlone is a research scientist in the Laboratory for Information and Decision Systems at the Massachusetts Institute of Technology. Before joining MIT, he was a postdoctoral fellow at Georgia Tech (2013-2015), and a visiting researcher at the University of California Santa Barbara (2011). He got his Ph.D. from the Polytechnic University of Turin, Italy, in 2012. His research interests include nonlinear estimation, numerical and distributed optimization, computer vision and probabilistic inference applied to sensing, perception, and control of single and multi robot systems. He published more than 60 papers on international journals and conferences, including a best paper award finalist at RSS 2015 and a best paper award winner at WAFR 2016.
Safe by Design: Manipulation Strategies for Soft, Assistive and Medical Robots
Abstract:
In recent years, the nascent field of soft robotics has emerged as an exciting area of research that stands to revolutionize our interaction with machines. Soft robots possess many attributes that are difficult, if not impossible, to achieve with conventional robots composed of rigid materials. Yet, despite recent advances, soft robots are require complete re-thinking of control and contain many rigid components, thus they remain either tethered to remote hardware, or they are soft-rigid hybrid systems. Recent work has explored possible soft analogs for these standard rigid components. While new challenges arise from the incorporation of these new components, new possibilities arise as well. Elimination of rigid components allows the shrinking of length scales and development of new form factors impossible with traditional motors, batteries, and electronic controllers. We look at the use of a novel soft controller, alternate fuel source, and soft actuator and explore possible form factors and applications. These components are brought together via a design and rapid fabrication approach, which lays the foundation for a new class of completely soft, autonomous robots.
Bio:
Michael Wehner received his Ph.D. in Mechanical Engineering at the University of California at Berkeley, where his research focused on human machine interaction and the development of an exoskeleton system to reduce back forces during lifting. In 2011, Michael returned to academia as a post-doctoral fellow in the Harvard Microrobotics lab with Robert Wood. In this group, he explored soft alternatives to conventional rigid orthotics for disabled children, and developed the first engineered soft exosuit. Working with the Jennifer Lewis and George Whitesides labs, he also developed Octobot, the first entirely soft untethered robot, replacing rigid battery, pump, and controller with monopropellant fluid power and microfluidic soft logic. Michael Wehner has also worked extensively in industry in various engineering, consultant, and management roles in the semiconductor capital equipment, medical device, and consumer goods fields.
