基于改进遗传算法的无人机协同航路规划

Cooperative Path Planning for Unmanned Aerial Vehicles Based on Improved Genetic Algorithm

研究多无人机协同航路规划问题,为提高协同航路规划的精度和效率,针对传统遗传算法易陷入局部最优、收敛速度慢的缺陷,提出一种基于改进遗传算法的无人机协同航路规划方法;改进算法利用混沌扰动产生初始种群P(t),将混沌扰动与模拟退火算法结合作为遗传操作的一个算子,并引入了协同时间构造了新的协同适应度函数f;通过这些改进,避免了求解时陷入局部最优,并能在短时间内找到较优的协同航路;仿真结果验证了该方法的快速性和可行性,可有效解决多无人机协同执行任务的航路设计问题。

In order to improve the accuracy and efficiency of the collaborative route, we start to study the collaborative route plan of the UVA. And for the traditional genetic algorithm has some defects such as local optimum and slow convergence, we propose a method of the collaborative route plan of the UVA, which is based on improved genetic algorithm. The improved algorithm uses chaotic disturbance to generate initial population, which combine chaos disturbance with simulated annealing algorithm as an operator of the genetic manipulation, and it lead into a collaborative time to construct a new collaborative fitness function. By these improvements, we avoid the defect of local op- timum and it is much easier to find a better collaborative route in short time. The simulation results verify the fast and feasibility of the meth- od, which can solve the route design problem of multi--UVA collaborative tasks efficiently.