Automated guided vehicle (AGV) path optimization method based on improved rapidly-exploring random trees

In response to the issues of low computational efficiency, slow convergence speed, curvy paths, and the tendency to fall into local optima in rapidly-exploring random tree (RRT) algorithms for automated guided vehicle (AGV) path planning, this article proposes an improved RRT algorithm that combines adaptive step-size optimization with K-dimensional tree (KD-Tree) based fast nearest neighbor search. Firstly, an adaptive step-size optimization strategy is introduced to dynamically adjust the step size during node searches, improving both the planning quality and computational efficiency of the algorithm. Secondly, the KD-Tree nearest neighbor search method is employed to accelerate node searching and reduce the time cost of path planning. Finally, a cubic spline interpolation function is applied to smooth the optimal path, further enhancing the planning quality. Experimental results show that the improved RRT algorithm significantly outperforms traditional RRT, RRT*, and Informed-RRT* in terms of path length, planning time, and path smoothness. Specifically, the average path length is reduced by 164.33 m, and the average search time is shortened by 3.3 s, making it more suitable for AGV path planning in practical applications.