Motion coordination is a remarkable phenomenon in biological systems and an extremely useful tool in man-made groups of vehicles, mobile sensors, and embedded robotic systems. Just like animals do, groups of mobile autonomous agents need the ability to deploy over a region, assume a specified pattern, rendezvous at a common point, or jointly move in a synchronized manner. This talk illustrates ways in which systems and control theory helps us design autonomous and reliable robotic networks. We present some recently developed theoretical tools for modeling, analysis, and design of motion coordination algorithms. Numerous examples from deployment, aggregation, and consensus scenarios help illustrate the technical approach. In our exposition, we pay special attention to the characterization of the correctness and the evaluation of the performance of coordination algorithms.
Jorge Cortes is an Associate Professor with the Department of Mechanical and Aerospace Engineering at the University of California, San Diego. He received the Licenciatura degree in mathematics from the Universidad de Zaragoza, Spain, in 1997, and the Ph.D. degree in engineering mathematics from the Universidad Carlos III de Madrid, Spain, in 2001. He held postdoctoral positions at the University of Twente, The Netherlands, and at the University of Illinois at Urbana-Champaign, USA. He was an Assistant Professor with the Department of Applied Mathematics and Statistics at the University of California, Santa Cruz from 2004 to 2007. He is the author of "Geometric, Control and Numerical Aspects of Nonholonomic Systems" (New York: Springer-Verlag, 2002) and co-author of "Distributed Control of Robotic Networks" (Princeton: Princeton University Press, 2009). He received a NSF CAREER award in 2006 and was the recipient of the 2006 Spanish Society of Applied Mathematics Young Researcher Prize. He has co-authored papers that have won the 2008 IEEE Control Systems Outstanding Paper Award, the 2009 SIAM Review SIGEST selection from the SIAM Journal on Control and Optimization, and the 2012 O. Hugo Schuck Best Paper Award in the Theory category.
He served in the Editorial Board of European Journal of Control (2006-2009), IEEE Transactions on Automatic Control (2010-2012), and Systems and Control Letters (2009-2012). He currently serves in the Editorial Boards of IEEE Control Systems Magazine, Journal of Geometric Mechanics, and SIAM Journal on Control and Optimization.
Distinguished Lecture Program
Talk Title: Models, Algorithms, and Tools for Motion Coordination
Talk Title: Discontinuous Dynamical Systems - a Tutorial
Discontinuous dynamical systems, i.e., systems whose associated vector field is a discontinuous function of the state, arise in a large number of applications, including optimal control, nonsmooth mechanics, and robotic manipulation. Independently of the particular application, one always faces similar questions when dealing with them. This talk focuses on two important issues for discontinuous systems: the notion of solution and the stability analysis. We begin by introducing some of the most commonly used notions of solutions defined in the literature, discussing existence and uniqueness results, and examining various examples. Regarding the analysis of stability of discontinuous systems, we present useful notions and tools from nonsmooth analysis, including generalized gradients of locally Lipschitz functions and proximal subdifferentials of lower semicontinuous functions. Building on these notions, we establish monotonic properties of candidate Lyapunov functions along the solutions. These results are key in providing suitable generalizations of Lyapunov stability theorems and the LaSalle Invariance Principle. We illustrate the applicability of these results in several examples from mechanics, cooperative control, and distributed dynamical systems.
Talk Title: Spatial Statistics and Distributed Estimation by Robotic Sensor Networks
Networks of environmental sensors are playing an increasingly important role in scientific studies of the ocean, rivers, and the atmosphere. Robotic sensors can improve the efficiency of data collection, adapt to changes in the environment, and provide a robust response to individual failures. Complex statistical techniques come into play in the analysis of spatial environmental processes. Consequently, the operation of robotic sensors must be driven by statistically-aware algorithms that make the most of the network capabilities for data collection and fusion. At the same time, such algorithms need to be distributed and scalable to make robotic networks capable of operating in an autonomous and robust fashion. The combination of these two objectives, complex statistical modeling and distributed coordination, presents grand technical challenges: traditional statistical modeling and inference assume full availability of all measurements and central computation. While the availability of data at a central location is certainly a desirable property, the paradigm for distributed motion coordination builds on partial, fragmented, and online information. In this talk, we present recent progress at bridging the gap between sophisticated statistical modeling and distributed motion coordination. We examine two problems for a network of robotic sensors: how to construct local representations of dynamic spatial processes in a distributed way and how to cooperatively optimize data collection for uncertainty minimization.