Cooperative Coverage Path Planning for Air-Ground Heterogeneous Robots in Aircraft Skin Inspection Tasks

Aircraft skin inspection is crucial for ensuring flight safety. Due to the complexity of aircraft structures, traditional manual operations and the use of a single type of robot such as Unmanned Ground Vehicles (UGVs) or Unmanned Aerial Vehicles (UAVs) have different degrees of limitations. Therefore, this paper proposes a method for Collaborative Coverage Path Planning (CCPP) using aerial and ground heterogeneous robots. Firstly, an obstacle map is constructed for collision detection. Secondly, viewpoints are classified based on the accessibility constraints of different robots. Then, the Digital Differential Analyzer (DDA) algorithm is combined with the Rapidly-exploring Random Tree Star (RRT*) algorithm to generate collision-free paths based on the classified viewpoints. Additionally, a time-cost matrix is built considering the differences in maneuverability among different robots. Finally, a tailored dual-population genetic algorithm is designed to achieve fast solutions for complete coverage paths, and the fitness function incorporates considerations for endurance constraints. The effectiveness of the proposed method is validated through simulation experiments in this paper.