Preliminary Study on Task Division for UAV-Based Visual Inspection of Large Structures with Multiple Flights Using 3D Urban Models

This paper addresses the problem of task division based on 3D urban models for visual inspection using a UAV with multiple flights. When inspecting large structures for damage detection, UAVs are often flown multiple times to obtain high-resolution data from all areas. Research is progressing on coverage path planning (CPP) methods for data collection in a single flight, and the 3D models of the target required for this can be readily obtained. However, the automation of task division for each flight has not been extensively studied. Because the scale of the target is large, it is necessary to execute a task division method that optimizes the performance efficiency of UAVs within a practical calculation time. This paper presents a task division method for data collection by multiple flights of a UAV based on the decomposition of the 3D urban models. Forming a framework in which data collection tasks are divided based on the 3D mesh decomposition, and then the CPP method is applied to each task, this enables efficient inspection of large-scale structures. In this paper, we implement three basic methods based on the strategies of avoiding going far, equalizing the amount of each task, and reducing unnecessary movement as preliminary research. Each method applies to objects on a scale of several hundred meters, and evaluates their performance in automated data collection.