Path Planning of Robot Based on Ant Colony Optimization Algorithm

Bao Ming Shan; De Xiang Zhang

doi:10.4028/www.scientific.net/AMM.614.199

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 614Path Planning of Robot Based on Ant Colony...

Path Planning of Robot Based on Ant Colony Optimization Algorithm

Abstract:

This paper presents a method for robot path planning based on ant colony optimization algorithm, in order to resolve the weakness of ant colony algorithm such as slow convergence rate and easy to fall into local optimum and traps. This method uses anti-potential field to make the robot escape from them smoothly, and at the end of each cycle, uses the way of judge first and then hybridization to optimize the algorithm. Finally, the simulation results show that the performance of the algorithm has been improved, and proved that the optimization algorithm is valid and feasible.

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Periodical:

Applied Mechanics and Materials (Volume 614)

Pages:

199-202

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.614.199

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Online since:

September 2014

Authors:

Bao Ming Shan*, De Xiang Zhang

Keywords:

Ant Colony Algorithm, Anti-Potential Field, Hybrid Optimization, Robot

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* - Corresponding Author

References

[1] X.S. Jiang, Introduction to Robotics [M], Liaoning: Liaoning Science and Technology Press, (1994).

Google Scholar

[2] Dorigo M, Maniezzo V, Colorni A. The ant system: optimization by a colony of cooperating agents [J]. IEEE Transactions on Systems Man an Cybernetics-Part B: Cybernetics, 1996, (01): 29-41.

DOI: 10.1109/3477.484436

Google Scholar

[3] Q.H. Wu, Y. Zhang and Z.M. Ma, Review of Ant Colony Optimization [J]. Microcomputer Information, 2007, 27(3): 1-2, 5.

Google Scholar

[4] C.M. Ren and J.X. Zhang, Robot Path Planning Based on Improved Ant Colony Optimization [J]. Computer Engineering, 2008, 34(15): 1-3, 35.

Google Scholar

[5] Y. Guo and S.Y. Li, Path Planning for Robot Based on Improved Ant Colony Algorithm [J]. Computer Measurement & Control, 2009, 17(1): 187-189, 206.

Google Scholar

[6] J. L Wang, G.L. Zhang and F. Yang, Improved artificial field method on obstacle avoidance path planning for manipulator [J]. Computer Engineering and Applications, 2013, 49(21): 266-270.

Google Scholar