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A Dynamic Path Planning Approach for Multirobot Sensor-Based Coverage Considering Energy Constraints

机译:考虑能量约束的基于多机器人传感器的覆盖范围的动态路径规划方法

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摘要

Multirobot sensor-based coverage path planning determines a tour for each robot in a team such that every point in a given workspace is covered by at least one robot using its sensors. In sensor-based coverage of narrow spaces, i.e., obstacles lie within the sensor range, a generalized Voronoi diagram (GVD)-based graph can be used to model the environment. A complete sensor-based coverage path plan for the robot team can be obtained by using the capacitated arc routing problem solution methods on the GVD-based graph. Unlike capacitated arc routing problem, sensor-based coverage problem requires to consider two types of edge demands. Therefore, modified Ulusoy algorithm is used to obtain mobile robot tours by taking into account two different energy consumption cases during sensor-based coverage. However, due to the partially unknown nature of the environment, the robots may encounter obstacles on their tours. This requires a replanning process that considers the remaining energy capacities and the current positions of the robots. In this paper, the modified Ulusoy algorithm is extended to incorporate this dynamic planning problem. A dynamic path-planning approach is proposed for multirobot sensor-based coverage of narrow environments by considering the energy capacities of the mobile robots. The approach is tested in a laboratory environment using Pioneer 3-DX mobile robots. Simulations are also conducted for a larger test environment.
机译:基于多机器人传感器的覆盖路径规划可确定团队中每个机器人的行程,以便至少一个机器人使用其传感器覆盖给定工作区中的每个点。在狭窄空间的基于传感器的覆盖范围内,即障碍物位于传感器范围内,可以使用基于广义Voronoi图(GVD)的图形对环境进行建模。通过使用基于GVD的图上的电容弧布线问题解决方法,可以获得机器人团队的基于传感器的完整覆盖路径计划。与电容弧布线问题不同,基于传感器的覆盖问题需要考虑两种类型的边缘需求。因此,通过考虑基于传感器的覆盖期间的两种不同的能耗情况,改进的Ulusoy算法可用于获得移动机器人漫游。但是,由于环境的部分未知,机器人可能会在旅途中遇到障碍。这需要重新计划过程,其中要考虑剩余的能量容量和机器人的当前位置。在本文中,对改进的Ulusoy算法进行了扩展,以纳入此动态规划问题。通过考虑移动机器人的能量容量,提出了一种动态路径规划方法,用于基于多机器人传感器的狭窄环境覆盖。使用Pioneer 3-DX移动机器人在实验室环境中对该方法进行了测试。还针对更大的测试环境进行了仿真。

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