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In this paper, we address the task of active acoustic source tracking as partof robotic path planning. It denotes the planning of sequences of roboticmovements to enhance tracking results of acoustic sources, e.g., talkinghumans, by fusing observations from multiple positions. Essentially, twostrategies are possible: short-term planning, which results in greedy behavior,and long-term planning, which considers a sequence of possible future movementsof the robot and the source. Here, we focus on the second method as it mightimprove tracking performance compared to greedy behavior and propose a flexiblepath planning algorithm which exploits Monte Carlo Tree Search (MCTS) andparticle filtering based on a reward motivated by information-theoreticconsiderations.

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