Paper Info
Title
Path Planning of UAVs Under Dynamic Environment based on a Hierarchical Recursive Multiagent Genetic Algorithm
Abstract
Path planning is a key technology to realize the automatic navigation of unmanned aerial vehicles (UAV), which has great significance both in theory and practical application. Evolutionary algorithms (EAs) are a type of nature-inspired computational methodologies for addressing complex real-world problems that cannot be solved well by mathematical or traditional modeling. However, the huge calculation of each iteration in the EAs greatly reduces the efficiency of the algorithm. In this paper, to accelerate the search speed of EAs and consider the dynamics of the environment, we propose a hierarchical recursive multi-agent genetic algorithm that can perform path planning in real time, termed as HR-MAGA. We used a strategy of layer-by-layer optimization on 3D maps with different resolution in the optimization process. The local search ability of the algorithm is improved by competition and self-learning process. In addition, the hierarchical recursive optimization process can greatly reduce the amount of computation and effectively deal with the dynamic characteristics of the environment. The experimental results show that HR-MAGA not only has strong global optimization ability, but more importantly, is able to generate collision free paths in real time after considering the physical limitations of the UAV and the dynamics of the environment.
Year
DOI
Venue
2020
10.1109/CEC48606.2020.9185513
2020 IEEE Congress on Evolutionary Computation (CEC)
Keywords
DocType
ISBN
Path Planning of UAVs,Multiagent Genetic Algorithm,Hierarchical Recursive Optimization,Dynamic Environment
Conference
978-1-7281-6930-9
Citations 
PageRank 
References 
0
0.34
12
Authors
3
Name
Order
Citations
PageRank
Qiansheng Yang100.34
Jing Liu21043115.54
Liqiang Li300.34