引用本文:杨博涵,邢燕好,张佳,张华良,张建鹏.运行时间周期化工业机器人模型迭代寻优NURBS轨迹插补[J].控制理论与应用,2024,41(2):331~341.[点击复制]
YANG Bo-han,XING Yan-hao,ZHANG Jia,ZHANG Hua-liang,ZHANG Jian-peng.Industrial robot running time periodization and model iterative optimization NURBS trajectory interpolation[J].Control Theory and Technology,2024,41(2):331~341.[点击复制]
运行时间周期化工业机器人模型迭代寻优NURBS轨迹插补
Industrial robot running time periodization and model iterative optimization NURBS trajectory interpolation
摘要点击 3303  全文点击 137  投稿时间：2022-01-13  修订日期：2023-09-15
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DOI编号  10.7641/CTA.2022.20037
  2024,41(2):331-341
中文关键词  工业机器人  NURBS曲线  运行时间周期化  优化回溯算法  模型迭代寻优
英文关键词  industrial robots  NURBS curves  running time periodization  optimized backtracking algorithms  model iterative optimization
基金项目  国家重点研发计划项目(2018YFE0205802), 2021年辽宁省教育厅面上项目(LJKZ0135
作者单位E-mail
杨博涵 沈阳工业大学 信息科学与工程学院 756553080@qq.com 
邢燕好* 沈阳工业大学 信息科学与工程学院 E-mail: 10514150@qq.com 
张佳 沈阳工业大学 信息科学与工程学院  
张华良 中国科学院 沈阳自动化研究所 网络化控制系统重点实验室  
张建鹏 西北工业集团有限公司  
中文摘要
      为满足工业机器人高精度复杂曲线运动的需求, 本文提出运行时间周期化工业机器人模型迭代寻优NURBS轨迹插补算法. 首先, 根据轨迹最大轮廓误差和机器人动力学特性对曲线分段. 随后, 提出优化回溯算法,使各子曲线段均可用S曲线加减速规划. 之后, 为保证机器人在进给速度极小值处不超速, 将各加减速阶段运行时间调整为插补周期的整数倍, 并对子曲线段衔接处速度平滑处理. 最后, 提出模型迭代寻优曲线插补, 大大降低了速度波动率. 仿真试验表明, 该方法插补轨迹的各项指标均满足要求且最大速度波动率仅为0.000099%. 真机试验也验证了该方法可有效减小轨迹误差
英文摘要
      Aiming to meet the needs of high-precision complex curve motion of industrial robots, industrial robot running time periodization and model iterative optimization NURBS trajectory interpolation is proposed. To begin with, the curve is segmented according to the maximum chord error of the trajectory and the dynamic characteristics of the robot. After that, an optimized backtracking algorithm is set out to make S-curve acceleration and deceleration planning available for each sub curve segment. In addition, in order to ensure the robot does not overspeed at the minimum feedrate, the running time of each acceleration and deceleration stage is adjusted to an integral multiple of the interpolation cycle, and the feedrate at the junction of sub curve segments is smoothened. In the end, the model iterative optimization curve interpolation is put forward, which considerably decreases the feedrate fluctuation. The simulation results show that all the parameters of the interpolation trajectory meet the requirements, and the maximum feedrate fluctuation is only 0.000099%. The real robot test also verifies that this method can effectively reduce the trajectory error.