Abstract: Robotic swarms are currently being developed for many applications, including environmental sensing, exploration and mapping, infrastructure inspection, disaster response, agriculture, and logistics. However, significant technical challenges remain before they can be robustly deployed in uncertain, dynamic environments. We are addressing the problem of controlling swarms of robots that lack prior data about the environment and reliable inter-robot communication. As in biological swarms, the highly resource-constrained robots would be restricted to information obtained through local sensing and signaling. We are developing scalable control strategies that enable swarms to operate largely autonomously, with user input consisting only of high-level directives that map to a small set of robot parameters. In this talk, I describe control strategies that we have designed for collective tasks that include coverage, mapping, and cooperative manipulation. We develop and analyze models of the swarm at different levels of abstraction based on differential equations, Markov chains, and graphs, and we design robot controllers using feedback control theory and optimization techniques. We validate our control strategies in simulation and on experimental testbeds with small mobile robots.
Biography: Dr. Spring Berman is an Associate Professor of Mechanical and Aerospace Engineering at Arizona State University (ASU) and directs the Autonomous Collective Systems Laboratory. She is also an Associate Director of the Center for Human, Artificial Intelligence, and Robot Teaming (CHART) and Graduate Faculty in Computer Science and Exploration Systems Design. Before joining ASU in 2012, she was a postdoctoral researcher in Computer Science at Harvard University. She received the M.S.E. and Ph.D. degrees in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania and the B.S.E. degree in Mechanical and Aerospace Engineering from Princeton University. Her research focuses on the design of control strategies for robotic swarms, soft continuum robots, and distributed human-autonomy teams.
Abstract: Despite the rise of self-driving cars and drones, an autonomous system that can operate reliably outside a carefully structured factory floor is rare. The main reason is uncertainty. A robot must decide what it should do now to accomplish its tasks, despite not knowing the exact effect of its actions, errors in sensors and sensing, and the lack of information and understanding about itself and its environment. However, the technology for making good decisions in the presence of uncertainty is still lacking. In this talk, I will present some of our recent work in developing such technology, specifically in our work on enabling the Partially Observable Markov Decision Processes —the general and principled framework for decision-making under uncertainty— to become practical.
Biography: Hanna Kurniawati is an Associate Professor with ANU and CS Futures fellowships at the School of Computing, Australian National University (ANU). Hanna’s research interests include planning under uncertainty, robotics, robot motion planning, integrated planning and learning, and computational geometry applications. Together with students and collaborators, her work has received multiple awards, including an ICAPS’15 best paper award, a finalist for the best paper award at ICRA’15, and the Robotics: Science and Systems’21 Test of Time Award. Hanna is also a keynote speaker for IROS’18 and a Program Co-Chair for ICRA 2022.