Deep Heuristic-Improved 3D A* Algorithm Based on Dynamic Field-of-View

In order to solve the problem of low search efficiency of A^\ast algorithm in mountainous terrain scenarios due to inaccurate heuristic function modeling, a deep heuristic-improved 3D A^\ast algorithm based on dynamic field-of-view is proposed. In the improved A^\ast algorithm, a deep heuristic network is used to construct heuristic function model, and further the performance of the model is optimized in terms of generalization ability and modeling accuracy. In order to improve the generalization ability, a local dynamic field-of-view model is designed, which enhances the ability of deep heuristic network to capture the key terrain information, and then makes it adapt to various terrain scenarios. In order to improve the modeling accuracy, a loss function model based on the distance weight factor is designed to reduce the cost estimation deviation of the deep heuristic network for the long-distance paths in 3D scenarios. Simulation experiments show that compared with the existing deep learning-based improved A^\ast algorithm, the proposed algorithm can improve the prediction accuracy of path cost by 45.2% on average, the average search efficiency by 12.8%, and the average path quality by 1.2% in 3D scenarios. Compared with the existing improved A^\ast algorithm based on empirical modeling, the search efficiency is also significantly improved.