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Reseach Article

A Comparative Study: Modified Particle Swarm Optimization and Modified Genetic Algorithm for Global Mobile Robot Navigation

by Nadia Adnan Shiltagh, Lana Dalawr Jalal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 89 - Number 9
Year of Publication: 2014
Authors: Nadia Adnan Shiltagh, Lana Dalawr Jalal
10.5120/15533-4432

Nadia Adnan Shiltagh, Lana Dalawr Jalal . A Comparative Study: Modified Particle Swarm Optimization and Modified Genetic Algorithm for Global Mobile Robot Navigation. International Journal of Computer Applications. 89, 9 ( March 2014), 32-46. DOI=10.5120/15533-4432

@article{ 10.5120/15533-4432,
author = { Nadia Adnan Shiltagh, Lana Dalawr Jalal },
title = { A Comparative Study: Modified Particle Swarm Optimization and Modified Genetic Algorithm for Global Mobile Robot Navigation },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 9 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 32-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume89/number9/15533-4432/ },
doi = { 10.5120/15533-4432 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:08:49.914302+05:30
%A Nadia Adnan Shiltagh
%A Lana Dalawr Jalal
%T A Comparative Study: Modified Particle Swarm Optimization and Modified Genetic Algorithm for Global Mobile Robot Navigation
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 9
%P 32-46
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, Modified Genetic Algorithm (MGA*1) and Modified Particle Swarm Optimization (MPSO* ) are developed to increase the capability of the optimization algorithms for a global path planning which means that environment models have been known already. The proposed algorithms read the map of the environment which expressed by grid model and then creates an optimal or near optimal collision free path. The effectiveness of these optimization algorithms for mobile robot path planning is demonstrated by simulation studies. This paper investigates the application of efficient optimization algorithms, MGA* and MPSO* to the problem of mobile robot navigation. Despite the fact that Genetic Algorithm (GA) has rapid search and high search quality, infeasible paths and high computational cost problems are exist associated with this algorithm. To address these problems, the MGA* is presented. Adaptive population size without selection and mutation operators are used in the proposed algorithm. In this thesis, Distinguish Algorithm (DA) is used to check the paths, whether the path is feasible or not, in order to come out with all feasible paths in the population. Improvements presented in MPSO* are mainly trying to address the problem of premature convergence associated with the original PSO. In the MPSO* an error factor is modelled to ensure that the PSO converges. MPSO* try to address another problem which is the population may contain many infeasible paths. A modified procedure is carrying out in the MPSO* to solve the infeasible path problem. According to the simulation done using MATLAB version R2012 (m-file), both algorithms (MGA* and MPSO*) are tested in different environments and the results are compared. The results demonstrate that these two algorithms have a great potential to solve mobile robot path planning with satisfactory results in terms of minimizing distance and execution time. The simulation results illustrate that the path obtained by MGA* is the shortest path, however the execution time based on MPSO* is significantly smaller than the execution time of MGA*. Thus, the proposed MPSO* is computationally faster than the MGA* in finding optimal path.

References
  1. Patnaik, S. , Jain, L. C. , Tzafestas, S. G. , Resconi, G. , Konar, A. 2005 Innovations in Robot Mobility and Control, Springer, Netherlands.
  2. Kolski, S. 2007. Mobile Robots Perception and Navigation, Advanced Robotic Systems International and pro literatur Verlag, Germany.
  3. Han, K. M. , 2007. Collision free path planning algorithms for robot navigation problem, Master Thesis, University of Missouri-Columbia.
  4. Yun, S. C. , Ganapathy, V. , Chong, L. O. 2010. Improved genetic algorithms based optimum path planning for mobile robot, International Conference on Control, Automation, Robotics and Vision, ICARCV, pp. 1565-1570.
  5. Mohanty, P. K. , and Parhi D. R. 2013, Controlling the Motion of an Autonomous Mobile Robot Using Various Techniques: a Review, Journal of Advance Mechanical Engineering, Vol. 1: pp. 24-39.
  6. Dutta, S. , 2010. Obstacle Avoidance of Mobile Robot using PSO-based Neuro Fuzzy Technique, International Journal of Computer Science and Engineering, Vol. 2, Issue, (2): pp. 301-304.
  7. Tamilselvi, D. , shalinie, M. , Hariharasudan. 2011. Optimal Path Selection for Mobile Robot Navigation Using Genetic Algorithm", International Journal of Computer Science Issues, Vol. 8, Issue, (4): pp. 433-440.
  8. Ragavan, S. V. , Ponnambalam, S. G. , Sumero, C. 2011. Waypoint-based Path Planner for Mobile Robot Navigation Using PSO and GA-AIS", Recent Advances in Intelligent Computational Systems (RAICS), pp. 756-760
  9. Raja, P. , Pugazhenthi, S. 2012 . Optimal path planning of mobile robots: A review, International Journal of Physical Sciences, Vol. 7, Issue, (9): pp. 1314 – 1320
  10. Ahmadzadeh, S. , Ghanavati, M. 2012. Navigation of Mobile Robot Using the PSO Particle Swarm Optimization, Journal of Academic and Applied Studies (JAAS), Vol. 2, Issue, (1): pp. 32-38.
  11. Habeeb, Z. Q. , 2012. A Simulation for Optimal Path Planning for Mobile Robot Using Modified Genetic Algorithm, Master Thesis, University of Baghdad.
  12. Yu-qin, W. , Xiao-peng, Y. 2012. Research for the Robot Path Planning Control Strategy Based on the Immune Particle Swarm Optimization Algorithm, 2nd International Conference on Intelligent System Design and Engineering Application, pp. 724-727.
  13. Gigras, Y. , Gupta, K. 2012. Artificial Intelligence in Robot Path Planning", International Journal of Soft Computing and Engineering (IJSCE), Vol. 2, Issue, (2): pp. 471-474.
  14. Jatmiko, W. , Sekiyama K. , and Fukuda, T. 2006. Modified Particle Swarm Robotic for Odor Source Localization in Dynamic Environment, the International Journal of Intelligent Control and Systems: Special Issue on Swarm Robotic, Vol. 11, Issue, (3): pp. 176-184.
  15. Eberhart, R. , and Shi, Y. 1998. Comparison between Genetic Algorithms and Particle Swarm Optimization. In Proceedings of the Seventh Annual Conference on Evolutionary Programming, Springer-Verlag, pp. 611-619.
Index Terms

Computer Science
Information Sciences

Keywords

Modified Genetic Algorithm (MGA*) Modified Particle Swarm Optimization (MPSO*) path planning mobile robot navigation.