{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T21:49:21Z","timestamp":1775598561100,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Mobile robots are increasingly integral to diverse applications, with path-planning algorithms being essential for efficient and secure mobile robot navigation. Mobile robot path planning is defined as the design of the least time-consuming, shortest-distance, and most collision-free path from the starting point to the endpoint for the mobile robot\u2019s autonomous movement. This study investigates and assesses two widely used algorithms in artificial intelligence (AI)\u2014Improved Particle Swarm Optimization (IPSO) and Improved Genetic Algorithm (IGA)\u2014for path planning of mobile robot navigation problems. In this work Manhattan movements are proposed as a distance formula to modify both algorithms in the path planning of the mobile robot navigation problem. Unlike the traditional GA and PSO, which can use horizontal search, the proposed algorithm relies on vertical search, which gives us an advantage. The results demonstrate the effectiveness of these modified algorithms in barrier detection and obstacle avoidance. Six different experiments were run using both improved algorithms to show their ability to achieve their goal and avoid obstacles in various scenarios with different complexities. Across various scenarios, the tested AI algorithms performed effectively, regardless of the map scale and complexity. This paper proposes a complete comparison between the two improved algorithms in different scenarios. The results show that the algorithms\u2019 performance is influenced more by the density of walls and obstacles than by the size or complexity of the map.<\/jats:p>","DOI":"10.3390\/a18110719","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T14:37:52Z","timestamp":1763131072000},"page":"719","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Optimizing Navigation in Mobile Robots: Modified Particle Swarm Optimization and Genetic Algorithms for Effective Path Planning"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0571-9814","authenticated-orcid":false,"given":"Mohamed","family":"Amr","sequence":"first","affiliation":[{"name":"Department of Computers and Systems, Electronics Research Institute, Cairo 12622, Egypt"}]},{"given":"Ahmed","family":"Bahgat","sequence":"additional","affiliation":[{"name":"Department of Computers and Systems, Electronics Research Institute, Cairo 12622, Egypt"}]},{"given":"Hassan","family":"Rashad","sequence":"additional","affiliation":[{"name":"Power and Machines Department, Faculty of Engineering, Cairo University CUFE, Cairo 12622, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6740-7820","authenticated-orcid":false,"given":"Azza","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Power and Machines Department, Faculty of Engineering, Cairo University CUFE, Cairo 12622, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6145-4071","authenticated-orcid":false,"given":"Ayman","family":"Youssef","sequence":"additional","affiliation":[{"name":"Department of Computers and Systems, Electronics Research Institute, Cairo 12622, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Marques, B., Junqueira, G., Alves, J., and Pedrosa, E. (2024, January 11\u201315). Mobile Robots Meet Augmented Reality Technologies: Transforming Human-Robot Interaction in Industry 4.0 Scenarios. Proceedings of the Companion of the 2024 ACM\/IEEE International Conference on Human-Robot Interaction, Boulder, CO, USA.","DOI":"10.1145\/3610978.3640681"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pachayapan, K., Abdulsalam, I., Subramanian, S., Sudhakaran, S., and Suresh, Y. (2024). Gas detecting robot for mining operations. Proceedings of the AIP Conference Proceedings, AIP Publishing.","DOI":"10.1063\/5.0194169"},{"key":"ref_3","first-page":"110","article-title":"Omni wheel robot movement exploration using a control system for military surveillance with integrated sensor","volume":"12","author":"Kurniawan","year":"2025","journal-title":"TEKNOSAINS J. Sains Teknol. Inform."},{"key":"ref_4","unstructured":"Schwaiger, S., Muster, L., Novotny, G., Schebek, M., W\u00f6ber, W., Thalhammer, S., and B\u00f6hm, C. (2024). Semi-Autonomous Mobile Search and Rescue Robot for Radiation Disaster Scenarios. arXiv."},{"key":"ref_5","first-page":"382","article-title":"Bio-inspired mobile robot design and autonomous exploration strategy for underground special space","volume":"44","author":"Wang","year":"2024","journal-title":"Robot. Intell. Autom."},{"key":"ref_6","first-page":"151","article-title":"Pitch Angle Control of the Self-Balancing Cargo Platform in an Agricultural Mobile Robot Using a 3-RPS Parallel Mechanism","volume":"6","author":"Huang","year":"2024","journal-title":"Precis. Agric. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"100827","DOI":"10.1016\/j.entcom.2024.100827","article-title":"Entertainment robot simulation in interactive art process based on deep learning algorithms and gesture recognition","volume":"52","author":"Lyu","year":"2025","journal-title":"Entertain. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Abdallaoui, S., Aglzim, E.H., Chaibet, A., and Krib\u00e8che, A. (2022). Thorough review analysis of safe control of autonomous vehicles: Path planning and navigation techniques. Energies, 15.","DOI":"10.3390\/en15041358"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104285","DOI":"10.1016\/j.robot.2022.104285","article-title":"Power solutions for autonomous mobile robots: A survey","volume":"159","author":"Farooq","year":"2023","journal-title":"Robot. Auton. