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Immune–wavelet optimization for path planning of large-scale robots

Published online by Cambridge University Press:  19 July 2013

Saeid Asadi ,
Vahid Azimirad ,
Ali Eslami  and
Saeid Karimian Eghbal
Saeid Asadi
Affiliation:
Biomechatronics Laboratory, School of Engineering Emerging Technologies, University of Tabriz, Tabriz, Iran
Vahid Azimirad*
Affiliation:
Biomechatronics Laboratory, School of Engineering Emerging Technologies, University of Tabriz, Tabriz, Iran
Ali Eslami
Affiliation:
Biomechatronics Laboratory, School of Engineering Emerging Technologies, University of Tabriz, Tabriz, Iran
Saeid Karimian Eghbal
Affiliation:
Biomechatronics Laboratory, School of Engineering Emerging Technologies, University of Tabriz, Tabriz, Iran
*
*Corresponding author. E-mail: [email protected]
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Summary

In this paper an optimal path planning method based on a new evolutionary algorithm is presented for higher order robotic systems. It is a combination of immune system and wavelet mutation. By increasing the system's dimensions, the complexity of algorithm grows linearly. The obtained results have been compared with other optimal path producing algorithms, and its excellence in terms of optimality has been proved. Strengths of this method are simplicity in large-scale path planning, being free of most of the common deadlocks in usual method, and ability to obtain more optimized results than other similar methods. The effectiveness of this approach on simulation case studies for a three-link planar robot and 5 degrees of freedom mobile manipulators as well as an experiment for a mobile robot called K-joniour is shown.

Keywords

Artificial immune systemWaveletPath planningLarge-scale robots
Type
Articles
Information
Robotica , Volume 32 , Issue 1 , January 2014 , pp. 77 - 95
Copyright
Copyright © Cambridge University Press 2013 

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References

1.Raja, P. and Pugazhenthi, S., “Optimal path planning of mobile robots: A review,” Int. J. Phys. Sci. 7 (9), 1314–1320 (2012).CrossRefGoogle Scholar
2.Moreno, L., Armingol, J. M., Garrido, S., De La Escalera, A. and Salichs, M. A., “A genetic algorithm for mobile robot localization using ultrasonic sensors,” J. Intell. Robot. Syst. 34, 135–154 (2002).CrossRefGoogle Scholar
3.Garcia, M. A. P., Montiel, O., Castillo, O., Sepulveda, R. and Melin, P., “Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation,” Appl. Soft Comput. 9, 1102–1110 (2009).CrossRefGoogle Scholar
4.Xianxiang, W., Baolong, G. and Juan, W., “Mobile robot path planning algorithm based on particle swarm optimization of cubic splines,” Robot 31 (6), 556–560 (2009).Google Scholar
5.Gyorfi, J. S., Gamota, D. R., Mok, S. M., Szczech, J. B., Toloo, M. and Zhang, J., “Evolutionary path planning with subpath constraints,” IEEE Trans. Electron. Packag. Manuf. 33, 143–151 (2010).CrossRefGoogle Scholar
6.Ching-Chih, T., Hsu-Chih, H. and Cheng-Kai, C., “Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation,” IEEE Trans. Ind. Electron. 58, 4813–4821 (2011).Google Scholar
7.Bin Hou, Y., Wang, W. and Yue Lu, X., “Mobile robot path planning and research in the improved artificial immune algorithm,” Adv. Mater. Res. 466, 864–869 (2012).Google Scholar
8.Das, P. K., Pradhan, S. K., Patro, S. N. and Balabantaray, B. K., “Artificial immune system-based path planning of mobile robot,” Stud. Comput. Intell. 395, 195–207 (2012).CrossRefGoogle Scholar
9.de Castro, L. N. and Von Zuben, F. J., “Immune-inspired somatic contiguous hypermutation for function optimization,” IEEE Trans. Evolut. Comput. 6, 239–251 (2002).CrossRefGoogle Scholar
10.Ling, S. and Leung, F., “An improved genetic algorithm with average-bound crossover and wavelet mutation operations,” Soft Comput. 11 (1), 7–31 (2007).CrossRefGoogle Scholar
11.Lai, J., Leung, F. and Ling, S., “A New Differential Evolution with Wavelet Theory-Based Mutation Operation,” In: Proceedings of the IEEE Congress on Evolutionary Computation (Trondheim, 2009) pp. 1116–1122.Google Scholar
12.Asadi, S., Azimirad, V. and Eslami, A., “A Novel Real-time Global Optimal Path Planning and Trajectory Method Based on Adaptive Dijkstra-Immune Approach for Mobile Robot,” In: Proceedings of the IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (Budapest, 2011) pp. 1093–1098.Google Scholar
13.Korayem, M. H., Azimirad, V., Vatanjou, H. and Korayem, A. H., “Maximum load determination of nonholonomic mobile manipulator using hierarchical optimal control,” Robotica 30, 53–65 (2011).CrossRefGoogle Scholar
14.Daubechies, I., Ten Lectures on Wavelets (Society for Industrical and Applied Mathematics, Philadelphia, PA, 1992).CrossRefGoogle Scholar
15.Crage, J., John, Introduction to Robotics-Mechancs and Control (Pearson, Upper Saddle River, NJ, 2005).Google Scholar
16.Wong, E. Y. C., Yeung, H. S. C. and Lau, H. Y. K., “Immunity-based hybrid evolutionary algorithm for multi-objective optimization in global container repositioning,” Eng. Appl. Artif. Intell. (Elsevier), 22, 842–854 (2009).CrossRefGoogle Scholar
17.Veldhuizen, D. A. V. and Lamont, G. B., “On Measuring Multiobjective Evolutionary Algorithm Performance,” In: Proceedings of Evolutionary Computation Congress (La Jolla, CA, 2000) pp. 204–211.Google Scholar
18.Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. A. M. T., “A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II,” IEEE Trans. Evolut. Comput. 6 (2), 182–197 (2002).CrossRefGoogle Scholar