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Bidirectional Homotopy-Guided RRT for Path Planning

机译:路径规划的双向同型rrt

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As a popular robot path planning algorithm, RRT (Rapid-exploring Random Tree) and various RRT-based extensions have achieved remarkable results. However, existing algorithms rely too much on randomness and often do not make use of known map information, this makes the growth of trees too blind. To this end, we introduce Bidirectional Homotopy-Guided RRT (BH-RRT) that combines Bidirectional RRT (Bi-RRT) with information obtained from obstacle contours. Compared with the previous methods, BH-RRT can reflect most of the map's information with a small number of feature points, and then form a set of points. This set can provide a better local goal point in each iteration, so that the tree can grow in a more favorable direction, rather than only being affected by the goal point. Experimental results show that BH-RRT outperforms RRT, HRRT and Bi-RRT in the success rate within a limited time.
机译:作为一种流行的机器人路径规划算法,RRT(快速探索随机树)和基于各种基于RRT的扩展都取得了显着的结果。然而,现有的算法依赖于随机性过多,并且通常不会利用已知地图信息,这使得树木的生长过于盲目。为此,我们引入双向同型引导RRT(BH-RRT),将双向RRT(BI-RRT)结合在障碍轮廓中获得的信息。与以前的方法相比,BH-RRT可以反映具有少量特征点的地图的信息,然后形成一组点。该集合可以在每次迭代中提供更好的本地目标点,使树可以以更有利的方向生长,而不是仅受目标点的影响。实验结果表明,BH-RRT在有限的时间内以成功率占RRT,HRRT和BI-RRT。

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