基于部分可观蒙特卡洛树搜索算法的无人系统异步任务规划

周鑫; 陈子夷; 周天

doi:10.16383/j.aas.c250313

基于部分可观蒙特卡洛树搜索算法的无人系统异步任务规划

doi: 10.16383/j.aas.c250313 cstr: 32138.14.j.aas.c250313

周鑫^1,,
陈子夷^1,,
周天^1,

1.
国防科技大学系统工程学院长沙 410073

基金项目: 国家自然科学基金(72471234)资助

详细信息

作者简介:
周鑫：国防科技大学系统工程学院副教授. 2019年获得国防科技大学管理科学与工程专业博士学位. 主要研究方向为复杂系统评估与优化. 本文通信作者. E-mail: zhouxin09@nudt.edu.cn

陈子夷：国防科技大学系统工程学院讲师. 2023年获得国防科技大学管理科学与工程专业博士学位. 主要研究方向为启发式算法, 组合优化, 复杂系统调度及深度强化学习. E-mail: chenziyi_nudt@163.com

周天：国防科技大学系统工程学院硕士研究生. 2025年获得国防科技大学目标工程专业学士学位. 主要研究方向为系统工程. E-mail: zhoutiannudt@163.com

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出版历程
- 收稿日期: 2025-07-14
- 录用日期: 2025-10-30
- 网络出版日期: 2025-12-05
- 刊出日期: 2026-01-20

Unmanned System Asynchronous Task Planning Based on Partially Observable Monte Carlo Tree Search Algorithm

ZHOU Xin^1
,,
CHEN Zi-Yi^1
,,
ZHOU Tian^1
,

1.
College of Systems Engineering, National University of Defense Technology, Changsha 410073

Funds: Supported by National Natural Science Foundation of China (72471234)

More Information

Author Bio:
ZHOU Xin　Associate professor at the College of Systems Engineering, National University of Defense Technology. He received his Ph.D. degree in management science and engineering from National University of Defense Technology in 2019. His main research interest is complex system evaluation and optimization. Corresponding author of this paper

CHEN Zi-Yi　Lecturer at the College of Systems Engineering, National University of Defense Technology. He received his Ph.D. degree in management science and engineering from National University of Defense Technology in 2023. His research interests include heuristic algorithm, combinatorial optimization, complex system scheduling, and deep reinforcement learning

ZHOU Tian　Master student at the College of Systems Engineering, National University of Defense Technology. He received his bachelor degree in objective project from National University of Defense Technology in 2025. His main research interest is systems engineering

摘要

摘要: 无人系统正深刻重塑社会生活方式与战争形态. 围绕无人系统动态规划领域, 首先, 将环境抽象为由节点和边组成的拓扑网络. 其次, 针对异步规划中变步长时间推进的问题, 提出一种新颖的异步规划算法, 即半马尔科夫环境下的部分可观蒙特卡洛树搜索算法(SPOMCP), 其创新之处在于将拓扑网络转化为具有最简信息表示的子目标图, 并实现基于变步长时间推进机制的策略快速寻优. 通过理论分析, 证明了SPOMCP能够生成最优策略, 且计算复杂度与子目标节点数呈指数相关. 最后, 仿真实验表明了SPOMCP的性能高于基准算法, 用不到基准算法89.18%的计算时间, 得到高于基准算法的平均回报值.
- 异步规划 /
- 最简信息表示 /
- 半马尔科夫环境 /
- 蒙特卡洛树搜索
Abstract: Unmanned systems are profoundly reshaping social lifestyles and modes of warfare. In the field of dynamic planning for unmanned systems, the environment is first abstracted as a topological network composed of nodes and edges. Second, for the variable step time advancement problem of asynchronous planning, a novel asynchronous planning algorithm, namely, a partially observable Monte Carlo tree search algorithm in the semi-Markov environment (SPOMCP) is proposed. The innovation is that the topological network is transformed into a sub-goal graph with the simplest information representation, and enabling rapid policy optimization based on a variable step time advancement mechanism. Through theoretical analysis, it is proven that SPOMCP can generate the optimal strategies, and the computational complexity is exponentially correlated with the number of sub-goal nodes. Finally, simulation experiments demonstrate that SPOMCP outperforms the benchmark algorithm in terms of performance, with less than 89.18% of the benchmark algorithm＇s computation time, resulting in higher average rewards.
- asynchronous planning /
- simplest information representation /
- semi-Markov environment /
- Monte Carlo tree search

HTML全文

图 1 无人系统信息收集示意图

Fig. 1 Schematic diagram of the unmanned system information gathering

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图 2 等步长/变步长时间推进机制示意图

Fig. 2 Schematic diagram of fixed step/variable step time advancement mechanism

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图 3 原始拓扑图

Fig. 3 The original topology map

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图 4 子目标拓扑图

Fig. 4 The sub-goal topology map

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图 5 SPOMCP示意图

Fig. 5 Schematic diagram of SPOMCP

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图 6 平均回报值(场景A)

Fig. 6 The average reward (Scene A)

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图 7 平均运行时间(场景A)

Fig. 7 The average runtime (Scene A)

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图 8 平均回报值(场景B)

Fig. 8 The average reward (Scene B)

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图 9 平均运行时间(场景B)

Fig. 9 The average runtime (Scene B)

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图 10 平均回报值(场景C)

Fig. 10 The average reward (Scene C)

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图 11 平均运行时间(场景C)

Fig. 11 The average runtime (Scene C)

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