Abstract

In this paper, we propose a coverage planning algorithm for inspecting an aircraft, using an Unmanned Ariel Vehicle (UAV). Inspecting structures (e.g., bridges, buildings, ships, wind turbines, aircrafts) is considered a hard task for humans to perform, and of critical nature since missing any detail could affect the structure’s performance and integrity. Additionally, structure inspection is a time and resource intensive task that should be performed as efficiently and accurately as possible. In this paper we introduce a search space coverage path planner (SSCPP) with a heuristic reward function that exploits our knowledge of the structure model, and the UAV’s onboard sensors’ models to generate resolution optimal paths that maximizes coverage. The proposed method follows a model-based coverage path planning approach to generate an optimized path that passes through a set of admissible waypoints to fully cover a complex structure. The algorithm predicts the coverage percentage by using an existing model of the complex structure as a reference. A set of experiments were conducted in a simulated environment to test the validity of the proposed algorithm.