Solution for Forward Kinematics of 6-DOF Parallel Robot Based on Particle Swarm Optimization

The analysis of the forward kinematics is the foundation for studying other performances of the parallel robot. Making use of the property that it is easy to obtain the inverse kinematics of 6-DOF parallel robot, the forward kinematics of the 6-DOF parallel robot is transformed by using inverse kinematics results through training and learning. The nonlinear mapping from the joint variable space to the operation variable space for the platform is accomplished solving the location and posture. The BP neural network is used to solve the forward kinematics, and the particle swarm optimization is applied to train the neural network. Simulation results show that this approach can be used for the online control of parallel robot with faster computing speed and more accurate solution.

[1]  Liu Hong-zhao A Solution for Forward Kinematics of 6-DOF Parallel Platform Based on Genetic Algorithm and Neural Network , 2004 .

[2]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[3]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[4]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[5]  Y. Rahmat-Samii,et al.  Particle swarm optimization in electromagnetics , 2004, IEEE Transactions on Antennas and Propagation.

[6]  G. Ming Model Analysis of Particle Swarm Optimizer , 2006 .

[7]  Qingsong Xu,et al.  Kinematic Analysis and Optimization of a New Compliant Parallel Micromanipulator , 2006 .

[8]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[9]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[10]  J. Kennedy,et al.  Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[11]  Huang Shao-rong,et al.  Survey of particle swarm optimization algorithm , 2009 .

[12]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[13]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[14]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[15]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).