Many-objective optimization based path planning of multiple UAVs in oilfield inspection

摘要

Unmanned aerial vehicles (UAVs) inspection can be considered as a path planning problem for the UAVs, which needs UAVs to traverse all task points one by one by avoiding some obstacles. In most current researches, only single objective function is used in the path planning for the UAVs, and some other objectives are seldom considered together, like: the number of UAVs, the required inspection time and so on. Therefore, in order to overcome the defect of using single objective function to plan the path of UAVs, a many-objective optimization based multiple UAVs path planning method is studied in this paper. Firstly, four objective functions are chosen which include the flight distance, the flight stability, the number of UAVs and the time offset of reaching the task point; considering the arrival time of UAVs at each task point is an interval value in the practical application, the time offset of reaching the task point is set as an interval objective function, which is handled by the matter-element extension method in this paper. Then, an improved NSGA-III algorithm is proposed to solve the established many-objective optimization problem, which uses fruit fly optimization algorithm (FOA) to replace the genetic algorithm (GA) in the NSGA-III algorithm. Finally, several evaluation indictors (Spread, generational distance (GD), inverse generational distance (IGD), and running time) are used to choose the optimal flight paths of multiple UAVs. Through simulation comparisons with other algorithms, it is concluded that the improved NSGA-III algorithm is more effective in the path planning of UAVs.