Evaluation and Enhancement of Resolution-Aware Coverage Path Planning Method for Surface Inspection Using Unmanned Aerial Vehicles

Abstract

We implemented and evaluated our previous path planning method for inspection using unmanned aerial vehicles (UAVs) in real-world, and identified its shortcomings in handling positioning errors. Then, we proposed an enhanced method to address this problem. The previous method theoretically guaranteed complete coverage of targets and data quality. However, we verified it in bridge inspection experiments and found that the former has not been ensured. The crucial factors of data omission are clarified as the errors in UAV positioning. Our previous method relies on appropriately setting ideal allowances to counteract positioning errors, which is challenging in practice. Therefore, we proposed an enhanced path planning method, which adaptively adjusts allowances according to positioning error to prevent omission while minimizing waypoints. In the simulation including positioning disturbances, the enhanced method consistently achieved full coverage in 1000 times simulation with over 28% waypoints less than the previous one.

Keywords

• Surface inspection
• visual inspection
• inspection path planning
• coverage path planning (CPP)
• unmanned aerial vehicle (UAV)
• remote sensing