A Novel Path Planning Method for Aerial UAV based on Improved Genetic Algorithm

In the actual flight mission of modern drones, the range of obstacles in the sensor detection environment will be deviated, and the control system will also have control errors when guiding the drone to fly. Therefore, accurate path planning is very important. In this paper, the novel path planning method for aerial UAV based on improved genetic algorithm is studied. The traditional path planning algorithm is relatively mature and has been widely used in the pathfinding problem of a single UAV, which consider less of the complexity, hence, our model will take the multi-UAV path planning as the target. In the designed pipeline the genetic algorithm is firstly designed, with the prior information of UAV performance constraint model. GA is not good at detailed search of local areas, which leads to poor local search ability of the algorithm and affects the optimization performance of the algorithm, hence, the PSO is combined for the optimization. The parameter of the GA crossover and mutation step is modified with the PSO to achieve the better performance. By simulating the proposed model, the path planning performance under the complex environment is tested.