%0 Journal Article
%A HAN Tong
%A TANG Andi
%A XIE Lei
%A XU Dengwu
%T Path planning method of unmanned aerial vehicle based on chaos sparrow search algorithm
%D 2021
%R 10.11772/j.issn.1001-9081.2020091513
%J Journal of Computer Applications
%P 2128-2136
%V 41
%N 7
%X Focusing on the issues of large alculation amount and difficult convergence of Unmanned Aerial Vehicle (UAV) path planning, a path planning method based on Chaos Sparrow Search Algorithm (CSSA) was proposed. Firstly, a two-dimensional task space model and a path cost model were established, and the path planning problem was transformed into a multi-dimensional function optimization problem. Secondly, the cubic mapping was used to initialize the population, and the Opposition-Based Learning (OBL) strategy was used to introduce elite particles, so as to enhance the diversity of the population and expand the scope of the search area. Then, the Sine Cosine Algorithm (SCA) was introduced, and the linearly decreasing strategy was adopted to balance the exploitation and exploration abilities of the algorithm. When the algorithm fell into stagnation, the Gaussian walk strategy was adopted to make the algorithm jump out of the local optimum. Finally, the performance of the proposed improved algorithm was verified on 15 benchmark test functions and applied to solve the path planning problem. Simulation results show that CSSA has better optimization performance than Particle Swarm Optimization (PSO) algorithm, Beetle Swarm Optimization (BSO) algorithm, Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO) algorithm and Sparrow Search Algorithm (SSA), and can quickly obtain a safe and feasible path with optimal cost and satisfying constraints, which proves the effectiveness of the proposed method.
%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2020091513