首页> 外文期刊>The International journal of robotics research >Structured triangulation in multi-robot systems: Coverage, patrolling, Voronoi partitions, and geodesic centers
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Structured triangulation in multi-robot systems: Coverage, patrolling, Voronoi partitions, and geodesic centers

机译:多机器人系统中的结构化三角剖分:覆盖范围,巡逻,Voronoi分区和测地线中心

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We present a fundamental framework for organizing exploration, coverage, and surveillance by a swarm of robots with limited individual capabilities, based on triangulating an unknown environment with a multi-robot system. Locally, an individual triangle is easy for a single robot to manage and covers a small area; globally, the topology of the triangulation approximately captures the geometry of the entire environment. Combined, a multi-robot system can explore, map, navigate, and patrol. Algorithms can store information in triangles that the robots can read and write as they run their algorithms. This creates a physical data structure (PDS) that is both robust and versatile. We study distributed approaches to triangulating an unknown, two-dimensional Euclidean space using a multi-robot network. The resulting PDS is a compact representation of the workspace, contains distributed knowledge of each triangle, encodes the dual graph of the triangulation, and supports reads and writes of auxiliary data. The ability to store and process this auxiliary information enables the simple robots to solve complex problems. We develop distributed algorithms for dual-graph navigation, patrolling, construction of a topological Voronoi tessellation, and location of the geodesic centers in non-convex regions. We provide theoretical performance guarantees for the quality of constructed triangulation and the connectivity of a dual graph in the triangulation. In addition, we show that the path lengths of the physical navigation are within a constant factor of the shortest-path Euclidean distance. We validate these theoretical results with simulations and experiments with a dozen or more robots.
机译:我们提出了一个基本框架,用于通过对未知环境和多机器人系统进行三角测量,来组织一群能力有限的机器人进行探索,覆盖和监视。在本地,单个机器人很容易管理单个三角形,并且占地面积小。在全局范围内,三角剖分的拓扑大约捕获了整个环境的几何形状。组合后,多机器人系统可以探索,地图绘制,导航和巡逻。算法可以将信息存储在三角形中,机器人在运行算法时可以读取和写入这些信息。这将创建既健壮又通用的物理数据结构(PDS)。我们研究了使用多机器人网络对未知的二维欧几里德空间进行三角剖分的分布式方法。生成的PDS是工作空间的紧凑表示,包含每个三角形的分布式知识,对三角剖分的对偶图进行编码,并支持辅助数据的读取和写入。存储和处理此辅助信息的能力使简单的机器人能够解决复杂的问题。我们开发了用于双图导航,巡逻,拓扑Voronoi细分的构造以及非凸区域中测地线中心位置的分布式算法。我们为构造的三角剖分的质量和三角剖分中的对偶图的连通性提供了理论上的性能保证。此外,我们表明物理导航的路径长度在最短路径欧几里得距离的恒定因子之内。我们通过十几个或更多机器人的仿真和实验来验证这些理论结果。

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