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.

翻译：不确定性下的在线规划仍然是机器人与自主系统领域的关键挑战。虽然树搜索技术通常被用于在计算约束下构建部分未来轨迹，但现有方法大多在考虑连续空间时丢弃了先前规划会话的信息。本研究提出了一种新颖的计算高效方法，该方法在当前决策过程中利用历史规划数据。我们为信息重用策略提供了理论基础，并介绍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，我们的方法在保持高性能水平的同时，显著减少了计算时间。我们的发现表明，整合历史规划信息可以大幅提高不确定性环境下在线决策的效率，为更具响应性和适应性的自主系统铺平道路。