Robotics and Autonomous Systems Center

Controlling Groups of Robots with Unreliable Relative Sensing

Mac Schwager



Groups of robots working collaboratively have the potential to change the way we sense and interact with our environment at large scales. However, in order to be useful in the real world, multi-robot systems must perform without global information, and they must adapt to faulty sensors. This talk will describe our recent work in controlling groups of robots with unreliable relative sensing measurements. We will treat two basic multi-robot problems: formation control and coverage control. In the first problem, we would like the robots to converge to a desired formation without a shared global reference frame, using only relative distance and bearing measurements. We propose a novel nonlinear control architecture that ensures asymptotic convergence to the desired formation. We also implement this controller on a network of quadrotor aerial robots. The robots use onboard vision, computing relative pose estimates from shared features in their images, in order to execute the formation controller without any global pose information. In the second problem we consider deploying a group of sensing robots to cover an environment with their sensors, however some (a priori unknown) robots have faulty sensors. We propose a decentralized adaptive control approach by which the robots collaboratively determine which robots have faulty sensors, and reposition themselves in order to compensate for the sensor faults. Convergence to a locally optimal sensing configuration is proven using a Lyapunov analysis.



Mac Schwager is an assistant professor in the Department of Mechanical Engineering and the Division of Systems Engineering at Boston University. He obtained his BS degree in 2000 from Stanford University, his MS degree from MIT in 2005, and his PhD degree from MIT in 2009. He was a postdoctoral researcher in the GRASP lab at the University of Pennsylvania from 2010 to 2012. His research interests are in distributed algorithms for control, perception, and learning in groups of robots and animals. He received the NSF CAREER award in 2014.


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