Distributed information-based guidance of multiple mobile sensors for urban target search

Abstract

We present a framework for distributed mobile sensor guidance to locate and track a target inside an urban environment. Our approach leverages the communications between robots when a link is available, but it also allows them to act independently. Each robot actively seeks the target using information maximization. The robots are assumed to be capable of communicating with their peers within some distance radius, and the sensor payload of each robot is a camera modeled to have target detection errors of types I and II. Our contributions include an optimal information fusion algorithm for discrete distributions which allows each agent to combine its local information with that of its neighbors, and a path planner that uses the fused estimate and a recent coverage result for information maximization to guide the agents. We include simulations and laboratory experiments involving multiple robots searching for a moving target within model cities of different sizes.