Stochastic Surveillance and Distributed Coordination
Technical Report,01 Jun 2015,31 May 2020
University of California Santa Barbara Santa Barbara United States
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This project focused on robotic surveillance in complex environments via autonomous vehicles. The chief aim was to design fast and unpredictable motion strategies for surveillance agents. The technical approach focused on Markov chain modeling and optimization methods. For the setting of faults or randomly-appearing intruders, we proposed quickest detection algorithms and computed and optimized the so-called hitting time of a single and of multiple Markov chains. For example, we analyzed the meeting time between a pair of pursuer and evader performing random walks on digraphs. We obtained the closed-form expression for the expected meeting time and setup and studied the minimization problem for the expected capture time for a pursuerevader pair.
- Military Intelligence