Hybrid Optimal Kinematic Parameter Identification for an Industrial Robot Based on BPNN-PSO

A novel hybrid algorithm that employs BP neural network (BPNN) and particle swarm optimization (PSO) algorithm is proposed for the kinematic parameter identification of industrial robots with an enhanced convergence response. The error model of the industrial robot is established based on a modified Denavit-Hartenberg method and Jacobian matrix. Then, the kinematic parameter identification of the industrial robot is transformed to a nonlinear optimization in which the unknown kinematic parameters are taken as optimal variables. A hybrid algorithm based on a BPNN and the PSO is applied to search for the optimal variables which are used to compensate for the error of the kinematic parameters and improve the positioning accuracy of the industrial robot. Simulations and experiments based on a realistic industrial robot are all provided to validate the efficacy of the proposed hybrid identification algorithm. The results show that the proposed parameter-identification method based on the BPNN and PSO has fewer iterations and faster convergence speed than the standard PSO algorithm.

[1]  Liu Guang-bin,et al.  An Improved Particle Swarm Optimization Algorithm with Immunity , 2009, 2009 Second International Conference on Intelligent Computation Technology and Automation.

[2]  Youping Chen,et al.  A novel calibration method for multi-robots system utilizing calibration model without nominal kinematic parameters , 2017 .

[3]  Fang Lijin,et al.  Kinematic Calibration Method of Robots Based on Quantum-behaved Particle Swarm Optimization , 2016 .

[4]  Jingfu Jin,et al.  Parameter identification for industrial robots with a fast and robust trajectory design approach , 2015 .

[5]  José Mauricio S. T. Motta,et al.  Theoretical and practical aspects of robot calibration with experimental verification , 2011 .

[6]  In-Won Park,et al.  Laser-Based Kinematic Calibration of Robot Manipulator Using Differential Kinematics , 2012, IEEE/ASME Transactions on Mechatronics.

[7]  Wen Wang,et al.  Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network , 2017, Complex..

[8]  Minzhou Luo,et al.  A Precise Positioning Method for a Puncture Robot Based on a PSO-Optimized BP Neural Network Algorithm , 2017 .

[9]  Chun-Yi Su,et al.  A product-of-exponential-based robot calibration method with optimal measurement configurations , 2017 .

[10]  Jian Zhou,et al.  A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network , 2015, Neurocomputing.

[11]  Fuad E. Alsaadi,et al.  A switching delayed PSO optimized extreme learning machine for short-term load forecasting , 2017, Neurocomputing.

[12]  Qiang Huang,et al.  A New Kind of Accurate Calibration Method for Robotic Kinematic Parameters Based on the Extended Kalman and Particle Filter Algorithm , 2018, IEEE Transactions on Industrial Electronics.

[13]  Zhijiang Du,et al.  A universal index and an improved PSO algorithm for optimal pose selection in kinematic calibration of a novel surgical robot , 2018 .

[14]  Ji Zhao,et al.  Calibration of a parallel mechanism in a serial-parallel polishing machine tool based on genetic algorithm , 2015 .

[15]  Wenjun Yang,et al.  Influence of probe dynamic characteristics on the scanning speed for white light interference based AFM , 2018 .

[16]  Yongji Wang,et al.  Legendre Cooperative PSO Strategies for Trajectory Optimization , 2018, Complex..

[17]  Xiaojun Liu,et al.  Non-linear dynamic compensation and re-sampling for tactile scanning measurement of curved surface topography based on GPS standards , 2012 .

[18]  John J. Craig Zhu,et al.  Introduction to robotics mechanics and control , 1991 .

[19]  Zhi-Ren Tsai,et al.  Robust digital design of continuous-time nonlinear control systems using adaptive prediction and random-local-optimal NARMAX model , 2013, Appl. Math. Comput..

[20]  Jorge Santolaria,et al.  Laser tracker-based kinematic parameter calibration of industrial robots by improved CPA method and active retroreflector , 2013 .

[21]  David Daney,et al.  Choosing Measurement Poses for Robot Calibration with the Local Convergence Method and Tabu Search , 2005, Int. J. Robotics Res..

[22]  Ilian A. Bonev,et al.  Absolute calibration of an ABB IRB 1600 robot using a laser tracker , 2013 .

[23]  Ye Shi.  Cartesian Non-holonomic Path Planning of Space Robot Based on Quantum-behaved Particle Swarm Optimization Algorithm , 2011 .

[24]  Roger Miranda-Colorado,et al.  Experimental parameter identification of flexible joint robot manipulators , 2018, Robotica.

[25]  Ilian A. Bonev,et al.  Absolute accuracy analysis and improvement of a hybrid 6-DOF medical robot , 2015, Ind. Robot.

[26]  Yu Guo,et al.  A SELF-CALIBRATION METHOD FOR ARTICULATED ARM COORDINATE MEASURING MACHINES , 2016 .

[27]  Cheng Cheng,et al.  Prediction of facial deformation after complete denture prosthesis using BP neural network , 2015, Comput. Biol. Medicine.