机器人2024，Vol.46Issue(3)：275-283,9.DOI:10.13973/j.cnki.robot.230128
基于分段动态运动基元的机械臂轨迹学习与避障方法
Robotic Arm Trajectory Learning and Obstacle Avoidance Method Based on Segmented Dynamic Movement Primitive
刘暾东 1张馨月 1林晨滢 1吴晓敏 1苏永彬1
作者信息
- 1. 厦门大学萨本栋微米纳米科学技术研究院,福建厦门 361102
- 折叠
摘要
Abstract
Aiming at the problem of low similarity between the planned robotic arm movement trajectory and the demon-stration trajectory in complex work scenes with obstacles,a trajectory learning and obstacle avoidance method based on segmented DMP(dynamic motion primitive)is proposed.Firstly,the DMP model is adopted to encode the demonstration trajectory to generate a learning trajectory,and the rapidly-exploring random tree(RRT)is utilized also to obtain an obstacle avoidance path in the workspace that can return to the original trajectory smoothly.Then,the intermediate point of obstacle avoidance path is determined by segmented DMP optimization coding to learn and generate the playback trajectory with generalization ability.Finally,the robotic arm reproduces the trajectory,achieving obstacle avoidance while preserving the original trajectory characteristics.The handwritten letters experiment and the object handling experiment on the six-axis robotic arm platform show that the trajectory deformation and feature destruction caused by the traditional obstacle avoid-ance algorithms,is effectively solved by DMP segmentation coding.The experimental result trajectory shows a significant improvement in similarity to the demonstration trajectory compared to the traditional obstacle avoidance algorithms,which verifies the effectiveness of the method proposed.关键词
示教学习/轨迹学习/动态运动基元/快速扩展随机树/避障轨迹Key words
learning from demonstration/trajectory learning/dynamic motion primitive/rapidly-exploring random tree/obstacle avoidance trajectory引用本文复制引用
刘暾东,张馨月,林晨滢,吴晓敏,苏永彬..基于分段动态运动基元的机械臂轨迹学习与避障方法[J].机器人,2024,46(3):275-283,9.
