arXiv:2510.02624cs.RO2025-10

多机器人保持方形队形沿S型路径移动,抗网络延迟且精度高。

Multi-robot Rigid Formation Navigation via Synchronous Motion and Discrete-time Communication-Control Optimization

  • 提出'hold-and-hit'框架,同步运动并适应离散通信周期。
  • 实测在0.1m/s速度下,间距误差≤±0.069m,角度误差≤±19.15°。
  • 适合车载机器人等需无线协同的微处理器平台部署。

多机器人刚性队形导航对协同运输等应用至关重要。该过程要求一组协作机器人在运动中维持预设几何构型(如正方形)。对于无缆协同运动,机器人间通信需通过无线网络完成。然而,现有工作鲜有能直接在微处理器平台上通过无线网络实现复杂曲线路径导航的完整解决方案。为此,本文提出一种新型“hold-and-hit”通信-控制框架,可无缝集成于主流机器人操作系统(ROS)平台。该框架通过离散时间通信-控制周期运行,对无线网络延迟和丢包具有鲁棒性,有效同步机器人运动。同时,提出一种循环内优化方法,使刚性队形能精确跟踪所需曲线路径,即使面对大多数车辆机器人的非完整运动约束。结合hold-and-hit与循环内优化,确保在复杂场景下的精准可靠导航。仿真验证了在虚拟环境中沿S型路径维持四机器人方形队形优于两种现有方法。真实实验进一步证明:机器人以固定线速度0.1 m/s沿S型路径行进时,相互距离误差保持在±0.069m以内,相互角度朝向误差在±19.15°以内。

原文摘要 · Abstract (English)

Rigid-formation navigation of multiple robots is essential for applications such as cooperative transportation. This process involves a team of collaborative robots maintaining a predefined geometric configuration, such as a square, while in motion. For untethered collaborative motion, inter-robot communication must be conducted through a wireless network. Notably, few existing works offer a comprehensive solution for multi-robot formation navigation executable on microprocessor platforms via wireless networks, particularly for formations that must traverse complex curvilinear paths. To address this gap, we introduce a novel "hold-and-hit" communication-control framework designed to work seamlessly with the widely-used Robotic Operating System (ROS) platform. The hold-and-hit framework synchronizes robot movements in a manner robust against wireless network delays and packet loss. It operates over discrete-time communication-control cycles, making it suitable for implementation on contemporary microprocessors. Complementary to hold-and-hit, we propose an intra-cycle optimization approach that enables rigid formations to closely follow desired curvilinear paths, even under the nonholonomic movement constraints inherent to most vehicular robots. The combination of hold-and-hit and intra-cycle optimization ensures precise and reliable navigation even in challenging scenarios. Simulations in a virtual environment demonstrate the superiority of our method in maintaining a four-robot square formation along an S-shaped path, outperforming two existing approaches. Furthermore, real-world experiments validate the effectiveness of our framework: the robots maintained an inter-distance error within $\pm 0.069m$ and an inter-angular orientation error within $\pm19.15^{\circ}$ while navigating along an S-shaped path at a fixed linear velocity of $0.1 m/s$.

多机器人路径规划通信控制

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