Previous Knowledge Utilization In Online Anytime Belief Space Planning

TL;DR

This paper introduces a novel Monte Carlo Tree Search-based method that reuses past planning data to improve online decision-making efficiency in uncertain environments, especially in robotics.

Contribution

It presents a new approach that leverages historical planning information within MCTS to reduce computation time without sacrificing performance.

Findings

Significantly reduces computation time in online planning.

Maintains high decision-making performance with information reuse.

Provides theoretical foundations for the approach.

Abstract

Online planning under uncertainty remains a critical challenge in robotics and autonomous systems. While tree search techniques are commonly employed to construct partial future trajectories within computational constraints, most existing methods discard information from previous planning sessions considering continuous spaces. This study presents a novel, computationally efficient approach that leverages historical planning data in current decision-making processes. We provide theoretical foundations for our information reuse strategy and introduce an algorithm based on Monte Carlo Tree Search (MCTS) that implements this approach. Experimental results demonstrate that our method significantly reduces computation time while maintaining high performance levels. Our findings suggest that integrating historical planning information can substantially improve the efficiency of online decision-making in uncertain environments, paving the way for more responsive and adaptive autonomous systems.