arXiv:2511.12237cs.ROcs.HC2025-11被引 2

用混合整数规划生成多机器人间歇通信探索计划,适合未知环境部署。

Intermittent Rendezvous Plans with Mixed Integer Linear Program for Large-Scale Multi-Robot Exploration

  • 基于MILP构建间歇通信下的机器人会合计划
  • 在Gazebo大规模仿真中实现高效任务完成与计划追踪
  • 提出简易规则让机器人在未知环境中跟进行动

具有通信约束的多机器人探索(MRE)系统在搜救、隐秘侦察和军事行动中表现高效。尽管部分工作采用机会式方法提升效率,另一些则侧重预设轨迹或调度以增强可解释性,但后者通常需预先掌握环境信息,限制了在如水下探测等不确定性场景的应用。此前我们提出一种基于周期性会合事件的间歇通信框架,以缓解该局限,但原系统无法生成最优计划,且缺乏应对真实轨迹的执行机制。本文进一步研究,在通信受限与间歇连通性(MRE-CCIC)背景下,提出一种混合整数线性规划(MILP)方法生成会合计划,并设计基于未知场景会合追踪(RTUS)机制的执行策略。该机制通过简单规则使机器人在未知条件下仍能有效遵循计划。我们在Gazebo中配置的大规模环境中进行了评估,结果表明该方法能快速响应并高效完成任务。我们开源了MILP计划生成器与大尺度MRE-CCIC实现。

原文摘要 · Abstract (English)

Multi-Robot Exploration (MRE) systems with communication constraints have proven efficient in accomplishing a variety of tasks, including search-and-rescue, stealth, and military operations. While some works focus on opportunistic approaches for efficiency, others concentrate on pre-planned trajectories or scheduling for increased interpretability. However, scheduling usually requires knowledge of the environment beforehand, which prevents its deployment in several domains due to related uncertainties (e.g., underwater exploration). In our previous work, we proposed an intermittent communications framework for MRE under communication constraints that uses scheduled rendezvous events to mitigate such limitations. However, the system was unable to generate optimal plans and had no mechanisms to follow the plan considering realistic trajectories, which is not suited for real-world deployments. In this work, we further investigate the problem by formulating the Multi-Robot Exploration with Communication Constraints and Intermittent Connectivity (MRE-CCIC) problem. We propose a Mixed-Integer Linear Program (MILP) formulation to generate rendezvous plans and a policy to follow them based on the Rendezvous Tracking for Unknown Scenarios (RTUS) mechanism. The RTUS is a simple rule to allow robots to follow the assigned plan, considering unknown conditions. Finally, we evaluated our method in a large-scale environment configured in Gazebo simulations. The results suggest that our method can follow the plan promptly and accomplish the task efficiently. We provide an open-source implementation of both the MILP plan generator and the large-scale MRE-CCIC.

多机器人路径规划混合整数规划探索

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