Cooperative USV–UAV marine search and rescue with visual navigation and reinforcement learning-based control

This paper investigates visual navigation and control of a cooperative unmanned surface vehicle (USV)-unmanned aerial vehicle (UAV) system for marine search and rescue. First, a deep learning-based visual detection architecture is developed to extract positional information from the images taken by the UAV. With specially designed convolutional layers and spatial softmax layers, the visual positioning accuracy and computational efficiency are improved. Next, a reinforcement learning-based USV control strategy is proposed, which could learn a motion control policy with an enhanced ability to reject wave disturbances. The simulation experiment results show that the proposed visual navigation architecture can provide stable and accurate position and heading angle estimation in different weather and lighting conditions. The trained control policy also demonstrates satisfactory USV control ability under wave disturbances. •Cooperative USV–UAV systems for marine search and rescue.•Convolutional neural network with spatial softmax layers for visual navigation.•Reinforcement learning based USV control with disturbance rejection ability.