Risk-RRT∗: A robot motion planning algorithm for the human robot coexisting environment
Risk-RRT∗: A robot motion planning algorithm for the human robot coexisting environment
复制标题
Risk-RRT*：人机共存环境下的机器人运动规划算法
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
M. Meng
中科院分区：
文献类型：
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作者：
Wenzheng Chi;M. Meng
在人-机器人共存的环境下，如何高效、安全地达到目标，对于移动服务机器人来说是非常有意义的。本文将舒适与碰撞风险（CCR）映射与RRT∗算法相结合，提出了一种基于风险的快速探索随机树最优运动规划（Risk-RRT∗）算法，提供了动态人机共存环境下RRT∗算法的一种变体。在实验中，利用导航过程的时间成本和轨迹长度对算法进行了评价。与Risk-RRT算法进行了比较，实验结果表明，本文提出的算法在静态和动态环境下都能取得比Risk-RRT算法更好的性能。
In the human robot coexisting environment, to reach the goal efficiently and safely is very meaningful for the mobile service robot. In this paper, a Risk based Rapidly-exploring Random Tree for optimal motion planning (Risk-RRT∗) algorithm is proposed by combining the comfort and collision risk (CCR) map with the RRT∗ algorithm, which provides a variant of the RRT∗ algorithm in the dynamic human robot coexisting environment. In the experiments, the time cost in the navigation process and the length of the trajectory are utilized for the evaluation of the proposed algorithm. A comparison with the Risk-RRT algorithm is carried out and experimental results reveal that our proposed algorithm can achieve a better performance than that of the Risk-RRT in both static and dynamic environments.