Intelligent Scheduling Technology of Swarm Intelligence Algorithm for Drone Path Planning

作     者：Meng, Zhipeng Li, Dongze Zhang, Yong Yan, Haoquan 

作者机构：Acad Mil Sci Natl Innovat Inst Def Technol Beijing 100071 Peoples R China Nanjing Univ Aeronaut & Astronaut Res Inst Pilotless Aircraft Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Nanjing 210016 Peoples R China 

出 版 物：《DRONES》 (Drones)

年 卷 期：2024年第8卷第4期

页      面：120-120页

核心收录：

基　　金：2021ZD0140300 

主　　题：algorithm scheduling deep reinforcement learning path planning swarm intelligence algorithm 

摘      要：Different kinds of swarm intelligence algorithm obtain superior performances in solving complex optimization problems and have been widely used in path planning of drones. Due to their own characteristics, the optimization results may vary greatly in different dynamic environments. In this paper, a scheduling technology for swarm intelligence algorithms based on deep Q-learning is proposed to intelligently select algorithms to realize 3D path planning. It builds a unique path point database and two basic principles are proposed to guide model training. Path planning and network learning are separated by the proposed separation principle and the optimal selection principle ensures convergence of the model. Aiming at the problem of reward sparsity, the comprehensive cost of each path point in the whole track sequence is regarded as a dynamic reward. Through the investigation of dynamic environment conditions such as different distances and threats, the effectiveness of the proposed method is validated.