Research on automated guided vehicle path planning for unmanned factories based on improved artificial potential field-quick rapidly-exploring random tree* algorithm

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Abstract
In order to solve the problem of poor quality of paths generated by the traditional Q-RRT* algorithm and blind random search, an improved APF-QRRT* algorithm is proposed in this paper. The improved APF-QRRT* algorithm obtains a set of discrete critical path points connecting the start point and the end point by the Q-RRT* algorithm, and then fine-tunes the paths by using the local optimization capability of the APF to improve the smoothness and safety of the paths. The traditional Q-RRT* algorithm is improved, and the fast alternating expansion of two random trees is realized by introducing a bidirectional search strategy of two random trees and adopting node greedy expansion, where the nearest node of the tree is used as the reference for the expansion of this tree during the iterative process of path node generation. The experimental results show that the improved APF-QRRT* algorithm reduces the path planning time by 20.3%, the path length by 1.8%, the number of path nodes by 33.3%, and the number of sampling points by 23.6% compared with the standard APF-QRRT* algorithm in a complex environment. In this paper, a system test platform is constructed and utilized to carry out multi-AGV path planning experiments in real environments, and the experimental results show that the proposed hybrid path planning algorithm has good path planning effects.

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