融合遗传算法与粒子群算法无人机路径规划技术研究

Research on Unmanned Aerial Vehicle Path Planning Technology Integrating Genetic Algorithm and Particle Swarm Optimization Algorithm

摘要: 在无人机应用场景不断拓展的背景下，高效、精准的路径规划对其任务执行效果起着决定性作用。传统的遗传算法与粒子群算法在无人机路径规划方面各有优劣。遗传算法凭借较强的全局搜索能力，可在较大空间内探寻可能路径，但易于陷入局部最优；粒子群算法收敛速度快，能快速逼近目标解，然而后期搜索精度受限。论文提出将二者融合的创新算法。先借助遗传算法的选择、交叉和变异操作对种群进行初始化与全局搜索，为粒子群算法提供良好的初始解集。之后，引入粒子群算法的速度与位置更新机制，增强算法的局部搜索能力，克服局部最优问题。通过搭建模拟实验平台，设置复杂环境场景进行测试。实验结果表明，融合算法在路径长度、搜索时间及成功率等核心指标上，相比单一算法有明显提升。在复杂多变的动态环境中，该算法能快速规划出安全且高效的路径，助力无人机顺利完成任务。

Abstract: Against the backdrop of the continuous expansion of application scenarios for unmanned aerial vehicles (UAVs), efficient and precise path planning plays a decisive role in the effectiveness of their mission execution. The traditional genetic algorithm and particle swarm optimization algorithm each have their advantages and disadvantages in the path planning of unmanned aerial vehicles. Genetic algorithms, with their strong global search ability, can explore possible paths in a large space, but they are prone to fall into local optima. The particle swarm optimization algorithm has a fast convergence speed and can quickly approach the target solution, but its search accuracy is limited in the later stage. The paper proposes an innovative algorithm that integrates the two. First, the population is initialized and globally searched by means of the selection, crossover and mutation operations of the genetic algorithm, providing a good initial solution set for the particle swarm optimization algorithm. Subsequently, the velocity and position update mechanism of the particle swarm optimization algorithm is introduced to enhance the local search ability of the algorithm and overcome the local optimum problem. Tests are conducted by building a simulation experiment platform and setting up complex environmental scenarios. The experimental results show that the fusion algorithm has significant improvements in core indicators such as path length, search time and success rate compared with the single algorithm. In complex and ever-changing dynamic environments, this algorithm can quickly plan out safe and efficient paths, assisting unmanned aerial vehicles in successfully completing their tasks.