"Dynamic and Communication-Aware Coordination of Large-Scale Mobile Robotic Networks"
-- Marco Pavone, Stanford University
Abstract
In the first part of the talk, I will present a joint algorithmic and queueing approach to the design of cooperative control and task allocation strategies for networks of robots, which must fulfill spatially-localized tasks generated over time by an exogenous process (example applications include environmental monitoring with mobile sensor networks, surveillance of protected environments with teams of unmanned aerial vehicles, and automated urban mobility systems). Task arrivals are modeled as a stochastic process, and provably efficient algorithms are designed to enable robots to identify, allocate, prioritize, and plan paths to reach tasks' locations. The key feature of this approach lies in the integration of geometric and combinatorial aspects (e.g., coverage, traveling salesman problems) with stochastic and control aspects (e.g., queueing effects, stability analysis) in the context of coordination of multi-robot networks.
In the second part of the talk, I will discuss the inherent trade-off between time and communication complexity for the cooperative control of robotic networks. Generally speaking, time-optimal algorithms are fast and robust, but require a large (and sometimes impractical) number of exchanged messages; in contrast, communication optimal algorithms minimize the amount of information routed through the network, but are slow and sensitive to link failures. Focusing on a generalized version of the decentralized consensus problem (that includes voting and mediation), I will present a tunable algorithm, where the tuning parameter allows a graceful transition from time-optimal to communication-optimal performance, and determines the algorithm’s robustness, measured as either the number of single points of failure or the time required to recover from a failure. An interesting feature of this algorithm is that it leads the robots to self-organize into a semi-hierarchical structure with variable-size clusters, within which information is flooded.
Biography
Marco Pavone is an assistant professor of aeronautics and astronautics at Stanford University, where he is the director of the Autonomous Systems Laboratory and holds courtesy appointments in the Department of Electrical Engineering, in the Institute for Computational and Mathematical Engineering, and in the Information Systems Laboratory. Before joining Stanford, he was a research technologist within the Robotics Section at the NASA Jet Propulsion Laboratory. He received a Ph.D. in aeronautics and astronautics from the Massachusetts Institute of Technology in 2010. Pavone’s areas of expertise lie in the fields of controls and robotics. His main research interests are in the development of methodologies for the analysis, design and control of autonomous systems, with an emphasis on large-scale robotic networks and autonomous aerospace vehicles. Pavone is a recipient of a NASA Early Career Faculty award, a Hellman Faculty Scholar Award and was named NASA NIAC Fellow in 2011.
