A point-based POMDP planner for target tracking

Abstract

Target tracking has two variants that are often studied independently with different approaches: target searching requires a robot to find a target initially not visible, and target following requires a robot to maintain visibility on a target initially visible. In this work, we use a partially observable Markov decision process (POMDP) to build a single model that unifies target searching and target following. The POMDP solution exhibits interesting tracking behaviors, such as anticipatory moves that exploit target dynamics, information- gathering moves that reduce target position uncertainty, and energy-conserving actions that allow the target to get out of sight, but do not compromise long-term tracking performance. To overcome the high computational complexity of solving POMDPs, we have developed SARSOP, a new point-based POMDP algorithm based on successively approximating the space reachable under optimal policies. Experimental results show that SARSOP is competitive with the fastest existing point-based algorithm on many standard test problems and faster by many times on some.