基于遗传算法的无人机航迹规划代价函数

A Study on Cost Function of UAV Path Planning Based on Genetic Algorithm

针对当前使用的无人机航迹规划代价函数的不足之处,提出一种利用遗传算法对无人机航迹规划代价函数进行优化的方法。对基本遗传算法进行了局部改进,设计了航迹极坐标编码方式及航迹适应度函数,在采用基本遗传操作算子的基础上采取精英保存策略,提高了算法的效率;采用代价归一化并进行优化的思想,得到优化之后的代价函数权重值。优化结果表明,该方法可以获得代价更低的航迹。

With the shortage of cost function of UAV path planning, an optimization method based on genetic algorithm is proposed. The traditional genetic algorithm is improved and the polar coordinate coding mode and path fitness function are designed. On the basis of genetic operators, the strategy for elite preserving is adopted to promote the algorithm effectiveness Besides cost normalized and optimized thought are used to get the optimized weight values. The optimization results show this method can obtain the lower cost path.