Motion planning of unicycle-like robot using single RRT with branch and bound algorithm

Rui-Jun Yan, Jing Wu, Ji Yeong Lee
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Abstract

This paper presents the motion planning of unicycle-like robot using single Rapidly-exploring Random Tree (RRT) algorithm, combining with Branch and Bound (BB) method. RRT can construct the searching tree efficiently between initial configuration and goal configuration. Based on the constructed searching tree, the optimal path can be extracted with BB algorithm rapidly. Obstacles avoidance is realized by checking whether the contour points of robot locate in the space of obstacles. The simulation result in a complex environment with unicycle-like robot proves the validity of proposed algorithms.
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基于分支定界算法的单RRT类独轮车机器人运动规划
本文采用单快速探索随机树(RRT)算法,结合分支定界(BB)方法对类独轮车机器人进行运动规划。RRT可以有效地在初始配置和目标配置之间构建搜索树。在构造的搜索树的基础上,用BB算法快速提取出最优路径。避障是通过检查机器人的轮廓点是否在障碍物空间中来实现的。在复杂环境下的单轮机器人仿真结果验证了算法的有效性。
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