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Mobile robot path planning in complex environments using ant colony optimization algorithm

机译:蚁群算法在复杂环境中的移动机器人路径规划

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Ant Colony Optimization (ACO) algorithm has been applied to solve the path planning problem of mobile robot in complex environments. The algorithm parameters have been analysed and tuned for different working areas with obstacles in different number, sizes and shapes. Also, the performance of ACO algorithm was tested for different resolutions of working area representations. In all cases, it was possible to find optimal or near-optimal minimum-length paths from the initial to final desired positions without collision with obstacles or wall-borders.
机译:蚁群优化(ACO)算法已被应用于解决复杂环境中移动机器人的路径规划问题。已经针对具有不同数量,大小和形状的障碍物的不同工作区域对算法参数进行了分析和调整。此外,针对工作区域表示的不同分辨率测试了ACO算法的性能。在所有情况下,都有可能找到从初始到最终所需位置的最佳或接近最佳的最小长度路径,而不会与障碍物或墙沿碰撞。

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