View planning for visual detection coverage tasks of large airplane upper surface using UAVs

In order to enhance the efficiency of visual inspection and effectively carry out 3D visual coverage tasks, this paper focuses on the 3D view planning problem concerning the visual coverage of an airplane’s surface using unmanned aerial vehicles (UAV). Our objective is to attain a sufficiently high coverage rate with the least number of viewpoints. The contributions of this work are enumerated as follows. Firstly, the 3D model of the target aircraft is spatially extended in accordance with the depth range of the camera mounted on the drone, thereby confining the sampling range of 3D viewpoints. Next, a candidate set of viewpoints is generated through random sampling and the probabilistic potential field technique. Subsequently, we propose a novel hyper-heuristic algorithm. In this algorithm, a genetic algorithm serves as a high-level heuristic strategy, in tandem with multiple low-level heuristic operators devised for combinatorial optimization. This not only augments the capacity to seek the global optimal solution but also expedites the convergence rate, aiming to ascertain the optimal subset of viewpoints. Moreover, we devise a new fitness function for appraising candidate solution vectors in the set covering problem (SCP), strengthening the evolutionary guidance for genetic algorithms. Eventually, experimental findings on the simulated and real airplanes corroborate the efficacy of the proposed method, i.e., it markedly diminishes the requisite number of viewpoints and augments inspection efficiency.