{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T05:36:03Z","timestamp":1763444163676,"version":"3.45.0"},"reference-count":33,"publisher":"Emerald","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,18]]},"abstract":"\n Purpose<\/jats:title>\n In mobile robot path planning, algorithms such as PSO and GA are widely applied but have issues such as premature convergence and insufficient path smoothness. Although ant colony optimization (ACO) has advantages in path diversity and global search capability, it faces limitations including poor initial guidance, slow convergence, time-consuming computation and excessive redundant turning points. This paper proposes an enhanced ACO integrating multiple improvement strategies to accelerate convergence, improve search efficiency, smooth trajectories and enhance the overall execution efficiency of robots.<\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n The method first uses BFS to pre-search a feasible path, which is smoothed and used to enhance pheromone concentration, improving the ants' initial search direction. A Sigmoid dynamic heuristic factor accelerates convergence, while a dynamic pheromone evaporation rate balances global exploration and local exploitation. The pheromone update equation has been improved to prevent the overuse of frequently selected edges, thereby avoiding premature convergence to local optima. Edge usage rate information further balances exploration and exploitation. Finally, Laplacian smoothing is applied to the path to remove discrete points and sharp turns, resulting in a natural and coherent trajectory.<\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n Simulations show that the improved ACO outperforms four existing algorithms in convergence speed, number of turning points and path smoothness, confirming its effectiveness in finding optimal and practical trajectories.<\/jats:p>\n <\/jats:sec>\n \n Practical implications<\/jats:title>\n This method holds broad future promise in the field of mobile robotics, enabling intelligent systems to achieve more efficient and safer autonomous navigation across diverse scenarios. By significantly enhancing task execution speed and resource utilization, it lays a solid foundation for the widespread adoption and sustained development of mobile robotics technology.<\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n This paper introduces a novel integration of BFS-based pre-search with pheromone enhancement, a Sigmoid dynamic heuristic factor, dynamic pheromone evaporation and improved pheromone updating based on edge usage rates, collectively addressing traditional ACO's weaknesses. The application of Laplacian smoothing further refines path quality. These contributions significantly improve converge.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/ijicc-05-2025-0307","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T05:37:02Z","timestamp":1761197822000},"page":"759-790","source":"Crossref","is-referenced-by-count":0,"title":["Application of an improved ACO integrating BFS and Laplacian smoothing strategy in mobile robot path planning"],"prefix":"10.1108","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5824-6846","authenticated-orcid":true,"given":"Shuai","family":"Wu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Dongguan City University , ,","place":["Dongguan, China"]},{"name":"Faculty of Applied Sciences, Macao Polytechnic University , ,","place":["Macao SAR, 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