在线随时信念空间规划中的先验知识利用

IF 5.3 2区 计算机科学 Q2 ROBOTICS
Michael Novitsky;Moran Barenboim;Vadim Indelman
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引用次数: 0

摘要

不确定性下的在线规划仍然是机器人和自主系统面临的关键挑战。虽然树搜索技术通常用于在计算约束下构建部分未来轨迹,但大多数现有方法都放弃了先前考虑连续空间的规划会议的信息。本研究提出了一种新颖的、计算效率高的方法,在当前决策过程中利用历史规划数据。我们为我们的信息重用策略提供了理论基础,并介绍了一种基于蒙特卡罗树搜索(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。我们的研究结果表明,整合历史规划信息可以大大提高不确定环境下在线决策的效率,为更具响应性和适应性的自治系统铺平道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Previous Knowledge Utilization in Online Anytime Belief Space Planning
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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来源期刊
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters Computer Science-Computer Science Applications
CiteScore
9.60
自引率
15.40%
发文量
1428
期刊介绍: The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.
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