CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Optimizing Robot Path Planning in 2D Static Environments using GA, PSO and ACO Search Algorithms

by Santosh Shrestha, Pranith Varma Appani, Praveen Reddy Kota, Alaa Sheta
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 7
Year of Publication: 2024
Authors: Santosh Shrestha, Pranith Varma Appani, Praveen Reddy Kota, Alaa Sheta
10.5120/ijca2024923402

Santosh Shrestha, Pranith Varma Appani, Praveen Reddy Kota, Alaa Sheta . Optimizing Robot Path Planning in 2D Static Environments using GA, PSO and ACO Search Algorithms. International Journal of Computer Applications. 186, 7 ( Feb 2024), 1-10. DOI=10.5120/ijca2024923402

@article{ 10.5120/ijca2024923402,
author = { Santosh Shrestha, Pranith Varma Appani, Praveen Reddy Kota, Alaa Sheta },
title = { Optimizing Robot Path Planning in 2D Static Environments using GA, PSO and ACO Search Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2024 },
volume = { 186 },
number = { 7 },
month = { Feb },
year = { 2024 },
issn = { 0975-8887 },
pages = { 1-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number7/optimizing-robot-path-planning-in-2d-static-environments-using-ga-pso-and-aco-search-algorithms/ },
doi = { 10.5120/ijca2024923402 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-22T22:17:52+05:30
%A Santosh Shrestha
%A Pranith Varma Appani
%A Praveen Reddy Kota
%A Alaa Sheta
%T Optimizing Robot Path Planning in 2D Static Environments using GA, PSO and ACO Search Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 7
%P 1-10
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Meta-heuristic search algorithms have shown great success in robot motion planning by designing collision-free paths in static and dynamic environments. Meta-heuristic search algorithms with suitable representation (i.e., encoding) can find the optimal path from a start to endpoint effectiveness via waypoints. The waypoints are random points generated in the search environment. In this research, we investigate the use of several meta-heuristic algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimizations (PSO), and Ant Colony Optimizations (ACO), to solve path planning problems in a two-dimensional static environment. The proposed path planning-based search enjoys a free robot passage in all possible space directions to operate in complex search spaces. Performance benchmarking is also carried out through simulation in various scenarios to determine and analyze the performance of each algorithm.

