计划对多机器人系统进行同步,并进行主动观察
Patrick Zhong1, Federico Rossi2, Dylan A Shell1
1Texas A&M University, College Station, TX 77840 USA.
概括
本研究介绍了一种新的马尔科夫决策过程 (MDP) 方法,用于多代理机器人系统,以有效地规划感知和通信行为. 该方法在不确定性条件下优化了联合状态估计,使合作任务的有效去中心化执行成为可能.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 多代理系统 多代理系统
- 在不确定性下规划
背景情况:
- 合作型多代理机器人系统需要基于共享状态观测的联合行动规划.
- 规划在不确定性下的感知和沟通行为对于高效的合作至关重要.
- 现有的方法难以应对大型联合信仰空间的难以处理.
研究的目的:
- 为在不确定性条件下的多代理规划开发一种可处理的计算方法.
- 让机器人能够主动决定何时获得国家信息的成本是合理的.
- 制定一种适用于高质量的观测方法,可以恢复联合状态,即使不经常.
主要方法:
- 问题作为一个马尔科夫决策过程 (MDP) 的制定,通过宏观行动解决.
- 开发一个类似贝尔曼的反复性来指导政策的产生.
- 政策同时定义了低级别的行动,国家恢复阶段和未来的重新安排承诺.
主要成果:
- 拟议的MDP表述有效地回避了完全联合信仰空间建设的需要.
- 在实际形式中展示了多机构:协助,传感器融合和协调活动.
- 成功的模拟研究和物理机器人实现验证了分散执行的方法.
结论:
- 开发的方法为在多代理机器人系统中分散执行联合计划提供了有效的框架.
- 该方法可以适应现实世界的非理想性,正如基于硬件经验的增强所示.
- 这项工作在需要联合状态估计和行动计划的场景中推进了合作机器人能力.
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