Mobile Robot Motion Planning to Avoid Obstacle Using Modified Ant Colony Optimization

Abstract:

The ability of mobile robot to move about the environment from initial position to the goal position, without colliding the obstacles is needed. This paper presents about motion planning of mobile robot (MR) in obstacles-filled workspace using the modified Ant Colony Optimization (M-ACO) algorithm combined with the point to point (PTP) motion in achieving the static goal. Initially, MR try to plan the path to reach a goal, but since there are obstacles on the path will be passed through so nodes must be placed around the obstacles. Then MR do PTP motion through this nodes chosen by M-ACO, in order to form optimal path from the choice nodes until the last node that is free from obstacles. The proposed approach shows that MR can not only avoid collision with obstacle but also make a global planning path. The simulation result have shown that the proposed algorithm is suitable for MR motion planning in the complex environments with less running time.

• Periodical: Applied Mechanics and Materials (Volume 776)
• Pages: 396-402
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.776.396
• Online since: July 2015
• Authors: Nukman Habib*, Adi Soeprijanto, Djoko Purwanto, Mauridhi Hery Purnomo
• Keywords: Ant Colony Optimization, Mobile Robot, Motion Planning, Obstacle Avoidance

References

[1] M. Elbanhawi and M. Simic, Sampling-Based Robot Motion Planning: A Review, IEEEXplore, Vol. 2, Feb, (2014).

[2] Guo Y., Shen X., Zhu Z., Based on ant colony algorithm to solve the mobile robots intelligent path planning for avoid obstacles, International Journal of Artificial Intelligence & Application (IJAIA), Vol. 5, No. 1, January (2014).

DOI: 10.5121/ijaia.2014.5101

[3] P. Raja and S. Pugazhenthi, Optimal path planning of mobile robots: A review, International Journal of Physical Sciences Vol. 7(9), pp.1314-1320, 23 Feb, (2012).

[4] Q. Zhu, J. Hu, W. Cai, L. Henschen, A new robot navigation algorithm for dynamic unknown environments based on dynamic path re-computation and an improved scout ant algorithm, Applied Soft Computing 11 (8), 4667–4676, (2011).

DOI: 10.1016/j.asoc.2011.07.016

[5] Garcia MAP, Montiel O, Castillo O, Sepulveda R, Melin P. Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost evaluation. Appl. Soft Comput., 9: 1102-1110, (2009).

DOI: 10.1016/j.asoc.2009.02.014

[6] Guan-zheng T, Huan HE, Sloman A. Global optimal path planning for mobile robot based on improved Dijkstra algorithm and ant system algorithm. J. Cent. South Univ. Technol., 13(1): 80-86, (2006).

DOI: 10.1007/s11771-006-0111-8

[7] Hao Mei, Tian Y, Zu L. A hybrid ant colony optimization algorithm for path planning of robot in dynamic environment. Int. Journal Inf. Tech., 12(3): 78-87, (2006).

[8] Dorigo, M., Maniezzo, V., Colorni, A., The Ant System: Optimization by a colony of cooperating agents, IEEE Trans. on Systems, Man, and Cybernetics, Vol 26, No. 1, 1996, p.1 – 13.

DOI: 10.1109/3477.484436

[9] M. Mohamad, N. Taylor, and M. Dunnigan, Articulated Robot Motion Planning Using Ant Colony Optimisation, Proc. IEEE Int. Conf. on Intelligent Systems, London, 2006, pp.690-695.

DOI: 10.1109/is.2006.348503

[10] S. Liu, L. Mao, and J. Yu, Path Planning Based on Ant Colony Algorithm and Distributed Local Navigation for Multi-Robot Systems, Proc. IEEE Int. Conf. on Mechatronics and Automation, Luoyang, China, 2006, pp.1733-1738.

DOI: 10.1109/icma.2006.257476

[11] SH. Chia, KL. Su, JH. Guo, and CY. Chung, Ant Colony System Based Mobile Robot Path Planning, Fourth International Conference on Genetic and Evolutionary Computing, (2010).

DOI: 10.1109/icgec.2010.59