文章基本信息

标题：Constrained Path Planning for Unmanned Aerial Vehicle in 3D Terrain Using Modified Multi-Objective Particle Swarm Optimization
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作者：Shuang Xia ; Xiangyin Zhang
期刊名称：Actuators
电子版ISSN：2076-0825
出版年度：2021
卷号：10
期号：10
页码：255
DOI：10.3390/act10100255
语种：English
出版社：MDPI Publishing
摘要：This paper considered the constrained unmanned aerial vehicle (UAV) path planning problem as the multi-objective optimization problem, in which both costs and constraints are treated as the objective functions. A novel multi-objective particle swarm optimization algorithm based on the Gaussian distribution and the Q-Learning technique (GMOPSO-QL) is proposed and applied to determine the feasible and optimal path for UAV. In GMOPSO-QL, the Gaussian distribution based updating operator is adopted to generate new particles, and the exploration and exploitation modes are introduced to enhance population diversity and convergence speed, respectively. Moreover, the Q-Learning based mode selection logic is introduced to balance the global search with the local search in the evolution process. Simulation results indicate that our proposed GMOPSO-QL can deal with the constrained UAV path planning problem and is superior to existing optimization algorithms in terms of efficiency and robustness.