| 刘小松,康磊,单泽彪,朱焕海,刘云清.基于双向目标偏置APF-informed-RRT*算法的机械臂路径规划[J].电子测量与仪器学报,2024,38(6):75-83 |
| 基于双向目标偏置APF-informed-RRT*算法的机械臂路径规划 |
| Path planning of robot arm based on APF-informed-RRT* algorithm with bidirectional target bias |
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| DOI： |
| 中文关键词: 路径规划 机械臂 双向目标偏置 人工势场 动态步长 |
| 英文关键词:path planning mechanical arm bidirectional target bias artificial potential field dynamic step size |
| 基金项目:吉林省教育厅产业化培育项目（JJKH20240940CY）、吉林省自然科学基金项目（YDZJ202301ZYTS412）、吉林省教育厅科学技术项目（JJKH20240938KJ）资助 |
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| Author | Institution |
| Liu Xiaosong | School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China |
| Kang Lei | School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China |
| Shan Zebiao | School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China |
| Zhu Huanhai | School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China |
| Liu Yunqing | School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China |
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| 中文摘要: |
| 针对当前机械臂路径规划算法存在搜索随机性大、目标偏置性差和路径曲折等问题，提出了一种基于双向目标偏置的APF-informed-RRT*算法。首先在双向informed-RRT*基础上引入概率自适应的目标偏置策略，降低搜索的随机性，提高采样效率；其次针对路径扩展在双向搜索树中融入人工势场法，减少算法的迭代次数；同时在路径生长阶段，采用动态步长生长策略，即根据搜索树的扩展趋势动态调整步长，避免出现局部最优，并且加快路径搜索时间；最后针对冗余节点采用三角不等式原理进行去除，进而通过B样条曲线对路径进行平滑处理，得到最优规划路径。通过与双向RRT*、双向informed-RRT*和双向P-RRT*等算法在三维环境下进行了仿真对比实验验证，相较于双向RRT*在时间上节约了41%，在采样点数量上减少了63%；相较于双向informed-RRT*在时间上节约了58%，在采样数量上减少了68%；相较于双向P-RRT*在时间上节约了30%，在采样数量上减少了60%。 |
| 英文摘要: |
| In view of the problems of large search randomness, poor target bias and path tortuousness in the current robotic arm path planning algorithm, an APF-informed-RRT* algorithm based on bidirectional target bias was proposed. Firstly, probabilistic adaptive target bias strategy is introduced based on bidirectional informed-RRT* to reduce the randomness of search and improve sampling efficiency. Secondly, for path expansion, the artificial potential field method is integrated into the two-way search tree to reduce the number of iterations of the algorithm. At the same time, in the path growth stage, the dynamic step growth strategy is adopted, that is, the step size is dynamically adjusted according to the expansion trend of the search tree, so as to avoid local optimization and speed up the path search time. Finally, the redundant nodes are removed by the principle of triangle inequality, and then the path is smoothed by B-spline curve to obtain the optimal planning path. The simulation and comparison experiments with bidirectional RRT*, bidirectional informed-RRT* and bidirectional P-RRT* are carried out in 3D environment. Compared with bidirectional RRT*, the time is saved by 41% and the number of sampling points is reduced by 63%. Compared with two-way informed-RRT*, 58% less time and 68% fewer samples are collected. Compared with bidirectional P-RRT*, it saves 30% in time and 60% in sampling quantity. |
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