Genetic Algorithm Based Planning of Multi-Task Intelligent Inspection Strategy for Unmanned Aerial Vehicles (UAVs)

UAV inspection plays a pivotal role in the power grid system, however, bottlenecks such as its short flight duration and low inspection efficiency have long plagued the inspection work. In order to effectively solve these problems, this paper thoroughly researches the multi-task optimal inspection strategy based on long flight time UAV platform. By adopting the hydrogen power supply technology, the flight time of the UAV is significantly improved, and the manual intervention time in the inspection process is greatly reduced. Meanwhile, combined with the genetic algorithm, we carefully planned the multi-task inspection path and successfully realized various inspection modes such as replacing the hydrogen storage tank in the middle of the UAV's mission, executing the mission at the breakpoint after resuming the flight, single-flight multitasking as well as multi-flight multitasking. This strategy not only optimizes the inspection efficiency of the UAV platform, but also provides a strong guarantee for the safe and stable operation of the grid system.