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MOBILE ROBOT PATH PLANNING USING HYBRID GENETIC ALGORITHM AND TRAVERSABILITY VECTORS METHOD

机译:混合遗传算法和可导向量的移动机器人路径规划。

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摘要

The shortest/optimal path generation is essential for the efficient operation of a mobile robot. Recent advances in robotics and machine intelligence have led to the application of modem optimization method such as the genetic algorithm (GA), to solve the path-planning problem. However, the genetic algorithm path planning approach in the previous works requires a preprocessing step that captures the connectivity of the free-space in a concise representation. In this paper, GA path-planning approach is enhanced with feasible path detection mechanism based on traversability vectors method. This novel idea eliminates the need of free-space connectivity representation. The feasible path detection is performed concurrently while the GA performs the search for the shortest path. The performance of the proposed GA approach is tested on three different environments consisting of polygonal obstacles with increasing complexity. In all experiments, the GA has successfully detected the near-optimal feasible traveling path for mobile.
机译:最短/最佳路径生成对于移动机器人的有效操作至关重要。机器人技术和机器智能的最新进展已导致应用现代优化方法(例如遗传算法(GA))来解决路径规划问题。然而,先前工作中的遗传算法路径规划方法需要预处理步骤,以简洁的方式捕获自由空间的连通性。本文通过基于可遍历向量法的可行路径检测机制,对遗传算法的路径规划方法进行了改进。这个新颖的想法消除了对自由空间连接表示的需求。可行路径检测是在GA执行最短路径搜索的同时执行的。所提出的遗传算法方法的性能在由多边形障碍物组成的三种不同环境中进行了测试,并且复杂度不断提高。在所有实验中,GA已成功检测到移动设备的最佳可行行进路径。

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