Algorithms for dynamic control of a deep-sea mining vehicle based on deep reinforcement learning
Abstract
Deep-sea mining aims to extract mineral resources from the ocean seabed, necessitating advanced vehicles for efficient operations. These vehicles, essential for exploiting the vast underwater resources, require sophisticated navigation. The primary challenge in deep-sea navigation is the absence of satisfactory algorithms that are capable of handling the complexity and unpredictability in deep-sea environment. This research deploys advanced deep reinforcement learning algorithms, to enable dynamic control in the deep-sea navigation – which was previously challenging when using conventional methods. These algorithms with detailed optimization of the hyperparameters have been implemented on a four-track deep-sea mining vehicle, demonstrating good performance in dynamic avoidance of obstacles that are randomly deployed.