Abstract
Dynamic obstacle avoidance is key to deep-sea mining vehicles. To enhance the dynamic avoidance capability in unpredictable seabed environments, this study employed an Improved Deep Deterministic Policy Gradient (IDDPG) algorithm. By developing intelligent controllers through extensive IDDPG optimization, this research achieves a significant breakthrough in enabling deep-sea mining vehicles to safely navigate around dynamically moving obstacles, demonstrating the algorithm's wide applicability and reliability. A notable achievement of this work is the strategy developed to balance safety and operational efficiency in obstacle avoidance contexts, underscoring the potential for future advancements in multi-vehicle operations and AI-based navigational systems. This foundation paves the way for enhancing deep-sea mining safety and operational efficiency, with supplementary materials provided for a comprehensive understanding of the methodology and the level of performance achieved in the results.
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