无需通信的多机器人系统通过高阶推理实现高效协同作业。
Higher Order Reasoning for Collaborative Communicationless Mobile Robot Operations
- 机器人用高阶信念粒子隐式传递信息，基于贝叶斯更新世界模型。
- 实验显示任务完成时间比一阶基线减少18%~32%，在部分可观测下仍稳定。
- 适合通信受限场景，如搜救、军事侦察中的无人集群协同。
在无通信环境下，多机器人系统需在不依赖持续信息交换的前提下运行。本文提出一种动态认知规划框架，通过机器人间的高阶推理实现隐式协调与长时程规划。机器人构建并传播高阶信念粒子，利用贝叶斯推断更新对世界的认知，并通过行为树选择动作，预判队友可能决策。一个时序感知的模型预测路径积分（MPPI）控制器将该推理嵌入底层执行，支持拦截规划与轨迹自适应调整，即使在部分可观测条件下也能稳定运行。该框架在仿真与物理实验中均验证有效，相较一阶基线显著缩短任务完成时间，证明认知逻辑可作为通信受限领域鲁棒协作的坚实基础。
原文摘要 · Abstract (English)
In communicationless environments, multi-robot systems must operate without the constant information exchange that many coordination strategies typically assume. This paper presents a novel dynamic epistemic planning framework that enables implicit coordination and long horizon planning through higher-order reasoning among robots. With our approach, robots form and propagate higher-order belief particles, update world beliefs using Bayesian inference, and select actions via a behavior tree that anticipates teammates' likely decisions. A temporally aware Model Predictive Path Integral (MPPI) controller integrates this reasoning into low-level execution, allowing robots to plan intercepts and adapt trajectories under partial observability. The proposed framework is evaluated in both simulations and physical experiments, where it consistently reduces task completion time compared to a first-order baseline, demonstrating that epistemic logic can serve as a robust foundation for resilient coordination in communication-restricted domains.
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