Tag: Past

November 01, 2019 – 11:15 AM
TSRB Auditorium

Abstract

Autonomous systems offer the promise of providing greater safety and access. However, this positive impact will only be achieved if the underlying algorithms that control such systems can be certified to behave robustly. This talk will describe a pair of techniques grounded in infinite dimensional optimization to address this challenge. The first technique, which is called Reachability-based Trajectory Design, constructs a parameterized representation of the forward reachable set, which it then uses in concert with predictions to enable real-time, certified, collision checking. This approach, which is guaranteed to generate not-at-fault behavior, is demonstrated across a variety of different real-world platforms. The second technique, is a polynomial optimization method that allows one to compute globally optimal solutions in real-time in the presence of hundreds of constraints. The utility of this approach is validated on a real-time trajectory design task for an autonomous ground vehicle.

Biography

Ram Vasudevan is an assistant professor in Mechanical Engineering and the Robotics Institute at the University of Michigan. He received a BS in Electrical Engineering and Computer Sciences, an MS degree in Electrical Engineering, and a PhD in Electrical Engineering all from the University of California, Berkeley. He is a recipient of the NSF CAREER Award and the ONR Young Investigator Award. His work has received best paper awards at the IEEE Conference on Robotics and Automation, the ASME Dynamics Systems and Controls Conference, and IEEE OCEANS Conference.

October 25, 2019 – 11:15 AM
TSRB Auditorium

Abstract

In the area of multi-robot systems, an understanding of collaboration has been developed and is showing great promise in real-world applications including self-driving cars, industrial automation, precision agriculture, and disaster response. However, existing multi-robot methods still leave much to be desired. In this talk, I will argue for innovation in several areas of multi-robot coordination that are often seen in real-world applications: (1) intermittent monitoring of slowly-evolving spatiotemporal processes on large scales; (2) robots that interact asymmetrically due to imperfect sensors and interference; (3) robots that collaborate as an informed response to a team objective and the environment; and (4) robots that interact with human experts. Towards this vision, I will first discuss methods we have developed that generate temporal deployment plans for multi-robot teams that balance the cost of deployment with the quality of information gathered about some evolving process of interest. In particular, I will outline a combinatorial optimization approach to this “intermittent deployment” problem, yielding greedy solutions with bounded suboptimality. Next, I will detail recent results in coordinated motion control for robots interacting with sensors exhibiting some limited field of view (FOV), along with experimental results in outdoor environments. Finally, I will outline planning problems we have developed that generate temporal interaction structures for multi-robot teams along with trajectories that complement the efforts of humans experts in a search and rescue domain. Throughout the talk I will show examples of the projects at the Coordination at Scale Lab (CAS Lab) at Virginia Tech that motivate the above methods, and conclude by discussing future directions in the area of multi-robot systems.

Biography

Ryan K. Williams received the B.S. degree in computer engineering from Virginia Polytechnic Institute and State University in 2005, and the Ph.D. degree from the University of Southern California in 2014. He is currently an Assistant Professor in the Electrical and Computer Engineering Department at Virginia Tech where he runs the Laboratory for Coordination at Scale (CAS Lab). His current research interests include control, cooperation, and intelligence in distributed multi-agent systems, topological methods in cooperative phenomena, and distributed algorithms for optimization, estimation, inference, and learning. Williams is a Viterbi Fellowship recipient, has been awarded the NSF CISE Research Initiation Initiative grant for young investigators, is a Junior Faculty Award Recipient at Virginia Tech, is a best multi-robot paper finalist at the 2017 IEEE International Conference on Robotics and Automation, and has been featured by various news outlets, including the L.A. Times.

October 11, 2019 – 11:15 AM
TSRB 523A

Abstract

Low gain feedback refers to a family of stabilizing state feedback gains that are parameterized in a scalar, referred to low gain parameter, and go to zero as the low gain parameter decreases to zero. Low gain feedback was initially proposed to achieve semi- global stabilization of linear systems subject to input saturation, and later found its other applications in the stabilization of nonlinear systems and linear systems with input delays. In this talk, we discuss the concept of low gain feedback, its properties, its design methods and its applications in constrained control, nonlinear stabilization and control of time-delay systems.

