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Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 11
Year of Publication: 2014
Authors:
Nizar Hadi Abbas
Farah Mahdi Ali
10.5120/16836-6681

Nizar Hadi Abbas and Farah Mahdi Ali. Article: Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm. International Journal of Computer Applications 96(11):11-16, June 2014. Full text available. BibTeX

@article{key:article,
	author = {Nizar Hadi Abbas and Farah Mahdi Ali},
	title = {Article: Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {11},
	pages = {11-16},
	month = {June},
	note = {Full text available}
}

Abstract

This paper describes the problem of offline autonomous mobile robot path planning, which is consist of generating optimal paths or trajectories for an autonomous mobile robot from a starting point to a destination across a flat map of a terrain, represented by a 2-D workspace. An improved algorithm for solving the problem of path planning using Artificial Bee Colony algorithm is presented. This nature-inspired metaheuristic algorithm, which imitates the foraging behavior of bees around their hive, is used to find the optimal path from a starting point to a target point. The proposed algorithm is demonstrated by simulations in three different environments. A comparative study is evaluated between the developed algorithm, the original ABC and other two state-of-the-art algorithms. This study shows that the proposed method is effective and gets trajectories with satisfactory results.

References

  • P. A. M. Ehlert, "The use of Artificial Intelligence Robots,"Report on research project, Delft University of Technology, Netherlands, October 1999.
  • C. A. Floudas, ?Deterministic Global Optimization: Theory, Methods and Applications," Nonconvex Optimization and Its Applications, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2000.
  • J. C. Spall, ?Introduction to Stochastic Search and Optimization: Estimation, Simulation and Control," Wiley-Interscience Series in Discrete Mathematics and Optimization, Wiley-Interscience, Hoboken,NJ, USA, 2003.
  • X. Yang, ? Nature-Inspired Metaheuristic Algorithms," 2nd Edition, by Luniver Press United Kingdom, 2010.
  • H. Chen, Y. Zhu, and K. Hu, ? Adaptive Bacterial Foraging Optimization," Abstract and Applied Analysis, Hindawi Publishing Corporation, 2011.
  • R. C. Eberhart, and J. Kennedy, "A New Optimizer using Particle Swarm Theory," In Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, vol. 1, pp. 39-43. 1995.
  • M. Dorigo, G. D. Caro, and L. M. Gambardella, "Ant Algorithms for Discrete Optimization," Artificial Life, vol. 5, no. 2,pp. 137-172,1999.
  • D. Karaboga, "An Idea based on Honey Bee Swarm for Numerical Optimization, "Technical Report, Erciyes University, Engineering Faculty, Computer Engineering Department, pp. 1-10, 2005.
  • B. Wu and C. Qian, ? Differential Artificial Bee Colony Algorithm for Global Numerical Optimization," Journal of Computer, vol. 6, no. 5, pp. 841-848 May 2011.
  • H. Miao, " Robot Path Planning in Dynamic Environments using Simulated Annealing Based Approach," Master thesis, Queensland University of Technology, Queensland, Australia, March 2009.
  • ] A. L. Bolaji, A. T. Khader, M. A. Al-betar, and M. A. Awadallah, "Artificial Bee Colony Algorithm, Its Variants and Applications: A Survey," Journal of Theoretical and Applied Information Technology, vol. 47, no. 2, pp. 434-459, 2013.
  • M. H. Saffari and M. J. Mahjoob, "Bee Colony Algorithm for Real-Time Optimal Path Planning of Mobile Robots," Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control (ICSCCW), 2-4 Sept. 2009.
  • W. Xiang, S. Ma and M. An, ? An Improved Artificial Bee Colony Algorithm with Multiple Search Operators," Journal of Computational Information Systems, pp. 3129–3139, August 2013.
  • D. Karaboga and B. Akay, ? A modified Artificial Bee Colony Algorithm for Real-Parameter Optimization," Swarm Intelligence and Its Applications, Elsevier, vol. 192, pp. 120-142, June 2010.
  • P. Erdogmus and M. Toz, ?Heuristic Optimization Algorithms in Robotics," Serial and Parallel Robot Manipulators-Kinematics, Dynamics, Control and Optimization, InTech, pp. 311-338, March, 2012.
  • C. A. Sierakowski and L. S. Coelho, " Study of Two Swarm Intelligence Techniques for Path Planning of Mobile Robots,"16thIFAC World Congress, Prague, July 4-8, 2005.
  • J. -H. Lin and L. -R. Huang, "Chaotic Bee Swarm Optimization Algorithm for Path Planning of Mobile Robots," Proceedings of the 10th WSEAS International Conference on Evolutionary Computing, Prague, Czech Republic, 2009.
  • L. S. Coelho and C. A. Sierakowski, " Bacteria Colony Approaches with Variable Velocity Applied to Path Optimization of Mobile Robots," 18th International Congress of Mechanical Engineering, Ouro Preto, MG, Brazil, November 6-11, 2005.