Develop of a Lightweight Underwater Robotic Arm for Automatic Sampling of Seafloor Hydrothermal Fluid

Traditional sampling methodologies that depend on the manual operation of large submersibles are characterized by significant time consumption and labor intensity. The incorporation of autonomous underwater robotic arms within unmanned underwater vehicles (UUVs) presents a promising opportunity to markedly improve sampling efficiency. This paper delineates the design of a lightweight underwater robotic arm specifically engineered for the sampling of hydrothermal fluids at the seafloor. We establish a comprehensive modeling framework that integrates kinematic, rigid‐body, and hydrodynamic parameters. To address environmental uncertainties and unmodeled dynamics, we employ a long short‐term memory (LSTM)–augmented model in conjunction with a momentum‐based observer for end‐effector force estimation, obviating the need for external force/torque sensors. Furthermore, we propose a multi‐sensor fusion control strategy that synergizes visual perception with temperature tracking and force feedback, thereby facilitating autonomous and precise sampling of hydrothermal vents. The proposed methodology has undergone experimental validation, yielding promising outcomes.