{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:42:19Z","timestamp":1760060539218,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:00:00Z","timestamp":1756771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Undergraduate Training Program on Innovation and Entrepreneurship","award":["202510345033","62272418","62102058","LGG18E050011","ADIC2023ZD001"],"award-info":[{"award-number":["202510345033","62272418","62102058","LGG18E050011","ADIC2023ZD001"]}]},{"name":"National Natural Science Foundation of China","award":["202510345033","62272418","62102058","LGG18E050011","ADIC2023ZD001"],"award-info":[{"award-number":["202510345033","62272418","62102058","LGG18E050011","ADIC2023ZD001"]}]},{"DOI":"10.13039\/501100017577","name":"Basic public welfare research program of Zhejiang Province","doi-asserted-by":"publisher","award":["202510345033","62272418","62102058","LGG18E050011","ADIC2023ZD001"],"award-info":[{"award-number":["202510345033","62272418","62102058","LGG18E050011","ADIC2023ZD001"]}],"id":[{"id":"10.13039\/501100017577","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Open Project of Key Laboratory for Advanced Design and Intelligent Computing of the Ministry of Education","award":["202510345033","62272418","62102058","LGG18E050011","ADIC2023ZD001"],"award-info":[{"award-number":["202510345033","62272418","62102058","LGG18E050011","ADIC2023ZD001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Robots play a crucial role in experimental smart cities and are ubiquitous in daily life, especially in complex environments where multiple robots are often needed to solve problems collaboratively. Researchers have found that the swarm intelligence optimization algorithm has a better performance in planning robot paths, but the traditional swarm intelligence algorithm cannot be targeted to solve the robot path planning problem in difficult problem. Therefore, this paper aims to introduce a fuzzy controller, mutation factor, exponential noise, and other strategies on the basis of particle swarm optimization to solve this problem. By judging the moving speed of different particles at different periods of the algorithm, the individual learning factor and social learning factor of the particles are obtained by fuzzy controller, and using the leader particle and random particle, designing a new dynamic balance of mutation factor, with the iterative process of the adaptation value of continuous non-updating counter and continuous updating counter to control the proportion of the elite individuals and random individuals. Finally, using exponential noise to update the matrix of the population every 50 iterations is a way to balance the local search ability and global exploration ability of the algorithm. In order to test the proposed algorithm, the main method of this paper is simulated on simple scenarios, complex scenarios, and random maps consisting of different numbers of static obstacles and dynamic obstacles, and the algorithm proposed in this paper is compared with eight other algorithms. The average path deviation error of the planned paths is smaller; the average distance of untraveled target is shorter; the number of steps of the robot movements is smaller, and the path is shorter, which is superior to the other eight algorithms. This superiority in solving multi-robot cooperative path planning has good practicality in many fields such as logistics and distribution, industrial automation operation, and so on.<\/jats:p>","DOI":"10.3390\/bdcc9090229","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T12:04:28Z","timestamp":1756814668000},"page":"229","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improved Particle Swarm Optimization Based on Fuzzy Controller Fusion of Multiple Strategies for Multi-Robot Path Planning"],"prefix":"10.3390","volume":"9","author":[{"given":"Jialing","family":"Hu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China"}]},{"given":"Yanqi","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China"}]},{"given":"Siwei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China"}]},{"given":"Changjun","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.isatra.2024.05.006","article-title":"A novel global path planning method for robot based on dual-source light continuous reflection","volume":"150","author":"Ye","year":"2024","journal-title":"ISA Trans."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102043","DOI":"10.1016\/j.rineng.2024.102043","article-title":"Optimizing path planning in mobile robot systems using motion capture technology","volume":"22","author":"Szabolcsi","year":"2024","journal-title":"Results Eng."},{"key":"ref_3","first-page":"103907","article-title":"A passage time\u2013cost optimal A* algorithm for cross-country path planning","volume":"130","author":"Liu","year":"2024","journal-title":"Int. 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