Stochastic target interception in non-convex domain using MILP

In this paper we present a planning approach for the stochastic target interception problem, in which, a team of mobile sensor agents is tasked with intercepting multiple targets. We extend our previous work on stochastic target interception to non-convex domains and propose a cost that addresses minimum time requirement for probabilistically intercepting all the targets if possible over a finite horizon. Indeed, our optimization problem for the stochastic case has similar computational costs as the optimization program for the corresponding deterministic case. Our solution presumes that the system can be approximated by linear dynamics and Gaussian noise, with Gaussian localization uncertainty.