Bio: Spring Berman is an associate professor of mechanical and aerospace engineering and graduate faculty in computer science and exploration systems design. She is associate director of the Center for Human, Artificial Intelligence, and Robot Teaming (CHART), a center within the ASU Global Security Initiative, as well as the director of the Autonomous Collective Systems Laboratory. Before joining ASU in 2012, she was a postdoctoral researcher in computer science at Harvard University. She received a PhD in mechanical engineering and applied mechanics from the University of Pennsylvania and a BSE in mechanical and aerospace engineering from Princeton University. Her research interests include the decentralized control of robotic swarms, soft robots and other types of bio-inspired resilient distributed systems, as well as the coordination of effective, ethical human-robot teams. She is a recipient of the 2016 ONR Young Investigator Award and the 2014 DARPA Young Faculty Award.
Abstract: Robotic swarms are currently being developed for many applications, including environmental sensing, exploration and mapping, disaster response, agriculture, transportation 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 global position information, 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 a rigorous control framework for swarms that are subject to these constraints. This framework will 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 our work on various aspects of the framework, including control strategies for coverage, mapping, task allocation and cooperative manipulation. We develop and analyze models of the swarm at different levels of abstraction based on differential equations, Markov chains and graphs. We also design robot controllers using feedback control theory, optimization techniques and reinforcement learning. We validate our control strategies in simulation and on experimental testbeds with small mobile robots.