Fuzzy Logic-Ant Colony Optimization for Explorer-Follower Robot with Global Optimal Path Planning

Bambang Tutuko; Siti Nurmaini; Ganesha Ogi

doi:10.18495/comengapp.v7i1.241

[1] M. Senanayake, I. Senthooran, J. C. Barca, H. Chung, J. Kamruzzaman, and M. Murshed, “Search and tracking algorithms for swarms of robots: A survey,” Rob. Auton. Syst., vol. 75, pp. 422–434, 2016.
[2] Y. Mohan and S. G. Ponnambalam, “An extensive review of research in swarm robotics,” in Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, 2009, pp. 140–145.
[3] C. Blum and X. D. Li, “Swarm intelligence in optimization. Swarm Intelligence,” Nat. Comput. Ser., pp. 43–85, 2008.
[4] L. Yin, Y. Yin, and C.-J. Lin, “A new potential field method for mobile robot path planning in the dynamic environments,” Asian J. Control, vol. 11, no. 2, pp. 214–225, 2009.
[5] F. Abdessemed et al., “A hierarchical fuzzy control design for indoor mobile robot,” Int. J. Adv. Robot. Syst., vol. 11, no. 3, p. 33, 2014.
[6] M. K. Singh and D. R. Parhi, “Path optimisation of a mobile robot using an artificial neural network controller,” Int. J. Syst. Sci., vol. 42, no. 1, pp. 107–120, 2011.
[7] A. H. Karami and M. Hasanzadeh, “An adaptive genetic algorithm for robot motion planning in 2D complex environments,” Comput. Electr. Eng., vol. 43, pp. 317–329, 2015.
[8] S. Liu, Y. Tian, and J. Liu, “Multi mobile robot path planning based on genetic algorithm,” in Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on, 2004, vol.5, pp. 4706–4709.
[9] Y.-Q. Qin, D.-B. Sun, N. Li, and Y.-G. Cen, “Path planning for mobile robot using the particle swarm optimization with mutation operator,” in Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on, 2004, vol. 4, pp. 2473–2478.
[10] F. Belkhouche, “Reactive path planning in a dynamic environment,” IEEE Trans. Robot., vol. 25, no. 4, pp. 902–911, 2009.
[11] P. K. Das, H. S. Behera, and B. K. Panigrahi, “A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning,” Swarm Evol. Comput., vol. 28, pp. 14–28, 2016.
[12] C. Luo, H. Mo, F. Shen, and W. Zhao, “Multi-goal motion planning of an autonomous robot in unknown environments by an ant colony optimization approach,” in International Conference in Swarm Intelligence, 2016, pp. 519–527.
[13] J. E. Bell and P. R. McMullen, “Ant colony optimization techniques for the vehicle routing problem,” Adv. Eng. informatics, vol. 18, no. 1, pp. 41–48, 2004.
[14] M. A. P. Garcia, O. Montiel, O. Castillo, R. Sepúlveda, and P. Melin, “Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation,” Appl. Soft Comput., vol. 9, no. 3, pp. 1102–1110, 2009.
15] M. Dorigo and T. Stützle, “Ant colony optimization: overview and recent advances,” Techreport, IRIDIA, Univ. Libr. Bruxelles, vol. 8, 2009.
[16] E. Bonabeau, M. Dorigo, and G. Theraulaz, “Inspiration for optimization from social insect behaviour,” Nature, vol. 406, no. 6791, p. 39, 2000.
[17] M. Dorigo, M. Birattari, C. Blum, M. Clerc, T. Stützle, and A. Winfield, Ant Colony Optimization and Swarm Intelligence: 6th International Conference, ANTS 2008, Brussels, Belgium, September 22-24, 2008, Proceedings, vol. 5217. Springer, 2008.
[18] M. Mavrovouniotis and S. Yang, “Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors,” Appl. Soft Comput., vol. 13, no. 10, pp. 4023–4037, 2013.
[19] S. Sural, G. Qian, and S. Pramanik, “Segmentation and histogram generation using the HSV color space for image retrieval,” in Image Processing. 2002. Proceedings. 2002 International Conference on, 2002, vol. 2, pp. II--II.

Abstract viewed - 1255 times
PDF downloaded - 1288 times

Affiliations

Bambang Tutuko
Faculty of Computer Science Sriwijaya University

Siti Nurmaini
Universitas Sriwijaya

Ganesha Ogi
Universitas Sriwijaya

Content Top

Fuzzy Logic-Ant Colony Optimization for Explorer-Follower Robot with Global Optimal Path Planning

Bambang Tutuko ,
Siti Nurmaini ,
Ganesha Ogi

Vol 7 No 1 (2018)

DOI 10.18495/comengapp.v7i1.241

Submitted: Nov 21, 2017

Published: Feb 10, 2018

Abstract

Path planning is an essential task for the mobile robot navigation. However, such a task is difficult to solve, due to the optimal path needs to be rerouted in real-time when a new obstacle appears. It produces a sub-optimal path and the robot can be trapped in local minima. To overcome the problem the Ant Colony Optimization (ACO) is combined with Fuzzy Logic Approach to make a globally optimal path. The Fuzzy-ACO algorithm is selected because the fuzzy logic has good performance in imprecision and uncertain environment and the ACO produce simple optimization with an ability to find the globally optimal path. Moreover, many optimization algorithms addressed only at the simulation level. In this research, the real experiment is conducted with the low-cost Explorer-Follower robot. The results show that the proposed algorithm, enables them to successfully identify the shortest path without collision and stack in â€œlocal minimaâ€.