Crane path planning method based on improved A* algorithm

Abstract

The invention discloses a crane planning method based on an improved A * algorithm, which comprises the following steps of: firstly, establishing a crane operation environment map by using a binocular camera, converting the environment map into a grid map, coding each grid, and determining the coordinate of each grid; secondly, an improved A * algorithm is used for path finding, and an initial path is obtained; and finally, carrying out smoothing processing on the obtained initial path by utilizing a floyd algorithm. According to the improved A * algorithm, weighting processing is carried out on an evaluation function of the A * algorithm, so that the moving length of the A * algorithm is more reasonable, smoothing processing is carried out on an initial path, and the obtained final path better conforms to the driving path of the crane.

Description

technical field [0001] The invention belongs to the technical field of industrial control, and in particular relates to a crane path planning method based on an improved A* algorithm. Background technique [0002] Realizing the intelligentization of cranes can not only solve the shortage of labor force, but also ensure the personal safety of practitioners in harsh working environments. Therefore, it is imminent to realize the intelligentization of cranes. For intelligent cranes, the main research directions include automatic positioning and environment modeling, path planning and other fields, and path planning is a key technology for cranes to realize intelligence, so the path planning of cranes is taken as the main research direction of the present invention. [0003] At present, the most widely used path finding method is the heuristic A* search algorithm, which is to heuristically find the end point of the target, and based on the minimum cost, find the most suitable and...

AI Technical Summary

Problems solved by technology

However, the traditional heuristic A* search algorithm will search for many useless nodes when facing obstacles. Scholars at home and abroad have also conducted a lot of research on this. Points are prioritized to avoid passing through the obstacle vertices, and there is a certain safety distance from the obstacle, but there are still many turning points and safety problems in the path; Xin Yu et al. searched for 8 discrete neighborhoods through the A* algorithm and extended it to infinite , increasing the search direction and improving the performance index of path smoothness, but the amount of calculation increases, resulting in a significant decrease in search efficiency

[0004] To sum up, although scholars have done a lot of research, they still search for useless nodes during the search process, resulting in increased search time and computing memory.

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