Active Acoustic Source Tracking Exploiting Particle Filtering and Monte Carlo Tree Search

In this paper, we address the task of active acoustic source tracking as part of robotic path planning. It denotes the planning of sequences of robotic movements to enhance tracking results of acoustic sources, e.g., talking humans, by fusing observations from multiple positions. Essentially, two strategies are possible: short-term planning, which results in greedy behavior, and long-term planning, which considers a sequence of possible future movements of the robot and the source. Here, we focus on the second method as it might improve tracking performance compared to greedy behavior and propose a path planning algorithm which exploits Monte Carlo Tree Search (MCTS) and particle filtering, based on a reward motivated by information-theoretic considerations. By representing the state posterior by weighted particles, we are capable of modelling arbitrary probability density functions (PDF)s and dealing with highly nonlinear state-space models.