Enhancing Unmanned Ground Vehicle Evasion Through Generative Adversarial Imitation Learning in UAV Pursuit Scenarios

This study focuses on enhancing the evasion capabilities of unmanned ground vehicles (UGVs) using Generative Adversarial Imitation Learning (GAIL). The UGVs are trained to evade unmanned aerial vehicles (UAVs). A decision-making neural network has been trained via GAIL to refine evasion strategies with expert demonstrations. The simulation environment was developed with OpenAI Gym and calibrated with real-world data for the improvement of accuracy. The integrated platform including the proposed algorithm was tested in flight experiments. Results showed that the UGVs could effectively evade UAVs in the complex and dynamic environment.