本文为美国德克萨斯A&M大学（YAN WU）的硕士论文，共79页。

本文提出了一种新的基于障碍物的多自由度机器人运动规划概率路线图方法，该方法可以在机器人空间拥挤的情况下获得高质量路线图。这种方法的主要创新之处在于，路线图候选点选择在工作空间中与障碍物相对应的约束曲面上。因此，路线图可能包含困难的路径，例如在机器人配置中穿越长而窄的通道空间。该空间方法可用于无碰撞路径规划和接触任务规划。针对平面关节机器人在具有多边形障碍物的二维工作空间中的路径规划问题，提出了一种基于该方法的路径规划。对各种类型的机器人和一系列环境的实验结果表明，即使路线图中的节点数很小，大多数环境都能生成连接良好的路线图。在预处理过程中建立路线图之后，许多困难的路径规划操作都在不到一秒钟的时间内完成。

This thesis presents a new obstacle-based probabilistic roadmap method for motion planning for many degree of freedom robots that can be used to obtain high quality roadmaps even when the robot's conguration space is crowded. The main novelty in this approach is that roadmap candidate points are chosen on the constraints urfaces corresponding to obstacles in the workspace. As a consequence, the roadmap is likely to contain difficult paths, such as those traversing long, narrow passages in the robot's conguration space.The approach can be used for both collision-free path planning and for planning contact tasks. A path planner based on this approach is implemented for planar articulated robots in a two-dimensional workspace with polygonal obstacles. Experimental results with various types of robots and a range of environments show that well connected roadmaps are generated for most environments, even when the number of nodes in the roadmap is small. After the roadmap is built during preprocessing, many difficult path planning operations are carried out in less than a second.

1.       引言

2. 路径规划方法概述

3. 基于障碍物的概率路线图方法

4. 具体实现

5. 实验结果与分析

6. 结论

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