Tube-GAN: A Novel Virtual Tube Generation Method for Unmanned Aerial Swarms Based on Generative Adversarial Network

Abstract

Virtual tube is a two-dimensional or three-dimensional strip or tubular area similar to RSFC (Relative Safe Flight Corridor), which can provide smooth, feasible, and safe space for UAV swarm in environments with dense obstacles. In order to address the problem that current virtual tube planning methods are mainly based on complex heuristic algorithm with consuming time complexity, we modify the model architecture by introducing generative adversarial network (GAN), and propose a Tube-GAN model. Tube-GAN takes the key point prompt image and obstacle environment image as inputs, and outputs the image of the virtual tube, which transforms the optimization problem into an image generation problem, leveraging the performance of computational efficiency for the construction of virtual tube. The experimental results demonstrate that the proposed Tube-GAN model can quickly generate virtual tube in random environments (less than 25ms), providing a new direction for the construction of virtual tube in real-time.