Tracking and coordination of multiple agents using sensor networks: System design, algorithms and experiments

This paper considers the problem of pursuit evasion games (PEGs), where the objective of a group of pursuers is to chase and capture a group of evaders in minimum time with the aid of a sensor network. The main challenge in developing a real-time control system using sensor networks is the inconsistency in sensor measurements due to packet loss, communication delay, and false detections. We address this challenge by developing a real-time hierarchical control system, named LochNess, which decouples the estimation of evader states from the control of pursuers via multiple layers of data fusion. The multiple layers of data fusion convert noisy, inconsistent, and bursty sensor measurements into a consistent set of fused measurements. Three novel algorithms are developed for LochNess: multisensor fusion, hierarchical multitarget tracking, and multiagent coordination algorithms. The multisensor fusion algorithm converts correlated sensor measurements into position estimates, the hierarchical multitarget tracking algorithm based on Markov chain Monte Carlo data association (MCMCDA) tracks an unknown number of targets, and the multiagent coordination algorithm coordinates pursuers to chase and capture evaders using robust minimum-time control. The control system LochNess is evaluated in simulation and successfully demonstrated using a large-scale outdoor sensor network deploymen