CCM: Cooperative Cross-Boundary Multimodal Communication Leveraging Swarms of UAVs via Multiagent Reinforcement Learning

Autonomous Underwater Vehicles (AUVs) have been becoming excellent platforms in marine environmental monitoring and data collection missions. However, it is challenging to obtain real-time data (e.g., video and high definition images) from AUV swarms, as traditional methods have limitations, such as low-data-rate communication and restricted mobility. To tackle these challenges, in this paper, we exploit complementary strengths of acoustic-optical technology, as well as the mobility of unmanned aerial vehicles (UAVs), and propose a cooperative cross-boundary multi-modal (CCM) communication scheme, in which multiple UAVs carrying acoustic-optical communication nodes serve as mobile sinks for swarms of AUVs. In this scheme, to deal with the imbalanced coverage problem, we design a cooperative movement strategy (CMS) algorithm for multiple UAVs based on the multi-agent reinforcement learning (MARL) approach, combined with particle filtering and task assignment to UAVs for achieving dynamic coverage of AUVs. Furthermore, to achieve high data-rate transmission dealing with optical beam link misalignment problem caused by interference from currents and movement of AUVs, we design an adaptive pointing adjustment (APA) algorithm to construct a stable optical link by controlling the pointing and divergence angles of optical beam for reliable high-speed data transmission. Through extensive simulations using open-source AUV trajectory datasets and ocean current datasets, we demonstrate the effectiveness of the proposed scheme with robust network performance under dynamic maritime conditions. The code is available at https://github.com/SDUST-smartocean/CCM.