UAV Trajectory Planning via Viewpoint Resampling for Autonomous Remote Inspection of Industrial Facilities

The autonomous remote inspection method based on unmanned aerial vehicle (UAV) has potential benefits in solving the safety inspection problem of large-scale industrial facilities. However, the low coverage rate caused by unsatisfactory trajectory quality is the main challenge of autonomous inspection operations. Therefore, in order to effectively optimize the trajectory quality of UAV, in this article, a mathematical model for trajectory planning considering UAV energy consumption, mapping efficiency, and target structure coverage is established while respecting various constraints related to hardware limitations of visual sensors and UAVs. To solve the above model to achieve autonomous inspection, a two-stage heuristic algorithm is designed, aiming to optimize a set of viewpoints for maximizing coverage of the target structure and reducing energy and time consumption. Finally, computational experiments were conducted based on three real industrial scenarios, proving that the model with the proposed algorithm in this study outperforms other advanced methods.