NURBS curve interpolation strategy for smooth motion of industrial robots

被引:0
|
作者
Guo, Yonghao [1 ]
Niu, Wentie [1 ]
Liu, Hongda [1 ]
Zhang, Zengao [1 ]
Zheng, Hao [1 ]
机构
[1] Tianjin Univ, Key Lab Mech Theory & Equipment Design, Minist Educ, Tianjin 300350, Peoples R China
关键词
NURBS interpolation; Industrial robots; Dynamics constraint; Interpolation output feedrate; Contour error; Roughness; FEEDRATE SCHEDULING METHOD; PARALLEL MANIPULATOR; PATH; OPTIMIZATION; JERK;
D O I
10.1016/j.mechmachtheory.2024.105885
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Smooth motion is crucial for industrial robots to efficiently execute accurate path tracking tasks. This paper proposes a NURBS curve interpolation strategy for smooth motion of industrial robots to reduce roughness and contour error. The strategy ensures smooth motion through two stages: feedrate planning and interpolation point parameter calculation. During the feedrate planning stage, kinematics and dynamics constraints, including torque and torque change rate, are considered in the parameter domain. Round-off error is considered, and an S-curve feedrate planning approach is employed to ensure the planned feedrate is smooth after transitioning from the parameter domain to the time domain. In the interpolation point parameter calculation stage, the displacement guidance curve is generated and updated based on the current situation. Interpolation point iteration compensation is conducted to ensure the interpolation output feedrate is smooth. Simulations and experiments are conducted to validate the effectiveness of the proposed strategy. The simulation results indicate that the proposed strategy effectively smooths the interpolation output feedrate while maintaining efficiency. The experimental results show that the strategy effectively reduces roughness and contour error.
引用
收藏
页数:25
相关论文
共 50 条
  • [21] Research on a new linear interpolation algorithm of NURBS curve
    Li Lijun
    Bai Weitao
    Sun Wei
    2013 INTERNATIONAL CONFERENCE ON MECHANICAL AND AUTOMATION ENGINEERING (MAEE 2013), 2013, : 80 - 84
  • [22] Fast NURBS interpolation based on the biarc guide curve
    Jichun Wu
    Huicheng Zhou
    Xiaoqi Tang
    Jihong Chen
    The International Journal of Advanced Manufacturing Technology, 2012, 58 : 597 - 605
  • [23] Research on Fast Recursive Algorithm for NURBS Curve Interpolation
    Zhang, Wanjun
    Gao, Shanping
    Cheng, Xiyan
    Zhang, Feng
    2017 5TH INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN, MANUFACTURING, MODELING AND SIMULATION (CDMMS 2017), 2017, 1834
  • [24] Research on an Innovative Modification Algorithm of NURBS Curve Interpolation
    Zhang, Wanjun
    Gao, Shanping
    Cheng, Xiyan
    Zhang, Feng
    2017 5TH INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN, MANUFACTURING, MODELING AND SIMULATION (CDMMS 2017), 2017, 1834
  • [25] FPGA IMPLEMENTATION OF NURBS INTERPOLATION FOR MOTION CONTROL
    Chen Youdong
    Gu Pingping
    Yan Liang
    PROGRESS OF MACHINING TECHNOLOGY, 2012, : 153 - 156
  • [26] Fast NURBS interpolation based on the biarc guide curve
    Wu, Jichun
    Zhou, Huicheng
    Tang, Xiaoqi
    Chen, Jihong
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2012, 58 (5-8): : 597 - 605
  • [27] Neural network methods for NURBS curve and surface interpolation
    Tsinghua Univ, Beijing, China
    J Comput Sci Technol, 1 (76-89):
  • [28] Development and implementation of a NURBS curve motion interpolator
    Zhang, QYG
    Greenway, RB
    ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 1998, 14 (01) : 27 - 36
  • [29] Analysis of Velocity Planning Interpolation Algorithm based on NURBS Curve
    Zhang, Wanjun
    Gao, Shanping
    Cheng, Xiyan
    Zhang, Feng
    2017 5TH INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN, MANUFACTURING, MODELING AND SIMULATION (CDMMS 2017), 2017, 1834
  • [30] Interpolation Algorithm for NURBS Curve with Scheduled Feedrate on Curvature Extreme
    Ji, Guo-Shun
    Yu, Wu-Jia
    Chen, Zhi-Ping
    PROCEEDINGS OF THE 2015 4TH INTERNATIONAL CONFERENCE ON SENSORS, MEASUREMENT AND INTELLIGENT MATERIALS, 2016, 43 : 103 - 107