Abstract
A very important problem usually encountered in the study of robot manipulators is the inverse kinematics problem. The inverse kinematics control of a robotic manipulator requires solving non-linear equations having transcendental functions and involving time-consuming calculations. In this paper, a hybrid particle swarm optimization based on the behaviour of insect swarms and natural selection mechanism is firstly presented to optimize neural network (HPSONN) for manipulator inverse kinematics. Compared with the results of the fast back propagation learning algorithm (FBP), conventional genetic algorithm (GA) based elitist reservation (EGA), improved GA (IGA) and immune evolutionary computation (IEC), the simulation results verify the hybrid particle swarm optimization is more effective for manipulator inverse kinematics control than above most methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tabandeh, S., Clark, C., Melek, W.: Genetic Algorithm Approach to Solve for Multiple Solutions of Inverse Kinematics using Adaptive Niching and Clustering. IEEE Congress on Evolutionary Computation, 1815–1822 (2006)
Poon, J.K., Lawrence, P.D.: Manipulator Inverse Kinematics based on Joint Functions. In: IEEE Int. Conf. on Robotics and Automation, vol. 2, pp. 669–674 (1988)
Brown, M., Harris, C.: Neuralfuzzy Adaptive Modeling and control. Prentice Hall, Englewood Cliffs (1994)
Hussein, S.B., Jamaluddin, H., Mailah, M.: A Hybrid Intelligent Active Force Controller for Robot Arms Using Evolutionary Neural Networks. In: Proc. of Congress on Evolutionary Computation, vol. 1, pp. 117–124 (2000)
Kim, J.H., Lee, C.H.: Evolutionary Ordered Neural Network and Its Application to Robot Manipulator Control. In: IEEE International Conference on Industrial Electronics, Control, and Instrumentation, vol. 2, pp. 876–880 (1996)
Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the Theory of Neural Computation. Addison- Wesley, Reading (1991)
Li, M., Tam, H.Y.: Hybrid Evolutionary Search Method Based on Clusters. IEEE Trans. Pattern Analysis and Machine Intelligence 23, 786–799 (2001)
Wen, X.L., Xue, X.L., Zhang, P.: The Combination of Immune Evolution and Neural Network for Nonlinear Time Series Forecasting. In: Proc. of, Sixth International Symposium on Instrumentation and Control Technology, pp. 876–880 (2006)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc. IEEE Int. Conf. Neural Networks, vol. 4, pp. 1942–1948 (1995)
Seo, J.H., IM, C.H., Heo, C.C.: Multimodal Function Optimization Based on Particle Swarm Optimization. IEEE Trans. On Magnetics 42, 1095–1098 (2006)
Ren, P., Gao, L.Q., Li, N.: Transmission Network Optimal Planning Using the Particle Swarm Optimization Method. In: Proc. Int. Conf. on Machine Learning and Cybernetics, pp. 4006–4011 (2005)
Angeline, P.: Using Selection to Improve Particle Swarm Optimization. In: IEEE Int. Conf. Evol. Computation, Anchorage, Ak (1998)
Naka, S., Genji, T., Yura, T.: A Hybrid Particle Swarm Optimization for Distribution State Estimation. IEEE Trans. Power Syst. 18, 60–68 (2003)
Naka, S., Genji, T., Yura, T.: Swarm Intelligence. Morgan Kaufmann, San Mateo (2001)
Eberhart, R., Shi, Y.: Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization. In: Proc. Cong. Evolutionary Computation, pp. 84–88 (2000)
Baillieu1, J.: Kinematic Programming Alternatives for Redundant Manipulators. In: IEEE Int Conf. on Robotics and Automation, vol. 2, pp. 722–728 (1985)
Watanabe, B., Shimizu, H.: A Study of Generalization Ability of Neural Network for Manipulator Inverse Kinematics. In: IEEE Int Conf. on Industrial Electronics, Control and Instrumentation, vol. 2, pp. 957–962 (1991)
Wen, X.L., Song, A.G.: An Improved Genetic Algorithm for Plannar Straightness and Spatial Straightness Error Evaluation. International Journal of Machine Tools and Manufacture 43, 1077–1084 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wen, X., Sheng, D., Huang, J. (2008). A Hybrid Particle Swarm Optimization for Manipulator Inverse Kinematics Control. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_96
Download citation
DOI: https://doi.org/10.1007/978-3-540-87442-3_96
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87440-9
Online ISBN: 978-3-540-87442-3
eBook Packages: Computer ScienceComputer Science (R0)