Previous Knowledge Utilization In Online Anytime Belief Space Planning
This work addresses efficiency challenges in autonomous systems by reusing information from previous planning sessions, representing an incremental improvement over existing methods.
The paper tackles the problem of online planning under uncertainty in robotics by leveraging historical planning data to reduce computation time, achieving significant reductions while maintaining high performance.
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.