基于DMP-RRT的机械臂轨迹学习与避障方法

doi:10.12341/jssms20224

系统科学与数学 ›› 2022, Vol. 42 ›› Issue (2): 193-205.DOI: 10.12341/jssms20224

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基于DMP-RRT的机械臂轨迹学习与避障方法

金宇强¹, 仇翔^1,2, 刘安东^1,2, 张文安^1,2

1. 浙江工业大学信息工程学院, 杭州 310023;
2. 人机协作技术浙江省国际科技合作基地, 杭州 310023

收稿日期:2020-06-15 修回日期:2020-10-12 出版日期:2022-02-25 发布日期:2022-03-21
通讯作者: 张文安,Email:wazhang@zjut.edu.cn.
基金资助:
浙江省自然科学基金重大项目（LD21F030002），国家自然科学基金优青项目（61822311）资助课题.

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金宇强, 仇翔, 刘安东, 张文安. 基于DMP-RRT的机械臂轨迹学习与避障方法[J]. 系统科学与数学, 2022, 42(2): 193-205.

JIN Yuqiang, QIU Xiang, LIU Andong, ZHANG Wen'an. A Trajectory Learning and Obstacle Avoidance Method for Manipulators Based on DMP-RRT[J]. Journal of Systems Science and Mathematical Sciences, 2022, 42(2): 193-205.

A Trajectory Learning and Obstacle Avoidance Method for Manipulators Based on DMP-RRT

JIN Yuqiang¹, QIU Xiang^1,2, LIU Andong^1,2, ZHANG Wen'an^1,2

1. College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023;
2. Zhejiang Provincial International Science and Technology Cooperation Base, Human-Robot Collaboration Technology, Hangzhou 310023

Received:2020-06-15 Revised:2020-10-12 Online:2022-02-25 Published:2022-03-21

针对机械臂在工作空间中运动轨迹规划难的问题，文章提出了一种基于示教学习的轨迹学习与避障方法，该方法融合了高斯混合模型（GMM）、动态运动基元（DMP）和快速扩展随机树（RRT）方法.通过GMM表征预处理后的轨迹数据集，提取运动特征，优化轨迹点分布并回归生成示教轨迹.利用DMP模型对优化轨迹进行编码，学习生成复现轨迹，并具有一定的轨迹泛化能力.同时，在工作空间中存在障碍物的情况下，采用RRT算法在DMP生成轨迹的基础上进行修正，快速得到避障路径.最后在Franka-Panda七自由度机械臂平台上进行物块搬运实验，实现了机械臂抓取-放置作业的快速部署与精准操作，结果验证了文章所设计方法的有效性.

关键词： 示教学习, 轨迹生成, 避障, 抓取-放置作业

In order to avoid the shortcomings of the commonly used trajectory planning methods, such as the cubersome model coupling and the difficulties in the model operation, a trajectory generation and obstacle avoidance method is proposed for manipulators based on the Learning from Demonstration (LfD). This method combines Gaussian mixture model (GMM), dynamic motion primitive (DMP) and rapid extended random tree (RRT) methods after pretreating the data recorded by the robot platform. The gaussian mixture model, aiming to optimize the set of demonstration data, is employed to generate trajectories containing as many motion features as possible. DMP is used to model and generalize the movements. And then, the trajectory can be adjusted by RRT algorithm to meet the operation requirements in cases of complex environments with obstacles in different shapes. Finally, the pick-and-place experiments based on Franka manipulator validate the effectiveness of the proposed method.

Key words: Learning from demonstration, trajectory generation, obstacle avoidance, pick-and-place

MR(2010)主题分类:

93C95

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