CPS Events
Von Neumann Meets the Nexus Problem
Abstract
Recently, there has been in the control community an increasing interest in studying large- scale distributed systems (LSDS). One of this LSDS is what the World Economic Forum defined in 2008 as the nexus problem, which arises in today’s major cities. In order to address the energy-water-food-transportation nexus, which includes different cyber- physical systems, networks, and management areas, a systemic approach could be useful. One way to approach this type of problems is to use game-theoretical methods. Game theory shares some common points with control systems problems, in particular of distributed topology, where the interconnection of different elements (agents) leads to a global behavior depending on the local interaction of these agents. The aim of this talk is to present and discuss how the nexus problem has been modeled in a microcity, and how the energy-transportation nexus could be address from a population games and evolutionary dynamics perspective.
Bio
Nicanor Quijano (IEEE Senior Member) received his B.S. degree in Electronics Engineering from Pontificia Universidad Javeriana (PUJ), Bogotá, Colombia, in 1999. He received the M.S. and PhD degrees in Electrical and Computer Engineering from The Ohio State University, in 2002 and 2006, respectively. In 2007, he joined the Electrical and Electronics Engineering Department, Universidad de los Andes (UAndes), Bogotá, Colombia as an Assistant Professor. He is currently a Full Professor, the director of the research group in control and automation systems (GIAP, UAndes), and an associate editor for the IEEE Transactions on Control Systems Technology, the Journal of Modern Power Systems and Clean Energy, and Energy Systems. He has been a member of the Board of Governors of the IEEE Control Systems Society (CSS) for the 2014 period, and he was the chair of the IEEE CSS, Colombia for the 2011-2013 period. He has published more than 100 scientific papers (journal papers, international conference papers, book chapters), he has co- advised the best European PhD thesis in the control systems area in 2017, and he is the co-author of the best paper of the ISA Transactions, 2018. In 2021, he obtained the Experienced Research Award from the School of Engineering, UAndes. Currently his research interests include hierarchical and distributed network optimization methods for control using learning, bio-inspired, and game-theoretical techniques for dynamic resource allocation problems, especially those in energy, water, agriculture, and transportation.
Robotics Motion Planning and Learning
Abstract
A very successful approach for finding the motions for a robot to move from some initial configuration to a goal configuration is sampling-based motion planning. In this approach, the planner performs a systematic exploration, through sampling, of the configuraton space in order to build a connectivity map that the robot can follow. These planners trade completeness for probabilistic completeness, which means that given enough time, they will find existing paths with high probability, although they are not able to tell when there is no such path. In this talk, I will discuss how, while exploring the configuration space, sampling based planners are also able to extract features that can be useful to produce better maps. I will also briefly talk about my work on multi-agent planning and autonomous navigation.
Bio
Marco Morales Aguirre is a Teaching Associate Professor at the Department of Computer Science at the University of Illinois Urbana-Champaign and he is currently on leave as an Associate Professor at the Department of Computer Science at Instituto Tecnológico Autónomo de México (ITAM). He has also been a Visiting Professor at Texas A&M University and a Lecturer at Universidad Nacional Autónoma de México (UNAM). He holds a Ph.D. in Computer Science from Texas A&M University, a M.S. in Electrical Engineering and a B.S. in Computer Engineering from UNAM. His main research interests are in motion planning and control for autonomous robots, artificial intelligence, machine learning, and computational geometry.
ECE Seminar Co-hosted by CPSRC: Tightly Integrated Design and Control of Aerial Robots
Abstract
Aerial robots are present in a variety of domains. In this talk I will discuss some approaches to expanding their capabilities through novel design; specifically viewing the design through the lens of the control problem. I will discuss vehicle designs that achieve greater disturbance rejection through angular momentum, that can morph mid-flight to traverse obstacles, and that can survive and exploit collisions with the environment. Two design approaches to (partially) overcoming the inherent energy storage constraints for aerial vehicles will also be discussed, motivated by challenges in creating useful urban air mobility vehicles ("air taxis"). Time permitting I will also share some work on motion planning for rapid flight through unstructured environments.
Bio
Mark W. Mueller is an assistant professor of Mechanical Engineering at UC Berkeley, whose research focuses on the design and control of aerial robots. He joined the mechanical engineering department at UC Berkeley in September 2016, after spending some time at Verity Studios working on a drone entertainment system, installed in the biggest theater on New York's broadway. He completed his PhD studies at the ETH Zurich in Switzerland in 2015 under the supervision of Prof. Raffaello D'Andrea, and received an MSc there in 2011. He received a bachelors degree in mechanical engineering from the University of Pretoria in South Africa.
ECE Seminar Co-hosted by CPSRC: Vision-Based Tracking with Small UAVs
Abstract
This talk will describe our current work on vision based autonomous target tracking and following using small UAVs. We will present a new multiple target tracking algorithm that is based on the random sample consensus (RANSAC) algorithm that is widely used in computer vision. A recursive version of the RANSAC algorithm will be discussed, and its extension to tracking multiple dynamic objects will be explained. The performance of R-RANSAC will be compared to state of the art target tracking algorithms in the context of problems that are relevant to UAV applications. We will also discuss recent research on vision based relative pose estimation. We will describe a technique for using point correspondences in video to estimate the camera pose, where the cost function to be optimized is derived from the epipolar constraint.
Bio
Randal W. Beard received the B.S. degree in electrical engineering from the University of Utah, Salt Lake City in 1991, the M.S. degree in electrical engineering in 1993, the M.S. degree in mathematics in 1994, and the Ph.D. degree in electrical engineering in 1995, all from Rensselaer Polytechnic Institute, Troy, NY. Since 1996, he has been with the Electrical and Computer Engineering Department at Brigham Young University, Provo, UT, where he is currently a professor. In 1997 and 1998, he was a Summer Faculty Fellow at the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA. In 2006 and 2007 he was a visiting research fellow at the Air Force Research Laboratory, Munitions Directorate, Eglin AFB, FL. His primary research focus is autonomous control of small air vehicles and multivehicle coordination and control. He is a past associate editor for the IEEE Transactions on Automatic Control, the IEEE Control Systems Magazine, and the Journal of Intelligent and Robotic Systems. He is a fellow of IEEE and AIAA.
Artificial Pancreas Project in Argentina
Abstract
In this talk a brief introduction to diabetes and the glucose control through an artificial pancreas will be presented. The complete solution, from the computation of a control-oriented model, it’s (in)validation vs. experimental data, the design of a controller and its test through simulations and clinical trials will be described. The first three clinical trials in Latin America and some ongoing and future research are also presented.
Bio
Ricardo S. Sánchez-Peña has an EE degree from the University of Buenos Aires (UBA) and a MSc. and Ph.D. from the California Institute of Technology. In Argentina he worked in CITEFA, CNEA, CNIE and CONAE. He collaborated with NASA, the German, and the Brazilian space agencies. He was Professor at the UBA and in Spain (UPC) as an ICREA researcher. He was Visiting Prof./researcher in several universities and consultant for companies in the aerospace and energy areas in the USA and the EU. He received awards from IEEE, NASA and ANCEFN. Presently he is Director of the Research & PhD Dept. at the Buenos Aires Institute of Technology (ITBA), and Investigador Superior of the National Research Council (CONICET). He leads the Artificial Pancreas project in Argentina, he has also applied control to the COVID19 lockdown schedule, and currently works in identification and control applied to neurobiology.
