中国机械工程 ›› 2023, Vol. 34 ›› Issue (13): 1589-1598.DOI: 10.3969/j.issn.1004-132X.2023.13.008

• 智能制造 • 上一篇    下一篇

基于网格搜索算法的6-RUS并联机器人时间最优轨迹规划

刘栋财1,2;董广宇1,2;杜玉红1,2;李文鹏1,2   

  1. 1.天津工业大学机械工程学院,天津,300387
    2.天津市现代机电装备重点实验室,天津,300387
  • 出版日期:2023-07-10 发布日期:2023-07-25
  • 通讯作者: 杜玉红(通信作者),女,1974年生,教授、博士研究生导师。研究方向为机器人技术与智能控制及图像处理,工业机器人控制。发表论文20余篇。E-mail:dyh202@163.com。
  • 作者简介:刘栋财,男,1999年生,硕士研究生。研究方向为并联机器人柔顺控制。E-mail:Liudongcai_119591@163.com。
  • 基金资助:
    天津市科技计划(21YFFCYS00080)

Time-optimal Trajectory Planning of 6-RUS Parallel Robots Based on Grid Search Algorithm

LIU Dongcai1,2;DONG Guangyu1,2;DU Yuhong1,2;LI Wenpeng1,2   

  1. 1.School of Mechanical Engineering,Tiangong University,Tianjin,300387
    2.Key Laboratory of Advanced Mechatronics Equipment Technology,Tianjin,300387
  • Online:2023-07-10 Published:2023-07-25

摘要: 针对6-RUS并联喷涂机器人再现轨迹不平滑、轨迹规划效率低等问题,提出了基于优化贝塞尔曲线节点位置的6-RUS并联机器人时间最优轨迹规划方法。首先,将预处理的轨迹离散化为网格点,更新节点参数并优化贝塞尔曲线弧长,进一步拟合小线段路径获取最优几何路径;然后,计算不同粗网格点对应的最佳速度以及求解时间,选择合适的粗网格点,进一步以较小步长密化网格点间路径,迭代求解正反向最大速度,搜索路径的最佳速度曲线,获取6-RUS并联机器人的最佳运行时间。最后,在自研的6-RUS并联机器人平台上进行实验。结果表明,在相同示教轨迹条件下,基于所提的改进贝塞尔曲线算法得到的路径长为8.12 m,优于传统贝塞尔曲线算法以及G2CBS算法的结果;同时将改进的时间最优轨迹规划算法(TOPP)用于优化后的示教路径,所提算法的最优速度曲线的求解时间为416.4 ms,与TOPP-RA算法的最优速度曲线的求解时间相比缩短了244.7 ms,而且该算法下最优轨迹规划时间也优于TOPP-RA算法,该方法提高了最佳速度的求解速率,缩短了6-RUS并联机器人轨迹再现时间,提高了工作效率。

关键词: 6-RUS并联机器人, 改进的时间最优轨迹规划, 贝塞尔曲线, 网格搜索

Abstract: Aiming at the problems of uneven reproducing trajectory and low trajectory planning efficiency of 6-RUS parallel painting robots, a trajectory planning method of optimal time of 6-RUS parallel painting robots was proposed based on optimizing the position of Bézier curve nodes. Firstly, the preprocessed trajectory was discretized to grid points, the node parameters were updated and the arc length of Bézier curve was optimized, and the path of small line segment was further fitted to obtain the optimal geometric path. Then, the optimal velocity corresponding to different coarse grid points and the solution time were calculated, and the appropriate coarse grid points were selected. The paths between grid points were further densified by small steps, and the forward and backward maximum velocity were iteratively solved. The maximum feasible velocity curve of the path was searched to obtain the optimal running time of the 6-RUS parallel robots. Finally, the experiments were carried out on a self-developed 6-RUS parallel robot platform. The results show that under the same teaching trajectory, the path length of the improved Bessel curve algorithm herein is as 8.12 m, which is better than that of traditional Bézier curve and G2CBS(G2 continuous cubic Bézier spiral)aigorithm. Meanwhile, the improved time-optimal path parameterization algorithm herein was used for the optimized teaching path. The solution time of the optimal velocity curve of the algorithm herein is as 416.4 ms, which is 244.7 ms less than that of TOPP-RA algorithm. Moreover, the time-optimal trajectory planning time of the algorithm herein is also better than that of TOPP-RA algorithm. The method improves the solving speed of the optimal velocity, shortens the track reproduction time of 6-RUS parallel robots, and improves the working efficiency. 

Key words: 6-RUS parallel robot, improved time-optimal path parameterization, Bézier curve, grid search

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