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About

Changyu Lu is a pioneering researcher in the field of autonomous underwater robotics, with a primary focus on intelligent navigation and control for deep-sea mining vehicles. His work addresses critical challenges in operating robots in complex, unstructured underwater environments, particularly through advanced computational methods. Lu’s most significant contribution is the development of an improved particle swarm optimization algorithm for three-dimensional path planning of deep-sea mining vehicles, which overcomes the limitations of conventional methods prone to local optima and slow convergence—a work that has garnered 16 citations. He has further advanced the field by proposing an optimized deep reinforcement learning framework for dual-task control, enabling simultaneous path following and obstacle avoidance, a breakthrough for real-world deployment. Additionally, Lu has innovated in underwater localization with a direct forward-looking sonar odometry technique, offering a two-stage approach that enhances accuracy for near-bottom operations. His cumulative work, with over 19 citations, demonstrates a clear trajectory toward making deep-sea mining vehicles more autonomous, reliable, and efficient, positioning him as a rising expert in marine robotics and artificial intelligence integration.