Path planning of UAV for oilfield inspections in a three-dimensional dynamic environment with moving obstacles based on an improved pigeon-inspired optimization algorithm

In recent years, uninhabited aerial vehicles (UAV) have been used for oilfield inspections in many enterprises which can realize oilfield inspections by fewer workers. Path planning is one indispensable element in oilfield inspections through UAVs and it is also a complicated optimal problem. Now, although many researches have been focused on it, they are mainly discussed based on two-dimension planes. In practices, oilfields are complex three-dimensional spaces with many targeted points and moving obstacles between the starting and the ending point, which bring current methods some difficulties. In order to solve this problem, a three-dimensional environment model for oilfields is established for the first time, which includes: a static oil-well equipment, moving obstacles, and so on. Then, a cost function is defined to evaluate the best path, which includes: total length, average height, total time, and total electricity consumption. Finally, an improved pigeon-inspired optimization algorithm is proposed to solve problems about path planning in a three-dimensional dynamic environment of oilfields, which is named PIOFOA. In the PIOFOA, a pigeon-inspired optimization (PIO) algorithm is used to optimize the initial path and a fruit fly optimization algorithm (FOA) is used to continue local optimizations, so as to search the best path after movements of obstacles. Compared with some other methods, simulation results show that the proposed PIOFOA method is more effective.