Abstract: Addressing issues such as low planning efficiency,long search time,and complex paths in improving intelligent optimization algorithms,the egret swarm optimization algorithm was first applied to mobile robot path planning,and a new method for mobile robot path planning based on the improved egret swarm optimization algorithm was proposed. In the exploration stage,the algorithm utilized adversarial learning for population initialization to reduce the cost of path search;the sine cosine algorithm and greedy strategy were used to improve the updating of individual positions of egrets,in order to balance the local development and global search capabilities of the algorithm;coordinate fine-tuning strategy was used to obtain a safe and reliable planning path. In the optimization stage,the vertical distance limit method and segmented Bessel curve were used to optimize the path,in order to obtain the final motion path of the mobile robot. The simulation results show that this algorithm significantly improves the efficiency of path planning compared to the comparative algorithm,with shorter overall time consumption and better paths. It can reduce the number of path turns and thereby improve the overall work efficiency of mobile robots.

Key words: path planning, path optimization, egret swarm optimization algorithm, coordinate fine-tuning strategy, sine cosine algorithm