基于确定性采样的机器人运动规划方法

Yulan Hu, Qisong Zhang
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引用次数: 0

摘要

随着机器人应用环境复杂性的不断增加,传统的运动规划算法无法克服模型在不确定性空间中的障碍并对问题进行描述,特别是在未知环境中,受到环境信息量的限制,传统的运动规划算法可能无法运行。基于采样的运动规划仅通过配置空间的采样点来获取障碍物碰撞检测信息,避免了运行空间、建模,完全适用于复杂未知环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robot Motion Planning Method Based on Deterministic Sampling
With the robotic application of environmental complexity increasing, the traditional motion planning can not overcome the obstacles in the uncertainty space of the model and describe the problem, especially in an unknown environment, subject to environmental restrictions on the amount of information, the traditional sports planning algorithm may not run. Sampling-based motion planning is only through the configuration space of the sampling points to obtain obstacle collision detection information, to avoid running space, modeling, fully applicable to complex and unknown environment.
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