A Control and Estimation Framework for Robotic Swarms in Uncertain Environments
Speaker
Spring Berman
Arizona State University
Host
Nick Roy
Abstract:
Robotic “swarms” comprising tens to thousands of robots have the potential to greatly reduce human workload and risk to human life. In many scenarios, the robots will lack global localization, prior data about the environment, and reliable communication, and they will be restricted to local sensing and signaling. We are developing a rigorous control and estimation 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. In this talk, I describe our work on various aspects of the framework, including scalable strategies for coverage, mapping, scalar field estimation, and cooperative manipulation. We use stochastic and deterministic models from chemical reaction network theory and fluid dynamics to describe the robots’ roles, state transitions, and motion at both the microscopic (individual) and macroscopic (population) levels. We also employ techniques from algebraic topology, nonlinear control theory, and optimization, and we model analogous behaviors in ant colonies to identify robot controllers that yield similarly robust performance. We are validating our framework on small mobile robots, called “Pheeno,” that we have designed to be low-cost, customizable platforms for multi-robot research and education.
Bio:
Spring Berman is an assistant professor of Mechanical and Aerospace Engineering at Arizona State University (ASU), where she directs the Autonomous Collective Systems (ACS) Laboratory. She received the B.S.E. degree in Mechanical and Aerospace Engineering from Princeton University in 2005 and the Ph.D. degree in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania (GRASP Laboratory) in 2010. From 2010 to 2012, she was a postdoctoral researcher in Computer Science at Harvard University. Her research focuses on controlling swarms of resource-limited robots with stochastic behaviors to reliably perform collective tasks in realistic environments. She was a recipient of the 2014 DARPA Young Faculty Award and the 2016 ONR Young Investigator Award. She currently serves as the associate director of the newly established ASU Center for Human, Artificial Intelligence, and Robotic Teaming.
Robotic “swarms” comprising tens to thousands of robots have the potential to greatly reduce human workload and risk to human life. In many scenarios, the robots will lack global localization, prior data about the environment, and reliable communication, and they will be restricted to local sensing and signaling. We are developing a rigorous control and estimation 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. In this talk, I describe our work on various aspects of the framework, including scalable strategies for coverage, mapping, scalar field estimation, and cooperative manipulation. We use stochastic and deterministic models from chemical reaction network theory and fluid dynamics to describe the robots’ roles, state transitions, and motion at both the microscopic (individual) and macroscopic (population) levels. We also employ techniques from algebraic topology, nonlinear control theory, and optimization, and we model analogous behaviors in ant colonies to identify robot controllers that yield similarly robust performance. We are validating our framework on small mobile robots, called “Pheeno,” that we have designed to be low-cost, customizable platforms for multi-robot research and education.
Bio:
Spring Berman is an assistant professor of Mechanical and Aerospace Engineering at Arizona State University (ASU), where she directs the Autonomous Collective Systems (ACS) Laboratory. She received the B.S.E. degree in Mechanical and Aerospace Engineering from Princeton University in 2005 and the Ph.D. degree in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania (GRASP Laboratory) in 2010. From 2010 to 2012, she was a postdoctoral researcher in Computer Science at Harvard University. Her research focuses on controlling swarms of resource-limited robots with stochastic behaviors to reliably perform collective tasks in realistic environments. She was a recipient of the 2014 DARPA Young Faculty Award and the 2016 ONR Young Investigator Award. She currently serves as the associate director of the newly established ASU Center for Human, Artificial Intelligence, and Robotic Teaming.