References
  1. M. N. A. Wahab, S. Nefti-Meziani, and A. Atyabi, “A comparative review on mobile robot path planning: Classical or meta-heuristic methods?” Annual Reviews in Control, vol. 50, pp. 233–252, 2020. [Online]. Available: https://www. sciencedirect.com/science/article/pii/S1367578820300675
  2. IFR, “International federation of robotics,” http://www.ifr. org/, 2020.
  3. M. Bartoš, V. Bulej, M. Bohušík, J. Stanček, V. Ivanov, and P. Macek, “An overview of robot applications in automotive industry,” Transportation Research Procedia, vol. 55, pp. 837–844, 2021, 14th International scientific conference on sustainable, modern and safe transport. [Online]. Available: https://www.sciencedirect.com/science/ article/pii/S2352146521004543
  4. A. Loganathan and N. S. Ahmad, “A systematic review on recent advances in autonomous mobile robot navigation,” Engineering Science and Technology, an International Journal, vol. 40, p. 101343, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S2215098623000204
  5. T.-K. Dao, T.-S. Pan, and J.-S. Pan, “A multi-objective optimal mobile robot path planning based on whale optimization algorithm,” in 2016 IEEE 13th international conference on signal processing (ICSP). IEEE, 2016, pp. 337–342.
  6. Z. Yan, J. Zhang, Z. Yang, and J. Tang, “Two-dimensional optimal path planning for autonomous underwater vehicle using a whale optimization algorithm,” Concurrency and Computation: Practice and Experience, vol. 33, no. 9, p. e6140, 2021.
  7. M. Abed, O. Farouq, and Q. Al-Doori, “A review on path planning algorithms for mobile robots,” Engineering and Technology Journal, vol. 39, pp. 804–820, 05 2021.
  8. J. Hopkins, F. Joy, A. Sheta, H. Turabieh, and D. Kar, “Path planning for indoor uav using A* and late acceptance hill climbing algorithms utilizing probabilistic roadmap,” International Journal of Engineering & Technology, vol. 9, no. 4, null. [Online]. Available: https://par.nsf.gov/biblio/10222262
  9. Z. Fu, J. Yu, G. Xie, Y. Chen, and Y. Mao, “A heuristic evolutionary algorithm of UAV path planning,” Wireless Communications and Mobile Computing, vol. 2018, pp. 1–11, 09 2018.
  10. A. Sheta, M. Braik, D. R. Maddi, A. Mahdy, S. Aljahdali, and H. Turabieh, “Optimization of PID controller to stabilize quadcopter movements using meta-heuristic search algorithms,” Applied Sciences, vol. 11, no. 14, 2021. [Online]. Available: https://www.mdpi.com/2076-3417/11/14/6492
  11. M. Brand, M. Masuda, N.Wehner, and X.-H. Yu, “Ant colony optimization algorithm for robot path planning,” vol. 3, pp. V3–436–V3–440, 2010.
  12. G. Bilbeisi, N. Al-Madi, and F. Awad, “Pso-ag: A multi-robot path planning and obstacle avoidance algorithm,” in 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), 2015, pp. 1–6.
  13. I. Altaharwa, A. Sheta, and M. Alweshah, “A mobile robot path planning using genetic algorithm in static environment,” Journal of Computer Science, vol. 4, 01 2008.
  14. “A comparative review on mobile robot path planning: Classical or meta-heuristic methods?” Annual Reviews in Control, vol. 50, pp. 233–252, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S1367578820300675
  15. Y. Gigras, N. Jora, and A. Dhull, “Comparison between different meta-heuristic algorithms for path planning in robotics,” International Journal of Computer Applications, vol. 142, pp. 6–10, 2016.
  16. C. Lamini, S. Benhlima, and A. Elbekri, “Genetic algorithm based approach for autonomous mobile robot path planning,” Procedia Computer Science, vol. 127, pp. 180–189, 2018, proceedings Of The First International Conference On Intelligent Computing In Data Sciences, ICDS 2017. [Online]. Available: https://www.sciencedirect.com/science/ article/pii/S187705091830125X
  17. M. Zubair and M. A. Choudhry, “Land mine detecting robot capable of path planning,” in 2010 Second World Congress on Software Engineering, vol. 1, 2010, pp. 34–37.
  18. R. Sawant, C. Singh, A. Shaikh, A. Aggarwal, P. Shahane, and H. R, “Mine detection using a swarm of robots,” in 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), 2022, pp. 1–9.
  19. B. A. Swett, E. N. Hahn, and A. J. Llorens, Designing Robots for the Battlefield: State of the Art. Cham: Springer International Publishing, 2021, pp. 131–146.
  20. G. Song, S. Thomas, K. Dill, J. M. Scholtz, and N. Amato, “A path planning-based study of protein folding with a case study of hairpin formation in protein g and l,” Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, pp. 240–51, 02 2003.
  21. A. K. Guruji, H. Agarwal, and D. Parsediya, “Time-efficient a* algorithm for robot path planning,” Procedia Technology, vol. 23, pp. 144–149, 2016, 3rd International Conference on Innovations in Automation and Mechatronics Engineering 2016, ICIAME 2016 05-06 February, 2016. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S2212017316300111
  22. D. R. Maddi, A. Sheta, A. Mahdy, and H. Turabieh, “Multiple waypoint mobile robot path planning using neighborhood search genetic algorithms,” ser. AIRC ’19. New York, NY, USA: Association for Computing Machinery, 2020, p. 14–22. [Online]. Available: https: //doi.org/10.1145/3388218.3388225
  23. M. M. Costa and M. F. Silva, “A survey on path planning algorithms for mobile robots,” in 2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), 2019, pp. 1–7.
  24. F. Syed Abdullah, S. Iyal, M. Makhtar, and A. A. Jamal, “Robotic indoor path planning using dijkstra’s algorithm with multi-layer dictionaries,” 12 2015.
  25. A. Sheta, A. Ali, A. Baareh, and S. Aljahdali, “Meta-heuristic search algorithms for solving the economic load dispatch problem,” in 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC), 2022, pp. 87–92.
  26. A. Sanyal, M. Nayab Zafar, J. C. Mohanta, and M. Faiyaz Ahmed, “Path planning approaches for mobile robot navigation in various environments: A review,” in Advances in Interdisciplinary Engineering, N. Kumar, S. Tibor, R. Sindhwani, J. Lee, and P. Srivastava, Eds. Singapore: Springer Singapore, 2021, pp. 555–572.
  27. K.-L. Du and M. N. S. Swamy, Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature, 1st ed. Birkhäuser Basel, 2016.
  28. M. Abdel-Basset, L. Abdel-Fatah, and A. K. Sangaiah, “Chapter 10 - metaheuristic algorithms: A comprehensive review,” in Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, ser. Intelligent Data-Centric Systems, A. K. Sangaiah, M. Sheng, and Z. Zhang, Eds. Academic Press, 2018, pp. 185–231. [Online]. Available: https://www.sciencedirect.com/science/ article/pii/B9780128133149000104
  29. K. Rajwar, K. Deep, and S. Das, “An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges,” Artificial Intelligence Review, vol. 56, pp. 13 187– 13 257, 2023. [Online]. Available: https://doi.org/10.1007/ s10462-023-10470-y
  30. J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press, 1975, second edition, 1992.
  31. J. Tu and S. Yang, “Genetic algorithm based path planning for a mobile robot,” in 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422), vol. 1, 2003, pp. 1221–1226 vol.1.
  32. G. Nagib and W. Gharieb, “Path planning for a mobile robot using genetic algorithms,” 10 2004, pp. 185– 189.
  33. W. Parvez and S. Dhar, “Path planning optimization using genetic algorithm,” International Journal of Computational Engineering Research, vol. 3, no. 4, pp. 23–28, 2013.
  34. Y. K. Ever, “Using simplified swarm optimization on path planning for intelligent mobile robot,” Procedia Computer Science, vol. 120, pp. 83–90, 2017, 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception, ICSCCW 2017, 22-23 August 2017, Budapest, Hungary. [Online]. Available: https://www.sciencedirect.com/science/ article/pii/S1877050917324237
  35. 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 Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826), vol. 4, 2004, pp. 2473–2478 vol.4.
  36. S. Chakraborty, “Ant colony system: A new concept to robot path planning,” International Journal of Hybrid Information Technology, vol. 6, pp. 11–30, 11 2013.
  37. H. Miao and Y.-C. Tian, “Dynamic robot path planning using an enhanced simulated annealing approach,” Applied Mathematics and Computation, vol. 222, pp. 420–437, 2013. [Online]. Available: https://www.sciencedirect.com/science/ article/pii/S0096300313007728
Index Terms

Computer Science
Information Sciences

Keywords

Path Planning Metaheuristic Search Algorithms Genetic Algorithms Particle Swarm Optimizations and Ant Colony Optimizations.

Powered by PhDFocusTM