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.cogr.2023.05.003","article-title":"Optimization of energy consumption in industrial robots, a review","volume":"3","author":"Soori","year":"2023","journal-title":"Cogn. Robot."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rajchandar, K., Kothandaraman, D., Manoharan, G., and Kabanda, G. (2024). Robotics and its Navigation Techniques: The Present and Future Revelations. Handbook of Artificial Intelligence and Wearables, CRC Press.","DOI":"10.1201\/9781032686714-12"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.arcontrol.2020.10.001","article-title":"A comparative review on mobile robot path planning: Classical or meta-heuristic methods?","volume":"50","author":"Atyabi","year":"2020","journal-title":"Annu. Rev. Control"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5259","DOI":"10.1007\/s00170-023-11294-4","article-title":"Perspectives of managers and workers on the implementation of automated-guided vehicles (AGVs)\u2014A quantitative survey","volume":"126","author":"Kopp","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Holland, J.H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT Press.","DOI":"10.7551\/mitpress\/1090.001.0001"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.1109\/ICNN.1995.488968","article-title":"Particle swarm optimization","volume":"Volume 4","author":"Kennedy","year":"1995","journal-title":"Proceedings of the ICNN\u201995-International Conference on Neural Networks"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Suriyan, K., and Nagarajan, R. (2024). Particle swarm optimization in biomedical technologies: Innovations, challenges, and opportunities. Emerging Technologies for Health Literacy and Medical Practice, IGI Global.","DOI":"10.4018\/979-8-3693-1214-8.ch011"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"134050","DOI":"10.1016\/j.energy.2024.134050","article-title":"Reinforcement learning-based particle swarm optimization for wind farm layout problems","volume":"313","author":"Zhang","year":"2024","journal-title":"Energy"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yi, J., Yu, P., Huang, T., and Xu, Z. (2024, January 8\u201310). Optimization of Transformer heart disease prediction model based on particle swarm optimization algorithm. Proceedings of the 2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC), Qingdao, China.","DOI":"10.1109\/ICFTIC64248.2024.10913096"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1007\/s11831-021-09694-4","article-title":"Particle swarm optimization algorithm and its applications: A systematic review","volume":"29","author":"Gad","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mohamed, A., Bahgat, A., Rashad, H., and Ibrahim, A. (2022). Comparison of Path Planning between Improved Informed and Uninformed Algorithms for Mobile Robot. Int. J. Adv. Comput. Sci. Appl., 13.","DOI":"10.14569\/IJACSA.2022.0130629"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106960","DOI":"10.1016\/j.asoc.2020.106960","article-title":"An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve","volume":"100","author":"Song","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"123762","DOI":"10.1016\/j.eswa.2024.123762","article-title":"Improved genetic algorithm for mobile robot path planning in static environments","volume":"249","author":"Nazir","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.procs.2018.01.113","article-title":"Genetic algorithm based approach for autonomous mobile robot path planning","volume":"127","author":"Lamini","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"83","DOI":"10.31026\/j.eng.2019.06.07","article-title":"A Comparative study of various intelligent algorithms based path planning for Mobile Robots","volume":"25","author":"Jawad","year":"2019","journal-title":"J. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.5897\/IJPS11.1745","article-title":"Optimal path planning of mobile robots: A review","volume":"7","author":"Raja","year":"2012","journal-title":"Int. J. Phys. Sci."},{"key":"ref_26","first-page":"2538220","article-title":"A review on path planning and obstacle avoidance algorithms for autonomous mobile robots","volume":"2022","author":"Rafai","year":"2022","journal-title":"J. Robot."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2138","DOI":"10.1109\/ACCESS.2018.2886245","article-title":"Multi-robot path planning based on multi-objective particle swarm optimization","volume":"7","author":"Thabit","year":"2018","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sun, R., Tang, C., Zheng, J., Zhou, Y., and Yu, S. (2019, January 8\u201311). Multi-robot path planning for complete coverage with genetic algorithms. Proceedings of the International Conference on Intelligent Robotics and Applications, Shenyang, China.","DOI":"10.1007\/978-3-030-27541-9_29"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"120254","DOI":"10.1016\/j.eswa.2023.120254","article-title":"Path planning techniques for mobile robots: Review and prospect","volume":"227","author":"Liu","year":"2023","journal-title":"Expert Syst. Appl."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/11\/719\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T14:45:45Z","timestamp":1763131545000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/11\/719"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,14]]},"references-count":29,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["a18110719"],"URL":"https:\/\/doi.org\/10.3390\/a18110719","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,14]]}}}