基于自适应遗传算法的无人机轨迹优化

针对目前无人机爬升轨迹优化算法存在的收敛速度慢、容易陷入局部最优解等问题,提出了一种基于自适应遗传算法的爬升轨迹优化方法。首先,结合无人机爬升阶段的运动方程和性能指标给出爬升段轨迹的优化模型。其次,为提高染色体的多样性和算法的收敛速度,对自适应遗传算法做了相应改进,使其更适合用于爬升轨迹的优化。最后,根据无人机爬升段轨迹特点,给出具体优化步骤,并对某型无人机爬升段轨迹做了优化仿真验证,结果表明所提出的方法能够在一定程度上节省运营成本。

Climb Trajectory Optimization of UAV Based on AGA

The traditional methods for Unmanned Aerial Vehicle(UAV) climb trajectory optimization got problems of having slow velocity of convergence and coming to local optimum easily. To solve this problem, a new method based on adaptive genetic algorithm (AGA) was proposed in this paper. First, we studied the mathematical model of UAV climb trajectory optimization. Second, According to the characteristic of genetic algorithm, and to improve the diversity and the algorithm convergence speed of the GA, this paper improved AGA to make sure it is more suitable for climb trajectory optimization. And then, this paper gave the optimization steps of optimization, according to the characteristic of trajectory mathematical model. Finally, validation is made in a UAV, and the simulation result turned out that the method proposed could save up a certain extent in operating costs.