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Which is Better for Mobile Robot Trajectory Optimization: PSO or GA?

  • Safa ZiadiEmail author
  • Mohamed Njah
  • Sana Charfi
Chapter
  • 12 Downloads
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 270)

Abstract

In mobile robot research domain and especially in its path planning sub-domain, the optimization tools the most used are PSO (Particle Swarm Optimization) and GA (Genetic Algorithms). In fact, mobile robot path planning is a multi-objective optimization problem where at least these objectives are considered: the shortest and the safest path to the goal. Sometimes other objectives are added like travel duration. The subject of this paper is a comparison investigation concerning the efficiency of PSO and GA in mobile robot path planning optimization. We start with a comparison of evolutionary performances of PSO and GA. Then, we apply PSO and GA in the optimization of parameters of DVSF\(^{2}\) (Dynamic Variable Speed Force Field) path planning approach. Different environments of different types, statics and dynamics are considered in these simulations to decide which is better for mobile robot path planning optimization: PSO or GA.

Keywords

Genetic Algorithms Particle Swarm Optimization Mobile robot Path planning 

References

  1. 1.
    Cong, Y.Z., Ponnambalam, S.G.: Mobile robot path planning using ant colony optimization. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009. IEEE (2009)Google Scholar
  2. 2.
    Zhang, H.-y., Lin, W.-m., Chen, A.-x.: Path planning for the mobile robot: a review. Symmetry. 10, 450–467 (2018)Google Scholar
  3. 3.
    Zafar, Mohd.N., Mohanta, J.C.: Methodology for path planning and optimization of mobile robots: a review. Procedia Comput. Sci. 133, 141–152 (2018)Google Scholar
  4. 4.
    Zhang, X., Zhao, Y., Deng, N., Guo, K.: Dynamic path planning algorithm for a mobile robot based on visible space and an improved genetic algorithm. Int. J. Adv. Robot. Syst. 13, 91–108 (2016)Google Scholar
  5. 5.
    Nazarahari, M., Khanmirza, E., Doostie, S.: Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm. Expert Syst. Appl. 115, 106–120 (2019)Google Scholar
  6. 6.
    Cholodowicz, E., Figurowski, D.: Mobile robot path planning with obstacle avoidance using particle swarm optimization. Pomiary Automatyka Robotyka 21 (2017)Google Scholar
  7. 7.
    Adamu, P.I., Jegede, J.T., Okagbue, H.I., Oguntunde, P.E.: Shortest path planning algorithm-a Particle Swarm Optimization (PSO) approach. In: Proceedings of the World Congress on Engineering, vol. 1 (2018)Google Scholar
  8. 8.
    Samadi, M., Othman, M.F.: Global path planning for autonomous mobile robot using genetic algorithm. In: 2013 International Conference on Signal-Image Technology and Internet-Based Systems (SITIS). IEEE (2013)Google Scholar
  9. 9.
    Rath, M.K., Deepak, B.B.V.L.: PSO based system architecture for path planning of mobile robot in dynamic environment. In: 2015 Global Conference on Communication Technologies (GCCT). IEEE (2015)Google Scholar
  10. 10.
    Souza Lima, C.A., Lapa, C.M.F., do N.A., Pereira, C.M., da Cunha, J.J., Alvim, A.C.M.: Comparison of computational performance of GA and PSO optimization techniques when designing similar systems-Typical PWR core case. Ann. Nucl. Energy 38, 1339–1346 (2011)Google Scholar
  11. 11.
    Khoshahval, F., Minuchehr, H., Zolfaghari, A.: Performance evaluation of PSO and GA in PWR core loading pattern optimization. Nucl. Eng. Des. 241, 799–808 (2011)CrossRefGoogle Scholar
  12. 12.
    Hassan, R., Cohanim, B., de Weck, O.: A comparison of particle swarm optimization and the genetic algorithm. In: American Institute of Aeronautics and Astronautics (2004)Google Scholar
  13. 13.
    Kecskes, I., Szekacs, L., Fodor, J.C., Odry, P.: PSO and GA optimization methods comparison on simulation model of a real hexapod robot. In: IEEE 9th International Conference on Computational Cybernetics (ICCC), pp. 125–130 (2013)Google Scholar
  14. 14.
    Ben Alaia, E., Harbaoui, I., Borne, P., Bouchriha, H.: A comparative study of the PSO and GA for the m-MDPDPTW. Int. J. Comput. Commun. Control 13, 8–23 (2018)Google Scholar
  15. 15.
    Ziadi, S., Njah, M., Chtourou, M.: PSO-DVSF\(2\): a new method for the path planning of mobile robots. In: 16th International Conference on Sciences and Techniques of Automatic control and computer engineering - STA’2015, Monastir, Tunisia (2015). Accessed 21–23 Dec 2015Google Scholar
  16. 16.
    Ziadi, S., Njah, M., Chtourou, M.: A*PSO-DVSF2 : an optimized mobile robot path planning approach. Int. J. Electr. Electron. Data Commun. 6, 63–68 (2018)Google Scholar
  17. 17.
    Kaewkamnerdpong, B., Bentley, P.: Perceptive particle swarm optimization: an investigation. In: Proceedings of the 2005 IEEE Swarm Intelligence Symposium, SIS 2005, pp. 169–176 (2005)Google Scholar
  18. 18.
    Yuhui, S., Eberhart, R.: Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, 1999, CEC 99, vol. 3. IEEE (1999)Google Scholar
  19. 19.
    Katiyar, S.: A comparative study of genetic algorithm and the particle swarm optimization. AKGEC Int. J. Technol. 2(2), 21–24 (2011)Google Scholar
  20. 20.
    Noura Teixeira, O.: Computaçáo evolucionà: dos aspectos filosóficos à implementaçáo dos algoritmos genèticos na soluçáo do problema do caixeiro viajante simétrico. Trabalho de Conclusãâo de Curso (Bacharelado em Ciência da Computaçáo) - Universidade Federal do Pará, Belém (2003)Google Scholar
  21. 21.
    Aliab, A.F., Tawhid, M.A.: A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems. Ain Shams Eng. J. (2016)Google Scholar
  22. 22.
    Prieto, L., Komínkova-Oplatková, Z., Frías, R., Hernández, J.: A time performance comparison of particle swarm optimization in mobile devices. In: MATEC Web of Conferences, vol. 76. EDP Sciences (2016)Google Scholar
  23. 23.
    Miro, J.V., Taha, T., Wang, D., Dissanayake, G., Liu, D.: An efficient strategy for robot navigation in cluttered environments in the presence of dynamic obstacles. In: The Eighth International Conference on Intelligent Technologies (2007)Google Scholar
  24. 24.
    Wang, D.: A generic force field method for robot real-time motion planning and coordination. A thesis submitted in fulfilment of the degree of doctor of philosophy (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Control and Energy Management Laboratory (CEM-Lab), National Engineering School of SfaxUniversity of SfaxSfaxTunisia
  2. 2.Digital Research Center of Sfax, Technopole of SfaxSfaxTunisia

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