利用历史规划数据提升在线决策效率
Previous Knowledge Utilization In Online Anytime Belief Space Planning
- 基于MCTS重用过往规划信息，避免重复计算
- 实验显示计算时间显著减少，性能保持高位
- 适合需快速响应的自主系统场景
在机器人与自主系统中，在不确定环境下进行在线规划仍是关键挑战。尽管树搜索技术常被用于在计算约束下构建部分未来轨迹，但现有方法大多忽略先前规划会话中的信息，尤其在连续空间中。本文提出一种新颖且计算高效的策略，将历史规划数据融入当前决策过程。我们提供了该信息复用策略的理论基础，并设计了一种基于蒙特卡洛树搜索（MCTS）的算法来实现该方法。实验结果表明，该方法在保持高性能的同时显著降低计算时间。研究发现，整合历史规划信息可大幅提升不确定环境中在线决策的效率，为更快速、更自适应的自主系统铺平道路。
原文摘要 · Abstract (English)
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.
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