Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (7): 2128-2136.DOI: 10.11772/j.issn.1001-9081.2020091513

Special Issue: 前沿与综合应用

• Frontier and comprehensive applications • Previous Articles     Next Articles

Path planning method of unmanned aerial vehicle based on chaos sparrow search algorithm

TANG Andi1, HAN Tong2, XU Dengwu3, XIE Lei1   

  1. 1. Graduate School, Air Force Engineering University, Xi'an Shaanxi 710038, China;
    2. School of Aeronautical Engineering, Air Force Engineering University, Xi'an Shaanxi 710038, China;
    3. Unit 94855, Quzhou Zhejiang 324000, China
  • Received:2020-09-28 Revised:2020-11-12 Online:2021-07-10 Published:2020-11-26
  • Supported by:
    This work is partially supported by the Natural Science Foundation of Shaanxi Province (2020JQ-481),the Aeronautical Science Foundation of China (201951096002).


汤安迪1, 韩统2, 徐登武3, 谢磊1   

  1. 1. 空军工程大学 研究生院, 西安 710038;
    2. 空军工程大学 航空工程学院, 西安 710038;
    3. 94855部队, 浙江 衢州 324000
  • 通讯作者: 韩统
  • 作者简介:汤安迪(1996-),男,重庆人,硕士研究生,主要研究方向:任务规划、机载武器系统;韩统(1980-),男,陕西西安人,副教授,博士,主要研究方向:任务规划、机载武器系统;徐登武(1980-),男,浙江衢州人,工程师,博士,主要研究方向:机载武器系统;谢磊(1997-),男,江苏常州人,硕士研究生,主要研究方向:无人机作战系统与技术。
  • 基金资助:

Abstract: 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.

Key words: optimization algorithm, Sparrow Search Algorithm (SSA), chaos operator, opposition-based learning, path planning

摘要: 针对无人机(UAV)航迹规划求解计算量大、难收敛等问题,提出了一种基于混沌麻雀搜索算法(CSSA)的航迹规划方法。首先,建立二维任务空间模型与航迹代价模型,将航迹规划问题转化为多维函数优化问题;其次,采用立方映射初始化种群,并使用反向学习策略(OBL)引入精英粒子,增强种群多样性,扩大搜索区域范围;然后,引入正弦余弦算法(SCA),并采用线性递减策略平衡算法的开发与探索能力,当算法陷入停滞时,采用高斯游走策略帮助算法跳出局部最优;最后,将提出的改进算法在15个基准测试函数中进行性能验证,并应用于航迹规划问题求解。仿真结果表明,CSSA的寻优性能优于粒子群优化(PSO)算法、天牛群优化(BSO)算法、鲸鱼优化算法(WOA)、灰狼优化(GWO)算法和麻雀搜索算法(SSA),并且能够快速地得到一条代价最优、满足约束的安全可行航迹,验证了所提方法的有效性。

关键词: 优化算法, 麻雀搜索算法, 混沌算子, 反向学习, 航迹规划

CLC Number: