A* algorithm based on adaptive expansion convolution for unmanned aerial vehicle path planning

Abstract

Aiming at the shortcomings of traditional A* algorithm in 3D global path planning such as inefficiency and large computation, an A* optimization algorithm based on adaptive expansion convolution is proposed to realize UAV path planning. First, based on the idea of expansion convolution, the traditional A* algorithm is optimized to improve the search efficiency by improving the search step length and reducing the number of nodes needed to select the extended nodes in path planning; adding a weight factor to the cost function to select the appropriate weight of the cost function by keeping the principle of optimal path length while accelerating the planning speed to improve the planning speed of the algorithm; finally, using path pruning to further optimize the paths and reduce the problems of path redundancy. The simulation analysis results show that compared with the traditional A* algorithm, the improved algorithm in this paper reduces the number of extended nodes and shortens the planning time.