基于粒子群优化算法的无人战斗机路径规划方法

Study on uninhabited combat arial vehicle path planning method based on particle swarm optimization algorithm

对于无人机的路径规划问题,从和机器人路径规划问题的差别入手,通过粒子群优化算法对有限数目的采样航点的优化,使用高次B样条曲线拟合出满足路径最短且威胁最小的无人战斗机的飞行路径。研究了路径规划约束的数学模型、粒子构造方式和粒子的评价适应度函数。通过仿真对目前出现的基于粒子群优化算法的无人机路径的多项式拟合方法和所提出的基于B样条拟合的方法进行了比较。仿真结果表明,使用粒子群算法优化出来的B样条曲线比多项式拟合法和几何方法更加合理有效。

Based on the difference of the path planned for robots and unmanned arial vehicles （UAV）, a uninhabited combat arial vehicle （UCAV） path planning algorithm based on the high-order B-spline curve fitting is presented, which the finite sample UCAV flight points are optimized by particle swarm optimization （PSO） algorithm. The UCAV path planning constraints model and the structures of the particles and the fitness value equations of particles are studied. The two curve fitting methods, the polynomial curve fitting method and the proposed s B-spline curve fitting method, are compared. Simulation shows that the B-spline curve fitting method is a more reasonable and effective hath planning method.