Biography

Zongli Lin is the Ferman W. Perry Professor in the School of Engineering and Applied Science and a Professor of Electrical and Computer Engineering at University of Virginia. He received his B.S. degree in mathematics and computer science from Xiamen University, Xiamen, China, in 1983, his Master of Engineering degree in automatic control from Chinese Academy of Space Technology, Beijing, China, in 1989, and his Ph.D. degree in electrical and computer engineering from Washington State University, Pullman, Washington, in 1994. His current research interests include nonlinear control, robust control, time delay systems, and control applications. He was an Associate Editor of the IEEE Transactions on Automatic Control (2001-2003), IEEE/ASME Transactions on Mechatronics (2006-2009) and IEEE Control Systems Magazine (2005-2012). He was elected a member of the Board of Governors of the IEEE Control Systems Society (2008- 2010, 2019-2021) and chaired the IEEE Control Systems Society Technical Committee on Nonlinear Systems and Control (2013-2015). He has served on the operating committees several conferences and was the program chair of the 2018 American Control Conference and a general chair of the 13th and 16th International Symposium on Magnetic Bearings (2012, 2018). He currently serves on the editorial boards of several journals and book series, including Automatica, Systems & Control Letters, Science China Information Sciences, and Springer/Birkhauser book series Control Engineering. He is a Fellow of IEEE, IFAC, and AAAS, the American Association for the Advancement of Science.

October 04, 2019 – 11:15 AM
TSRB Auditorium

Abstract

Variable renewable energy integration and resilience to extreme events motivate the need for flexible resources in electric power systems. Distributed energy resources (DERs), such as electric vehicles and thermostatically controlled loads, provide an intriguing set of distributed assets to provide flexible services in power systems. However, leveraging populations of DERs are challenging because they are (i) large-scale, and (ii) involve discrete-valued control. This talk addresses modeling, estimation, and control for aggregations of DERs. Specifically, the talk is divided into two parts. First, we discuss a partial differential equation (PDE) approach to modeling and estimating aggregations of DERs. Second, we discuss a novel class of methods for controlling DER populations that are mathematically formulated as large-scale mixed integer programs. We call this class of methods “Hopfield methods”.

Biography

Scott Moura is an Associate Professor in Civil & Environmental Engineering and Director of the Energy, Controls, & Applications Lab (eCAL) at the University of California, Berkeley. He is also a faculty member at the Tsinghua-Berkeley Shenzhen Institute. He received the B.S. degree from the University of California, Berkeley, CA, USA, and the M.S. and Ph.D. degrees from the University of Michigan, Ann Arbor, in 2006, 2008, and 2011, respectively, all in mechanical engineering. From 2011 to 2013, he was a Post-Doctoral Fellow at the Cymer Center for Control Systems and Dynamics, University of California, San Diego. In 2013, he was a Visiting Researcher at the Centre Automatique et Systèmes, MINES ParisTech, Paris, France. His research interests include control, optimization, and machine learning for batteries, electrified vehicles, and distributed energy resources.

Dr. Moura is a recipient of the National Science Foundation (NSF) CAREER Award, Carol D. Soc Distinguished Graduate Student Mentor Award, the Hellman Fellowship, the O. Hugo Shuck Best Paper Award, the ACC Best Student Paper Award (as advisor), the ACC and ASME Dynamic Systems and Control Conference Best Student Paper Finalist (as student and advisor), the UC Presidential Postdoctoral Fellowship, the NSF Graduate Research Fellowship, the University of Michigan Distinguished ProQuest Dissertation Honorable Mention, the University of Michigan Rackham Merit Fellowship, and the College of Engineering Distinguished Leadership Award.

September 27, 2019 – 11:15 AM
TSRB Auditorium

Abstract

This talk presents a novel geometric method to investigate whether a multirotor unmanned aerial vehicle (UAV) can achieve stable hovering, i.e., hoverability. The hoverability is indispensable for a multirotor UAV to conduct its task safely, and should be satisfied even when a rotor fails to prevent an accident. The proposed geometric method reveals the relationship between the position of the center of mass (CoM) and the rotor placement of a multirotor UAV to satisfy the hoverability, which can be applied to a multirotor UAV with any number and position of rotors. This talk also provides its application to investigation of a robust structure against rotor failures. Furthermore, a quantitative measure of the hoverability is newly presented based on the proposed analysis method. It enables us to design a multirotor UAV with an optimal structure in the sense of the hoverability. Finally, experimental validation is performed by using a hexrotor UAV whose CoM position is intentionally shifted.

Biography

Tatsuya Ibuki is an Assistant Professor at the Department of Systems and Control Engineering of Tokyo Institute of Technology, Japan. He received his Ph.D.Eng. degree from Tokyo Tech in 2013. He was a research fellow of the Japan Society for the Promotion of Science from 2012 to 2013, and is currently a visiting scholar at the School of Electrical and Computer Engineering of Georgia Institute of Technology. His research interests include cooperative control of robotic networks, multirotor UAV design and control, and vision-based estimation and control. He received some awards from the Society of Instrument and Control Engineers in Japan on these topics.