Trajectory planning aided unmanned surface vehicle optimization communication method with hierarchical reinforcement learning

In maritime search and rescue (SAR), the boat swarm mode can greatly improve the success rate, but because of the influence of the curvature of the earth, the communication distance on the ground is short. Relay communication using unmanned surface vehicles (USVs) is the main means of maritime SAR, but communication speed and reliability are the core difficulties. To solve the problem, this paper proposes a trajectory planning aided unmanned surface vehicle optimization communication method with hierarchical reinforcement learning (TP–OC–HRL), which introduces relay USVs into the maritime SAR system and uses trajectory planning and the adaptive orthogonal frequency-division multiplexing (OFDM) technology as variables to optimize the communication rate of the SAR system. The paper proposes a communication optimization scheme based on hierarchical reinforcement learning, which divides the modulation coding scheme and the trajectory planning of the USVs into two hierarchical subproblems to improve the communication rate of the maritime SAR system through reinforcement learning. The experimental verification in the island area of the South China Sea shows that the TP–OC–HRL algorithm proposed in this paper has a faster convergence speed compared with that of the existing relay communication optimization methods, and can improve the communication rate of the maritime SAR system under the condition of meeting the reliability. •We established a high-speed maritime search and rescue communication method utilizing USV relays.•We established a surface USV communication optimization scheme based on hierarchical reinforcement learning.•Our method can adjust the MCS in real time based on the motion status of the USV, effectively improving the communication speed.