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arXiv 2407.06296cs.ROcs.MA
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面向锚点的局部Voronoi划分用于无GPS多机器人覆盖

Anchor-Oriented Localized Voronoi Partitioning for GPS-denied Multi-Robot Coverage

School of Computing, University of Georgia(佐治亚大学计算学院)

Aiman Munir, Ehsan Latif, Ramviyas Parasuraman

AI总结:

针对无GPS环境下多机器人覆盖缺乏全局定位的难题,提出基于公共锚点的动态局部Voronoi划分方法与共识协调算法,实验表明其性能与使用GPS的最先进方法相当。

摘要:

多机器人覆盖在环境监测、搜救行动、精准农业等众多应用中至关重要。在现代应用中,多机器人团队需在无法获取全局定位的无GPS极端环境中,协同探索未知空间场。覆盖算法通常假设机器人位置与覆盖环境定义在全局参考坐标系中,但在无全局定位的情况下协调机器人运动并确保共享凸工作空间的覆盖极具挑战。本文提出一种新颖的面向锚点的覆盖(AOC)方法,基于公共锚点位置生成本地化动态Voronoi划分。我们进一步提出一种基于共识的协调算法,可在机器人的相对参考坐标系中就锚点周围的覆盖工作空间达成一致。通过大量仿真与真实世界实验,我们证明所提出的采用局部Voronoi划分的面向锚点方法,性能与使用GPS的最先进覆盖控制器相当。

英文摘要:

Multi-robot coverage is crucial in numerous applications, including environmental monitoring, search and rescue operations, and precision agriculture. In modern applications, a multi-robot team must collaboratively explore unknown spatial fields in GPS-denied and extreme environments where global localization is unavailable. Coverage algorithms typically assume that the robot positions and the coverage environment are defined in a global reference frame. However, coordinating robot motion and ensuring coverage of the shared convex workspace without global localization is challenging. This paper proposes a novel anchor-oriented coverage (AOC) approach to generate dynamic localized Voronoi partitions based around a common anchor position. We further propose a consensus-based coordination algorithm that achieves agreement on the coverage workspace around the anchor in the robots' relative frames of reference. Through extensive simulations and real-world experiments, we demonstrate that the proposed anchor-oriented approach using localized Voronoi partitioning performs as well as the state-of-the-art coverage controller using GPS.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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