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Reseach Article

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

by Nizar Hadi Abbas, Farah Mahdi Ali
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 96 - Number 11
Year of Publication: 2014
Authors: Nizar Hadi Abbas, Farah Mahdi Ali
10.5120/16836-6681

Nizar Hadi Abbas, Farah Mahdi Ali . Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm. International Journal of Computer Applications. 96, 11 ( June 2014), 11-16. DOI=10.5120/16836-6681

@article{ 10.5120/16836-6681,
author = { Nizar Hadi Abbas, Farah Mahdi Ali },
title = { Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 96 },
number = { 11 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 11-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume96/number11/16836-6681/ },
doi = { 10.5120/16836-6681 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:21:27.547853+05:30
%A Nizar Hadi Abbas
%A Farah Mahdi Ali
%T Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 96
%N 11
%P 11-16
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
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
  1. P. A. M. Ehlert, "The use of Artificial Intelligence Robots,"Report on research project, Delft University of Technology, Netherlands, October 1999.
  2. C. A. Floudas, ?Deterministic Global Optimization: Theory, Methods and Applications," Nonconvex Optimization and Its Applications, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2000.
  3. 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.
  4. X. Yang, ? Nature-Inspired Metaheuristic Algorithms," 2nd Edition, by Luniver Press United Kingdom, 2010.
  5. H. Chen, Y. Zhu, and K. Hu, ? Adaptive Bacterial Foraging Optimization," Abstract and Applied Analysis, Hindawi Publishing Corporation, 2011.
  6. 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.
  7. M. Dorigo, G. D. Caro, and L. M. Gambardella, "Ant Algorithms for Discrete Optimization," Artificial Life, vol. 5, no. 2,pp. 137-172,1999.
  8. 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.
  9. 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.
  10. H. Miao, " Robot Path Planning in Dynamic Environments using Simulated Annealing Based Approach," Master thesis, Queensland University of Technology, Queensland, Australia, March 2009.
  11. ] 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
Index Terms

Computer Science
Information Sciences

Keywords

Autonomous robots Holonomic robot Path planning Optimization methods Artificial bee colony algorithm