Active Estimation for Multi-Robot Teams in Hazardous Environments
This talk will discuss the problem of deploying a group of robots into an environment for state estimation tasks (e.g. target localization, field estimation, or mapping), while avoiding hazards at unknown positions that cause the robots to fail. Some regions of the environment may be hazardous, for example, due to fire, severe weather, caustic chemicals, or the presence of adversarial agents. A probabilistic model is formulated, under which recursive Bayesian filters are used to estimate the state and hazards online. The robots move both to avoid hazards and to provide useful sensor information by following the gradient of mutual information. Efforts toward overcoming the challenges of decentralization and scalability will also be discussed.
Mac Schwager is an assistant professor in the Department of Mechanical Engineering at Boston University. He obtained a PhD degree from MIT in 2009, an MS degree from MIT in 2005, and a BS degree in 2000 from Stanford University. He was a postdoctoral researcher in the General Robotics, Automation, Sensing, and Perception (GRASP) Lab at the University of Pennsylvania from 2010 to 2011. His research interests are in distributed algorithms for control, estimation, and learning in groups of robots and animals.