Enhanced deep reinforcement learning for integrated navigation in multi-UAV systems^☆

In multiple Unmanned Aerial Vehicles (UAV) systems, achieving efficient navigation is essential for executing complex tasks and enhancing autonomy. Traditional navigation methods depend on predefined control strategies and trajectory planning and often perform poorly in complex environments. To improve the UAV-environment interaction efficiency, this study proposes a multi-UAV integrated navigation algorithm based on Deep Reinforcement Learning (DRL). This algorithm integrates the Inertial Navigation System (INS), Global Navigation Satellite System (GNSS), and Visual Navigation System (VNS) for comprehensive information fusion. Specifically, an improved multi-UAV integrated navigation algorithm called Information Fusion with Multi-Agent Deep Deterministic Policy Gradient (IF-MADDPG) was developed. This algorithm enables UAVs to learn collaboratively and optimize their flight trajectories in real time. Through simulations and experiments, test scenarios in GNSS-denied environments were constructed to evaluate the effectiveness of the algorithm. The experimental results demonstrate that the IF-MADDPG algorithm significantly enhances the collaborative navigation capabilities of multiple UAVs in formation maintenance and GNSS-denied environments. Additionally, it has advantages in terms of mission completion time. This study provides a novel approach for efficient collaboration in multi-UAV systems, which significantly improves the robustness and adaptability of navigation systems.