未知场景下户外扫描机器人多轨迹路径规划

Sheng Liu
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引用次数: 0

摘要

随着数字时代的不断发展,户外环境的自动扫描带来了挑战。目前户外移动机器人扫描存在两个主要问题:一是扫描路径过长;其次,容易与障碍物碰撞,本文将重点研究以上两个问题。首先,对室外场景构造拓扑结构图,得到可达点集;首先利用贪心算法对高勘探值区域进行探索,然后利用蚁群算法对一般勘探值区域进行路径规划。其次,我们对蚁群算法进行了算法改进,采用了允许决策者获得多个解的多轨迹路径规划算法,提出了负反馈蚁群算法,引入了引导信息素和警示信息素,使蚁群算法能够综合考虑环境带来的影响进行探索。最后,对于本文提出的算法,我们分别使用点云图和栅格法进行了仿真实验,我们提出的算法在点云图上具有更好的性能,在移动相同距离的情况下,改进的蚁群算法比原算法的扫描范围提高了4%左右。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Track Path Planning of Outdoor Scanning Robot in Unknown Scene
As the digital age continues to evolve, automated scanning of outdoor environments ushers in challenges. There are currently two main problems with outdoor mobile robot scanning: firstly, the scanning path is too long; secondly, it is easy to collide with obstacles, and this thesis will focus on the above two problems. Firstly, a topological structure graph is constructed for the outdoor scene, and the set of accessible points is obtained. The high exploration value region is first explored by the greedy algorithm, and then the ant colony algorithm is used for path planning of the general exploration value region. Secondly, we make algorithmic improvements to the ant colony algorithm by adopting a multi-trajectory path planning algorithm that allows decision makers to obtain multiple solutions, proposing a negative feedback ant colony algorithm, and introducing guidance pheromones and alert pheromones to enable the ant colony algorithm to explore with a comprehensive consideration of the effects brought about by the environment. Finally, for the algorithm proposed in this thesis, we conducted simulation experiments using point cloud map and raster method respectively, our proposed algorithm has better performance on the map, and the improved ant colony algorithm can improve the scanning range by about 4% than the original algorithm when moving the same distance.
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