How can AUVs hunt covertly using diffusion models?
Adaptive AUV Hunting Policy with Covert Communication via Diffusion Model
This paper proposes a new method for coordinating multiple autonomous underwater vehicles (AUVs) to hunt a target, even if the target can eavesdrop on their communications. It uses a "covert communication" strategy to make it harder for the target to understand the AUVs' plans. The core algorithm, AMADP, uses a diffusion model to predict the AUVs' movements and an adaptive attention mechanism for better coordination. This combination allows the AUVs to work together effectively while remaining stealthy. The approach uses offline reinforcement learning, training on pre-collected data, making it suitable for complex and communication-constrained underwater environments. Key improvements over previous methods include covert communication integration and efficient trajectory planning with diffusion models.