通过Poset产品在协作时间逻辑任务下进行快速和自适应的多代理计划
Zesen Liu1, Meng Guo1, Weimin Bao2
1Department of Mechanics and Engineering Science, College of Engineering, Peking University, Beijing 100871, China.
Research (Washington, D.C.)
|August 7, 2025
概括
这项研究引入了多代理系统的新规划范式,使复杂的时间逻辑公式能够有效地规划任务. 适应性算法可扩展到大型车队和长任务公式,克服传统方法的局限性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 正式方法 正式方法
背景情况:
- 在动态环境中的大型多代理系统中,协调规划至关重要.
- 现有的启发式或基于学习的方法缺乏性能保证.
- 正式方法为具有时空要求的任务 (例如线性时间逻辑公式) 提供可验证性,但面临可扩展性问题.
研究的目的:
- 在多代理系统中开发一个可扩展的计划范式,用于全系统的时间任务公式.
- 为了克服指数复杂度瓶在任务规划的传统的正式方法.
- 为大型机队提供高效和可验证的任务规划,以实现复杂的,持续释放的任务.
主要方法:
- 提出了一个新的规划范式,避免了直接的Büchi自动翻译和同步产品.
- 介绍了一种自适应规划算法,即时计算放松部分有序集合 (R-posets) 的产品.
- 在尊重订单约束的同时,将子任务分配给代理人.
主要成果:
- 实现了关于系统大小和公式长度的多项式时间和内存复杂性.
- 成功规划了400多种代理和400多种公式的任务,显著优于现有方法.
- 在模拟和硬件中证明了该方法在大型服务机器人车队上的有效性.
结论:
- 拟议的自适应规划方法为复杂的多代理任务规划提供了可扩展和高效的解决方案.
- 它为具有线性时间逻辑要求的任务提供了一个可验证的框架,即使对于大型系统也是如此.
- 该方法可以在动态环境中实现强大的协调和规划,用于现实世界的应用.
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