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Performance Guarantee of a Suboptimal Policy for Multi-Robot Perimeter Surveillance

This talk deals with the development and analysis of a sub-optimal decision algorithm for a collection of robots that assist a remotely located operator in perimeter surveillance. The operator is tasked with the classification of incursions across the perimeter. Whenever there is an incursion into the perimeter, an Unattended Ground Sensor (UGS) in the vicinity, signals an alert. A robot services the alert by visiting the alert location, collecting evidence in the form of video imagery, and transmitting them to the operator. The accuracy of operator's classification depends on the volume and freshness of information gathered and provided by the robots at locations where incursions occur. There are two competing needs for a robot: it needs to spend adequate time at an alert location to collect evidence for aiding the operator in accurate classification but it also needs to service other alerts as soon as possible, so that the evidence collected is relevant. The control problem is to determine the optimal amount of time a robot must spend servicing an alert. The incursions are stochastic and their statistics are assumed to be known.

The control problem is naturally posed as a Markov Decision Problem. However, even for two robots and five UGS locations, the number of states is of the order of billions rendering exact dynamic programming methods intractable. Approximate Dynamic Programming (ADP) via Linear Programming (LP) provides a way to approximate the value function and derive sub-optimal strategies. The focus of this talk is the derivation of a tractable lower bound via LP and state aggregation and the construction of a sub-optimal policy whose performance exceeds the lower bound. An illustrative perimeter surveillance example will be presented at the end of the talk.

It is joint work with Meir Pachter, Phil Chandler and Krishna Kalyanam at AFRL, Dayton, with Prof. Khargonekar at Univ. of Florida, and my student, Dr. Myoungkuk Park.

Type of Seminar:
IfA Seminar
Prof. Swaroop Darbha
Mechanical Engineering, Texas A&M University
May 26, 2014   15:15

Contact Person:

Prof. John Lygeros
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Biographical Sketch:
Swaroop Darbha received his Bachelor of Technology from the Indian Institute of Technology - Madras in 1989, M. S. and Ph. D. degrees from the University of California in 1992 and 1994 respectively. He was a post-doctoral researcher at the California PATH program from 1995 to 1996. He has been on the faculty of Mechanical Engineering at Texas A&M University since 1997, where he is currently a professor. His current research interests lie in the development of diagnostic systems for air brakes in trucks, development of planning, control and resource allocation algorithms for a collection of Unmanned Vehicles.