Underwater Robotics in Obstacle Avoidance and Containment Operations

Our research team has successfully implemented underwater robotics research focused on obstacle avoidance and target containment. This study leverages state-of-the-art deep learning and artificial intelligence technologies, employing sophisticated algorithms to train underwater robots for autonomous navigation and target tracking. The implementation incorporates sensor fusion techniques and computer vision algorithms that enable real-time environment mapping and dynamic path planning. Our obstacle avoidance system utilizes reinforcement learning models that continuously adapt to underwater conditions, while the containment mechanism employs multi-robot coordination algorithms for effective target encirclement. These technological advancements significantly enhance the intelligence level of underwater robots, providing more reliable and efficient technical support for marine development, resource exploration, and related fields. The code architecture includes modular components for perception, decision-making, and control systems, allowing for scalable deployment in various underwater applications.