  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 flexible
path planning algorithm which exploits Monte Carlo Tree Search (MCTS) and
particle filtering based on a reward motivated by information-theoretic
considerations.
