Stochastic Distributed Algorithms for Target Surveillance

In this paper we investigate problems of target surveillance with the aim of building a general framework for the evaluation of the performance of a system of autonomous agents. To this purpose we propose a class of semi-distributed stochastic navigation algorithms, that drive swarms of autonomous scouts to the surveillance of grounded targets, and we provide a novel approach to performance estimation based on analysing sequential observations of the system’s state with information theoretical techniques. Our goal is to achieve a deeper understanding of the interrelations between randomness, resource consumption and ergodicity of a decentralized control system in which the decision-making process is stochastic.