Application of Improved Sparrow Search Algorithm to Path Planning of Mobile Robots.

• Published in: Biomimetics
• Main Authors: Xu, Yong, Sang, Bicong, Zhang, Yi
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI 2024
• Subjects: Lévy flight strategy; adaptive T-distribution variation strategy; circle chaotic mapping; integration of northern goshawk exploration phase location strategy; path planning; sparrow search algorithm; Northern Goshawk
• Online Access: https://doi.org/10.3390/biomimetics9060351; https://pubmed.ncbi.nlm.nih.gov/38921231; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11201462/

Path planning is an important research direction in the field of robotics; however, with the advancement of modern science and technology, the study of efficient, stable, and safe path-planning technology has become a realistic need in the field of robotics research. This paper introduces an improved sparrow search algorithm (ISSA) with a fusion strategy to further improve the ability to solve challenging tasks. First, the sparrow population is initialized using circle chaotic mapping to enhance diversity. Second, the location update formula of the northern goshawk is used in the exploration phase to replace the sparrow search algorithm's location update formula in the security situation. This improves the discoverer model's search breadth in the solution space and optimizes the problem-solving efficiency. Third, the algorithm adopts the Lévy flight strategy to improve the global optimization ability, so that the sparrow jumps out of the local optimum in the later stage of iteration. Finally, the adaptive T-distribution mutation strategy enhances the local exploration ability in late iterations, thus improving the sparrow search algorithm's convergence speed. This was applied to the CEC2021 function set and compared with other standard intelligent optimization algorithms to test its performance. In addition, the ISSA was implemented in the path-planning problem of mobile robots. The comparative study shows that the proposed algorithm is superior to the SSA in terms of path length, running time, path optimality, and stability. The results show that the proposed method is more effective, robust, and feasible in mobile robot path planning.