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<p>This paper presents distributed message-based triangulation that concurrently extracts a triangle lattice from an underlying robot network. The lattice is aimed at being planar. Hence, a robot inside a specific triangle can localize using only the bearing information of its local neighbors. For the planarity of the lattice, we introduce two different versions of triangulation. 1) The <i>default</i> version compares the unique ID of two or more geometrically overlapped triangles, and only the triangle with the lowest ID survives. 2) The <i>edge-length minimization</i> version estimates the longest edge lengths of all overlapped triangles and elects only the triangle having the locally shortest one. For each version, we provide implementation details on a group of practical low-cost robots, r-one, with extensive simulation results that validate its feasibility to the platform. We also address theoretical analyses showing that our methods are scalable for any population of robots.</p>

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Distributed concurrent triangulation for a group of low-cost robots with bearing-only sensing

  • 期刊论文

  • 发表日期：

    2026年1月20日

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  • Seoung Kyou Lee

摘要

This paper presents distributed message-based triangulation that concurrently extracts a triangle lattice from an underlying robot network. The lattice is aimed at being planar. Hence, a robot inside a specific triangle can localize using only the bearing information of its local neighbors. For the planarity of the lattice, we introduce two different versions of triangulation. 1) The default version compares the unique ID of two or more geometrically overlapped triangles, and only the triangle with the lowest ID survives. 2) The edge-length minimization version estimates the longest edge lengths of all overlapped triangles and elects only the triangle having the locally shortest one. For each version, we provide implementation details on a group of practical low-cost robots, r-one, with extensive simulation results that validate its feasibility to the platform. We also address theoretical analyses showing that our methods are scalable for any population of robots.