Autonomous Underwater Mining Operations using Proximal Policy Optimization for Environmental Sustainability

Autonomous underwater mining operations offer a distinctive chance to gather valuable materials from the ocean floor while reducing human intervention. The ecological consequences of such activities must be meticulously regulated. This work investigates the utilisation of Proximal Policy Optimisation (PPO), a reinforcement learning (RL) technique, to enhance autonomous underwater mining operations while prioritising environmental sustainability. A PPO-based autonomous underwater mining system that optimises resource extraction efficiency and reduces environmental impact via adaptive, real-time decision-making in dynamic marine environments is developed. The system underwent testing in a simulated underwater mining environment, autonomously managing mining equipment to harvest minerals while reducing environmental disturbances, including silt plumes and habitat degradation. The results indicated a 25% enhancement in resource extraction efficiency relative to traditional management approaches, alongside a 30% reduction in environmental effect, as seen by the reduction in sediment disturbance and ecosystem disruption. These findings highlight the capacity of RL algorithms, such as PPO, to improve the sustainability and efficiency of autonomous underwater mining operations. The further advancement of this methodology may result in enhanced safety and sustainability in underwater resource extraction techniques.