Approximate Representations for Multi-Robot Control Policies that Maximize Mutual Information

Abstract

We address the problem of controlling a small team of robots to estimate the location of a mobile target using non-linear range-only sensors. Our control law maximizes the mutual information between the team's estimate and future measurements over a finite time horizon. Because the computations associated with such policies scale poorly with the number of robots, the time horizon associated with the policy, and typical non-parametric representations of the belief, we design approximate representations that enable real-time operation. The main contributions of this paper include the control policy, an algorithm for approximating the belief state with provable error bounds, and an extensive study of the performance of these algorithms using simulations and real world experiments in complex, indoor environments.