Deep Heuristic-Improved 3D A*Algorithm Based on Dynamic Field-of-View
In order to solve the problem of low search efficiency of A*algorithm in mountainous terrain scenarios due to inaccurate heuristic function modeling,a deep heuristic-improved 3D A*algorithm based on dynamic field-of-view is proposed.In the improved A*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*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*algorithm based on empirical modeling,the search efficiency is also significantly improved.
3D path planningA*algorithmheuristic functiondynamic field-of-viewmountain mobile robot
万方数据
维普
