The use of UAVs for aircraft maintenance inspections has emerged as a promising solution to reduce maintenance costs and increase inspection quality by providing efficient and accurate data collection. In this paper a novel path planning approach is proposed for autonomous aircraft maintenance inspections using UAV swarms, leveraging a frontier-following algorithm. The algorithm guides a drone's trajectory to cover an aircraft's surface while maintaining a constant distance tangent to the surface. Designed to operate with delayed depth sensing methods, where scenes are reconstructed using structure from motion principles, this approach can be integrated with online reconstruction techniques. The frontier-following methodology results in a characteristic spiraling pattern behavior and facilitates complex surface tracking. The primary environment sensing detector, an RGB camera, is angled towards a target on the frontier. This allows for preliminary mapping of a route prior to traversal, while a collision avoidance strategy ensures collision-free flight by tracking an obstacle-free tunnel around the drone. Furthermore, this method can be scaled up for application to swarming agents, accelerating surface coverage. Simulation results demonstrate the algorithm's efficiency in achieving comprehensive surface coverage. Frontier-following path planning outperforms the 2D coverage path planning algorithm Random Walk, while performing lower than Spiral Spanning Tree Coverage - a result consistent with the latter's optimality for 2D surfaces. The proposed method's ability to successfully track complex 3D surfaces, a capability lacking in traditional 2D coverage path planning algorithms, renders it a promising solution for autonomous inspection systems.