基于概率路标的机器人狭窄通道路径规划

针对机器人工作空间中存在狭窄通道时,基于概率路标图的路径规划法不能有效提高狭窄通道中路标分布的合理性,研究一种基于狭窄通道辨识的混合路标规划法的混合路标采集策略,利用星形试验法辨识出狭窄通道形状,增加狭窄通道中的路标密度,使全局路标分布合理化,提高了路径规划的效率.二维和三维配置空间中的仿真实验验证了该算法的有效性.

Robot path planning in narrow passage based on probabilistic roadmapmethod

The performance of the path planner based on probabilistic roadmap method often degrades seriously because
of the irrational distribution of roadmaps, when narrow passages exist in the robot’s configuration space. Therefore, a
hybrid roadmap sampling strategy, hybrid roadmap planner based on narrow passage recognition is proposed. This strategy
recognizes narrow passages by using the proposed randomized star builder to increase the roadmap density within narrow
passages and derive rational distribution of roadmaps, which improves the efficiency of path planning. The experiments in
2D and 3D configuration space show the effectiveness of the proposed algorithm.