부산대학교 도서관

Delicate underwater manipulation tasks such as biological specimen collection are promising fields that require new robotic designs and intelligent robotic technologies. In this study, we proposed an automatic aquatic object-collecting system with a soft manipulator controlled by a reinforcement learning-based controller. For underwater sensing, we implemented a visual perception framework to restore the quality of the underwater image, detect the seafood animals, and track the target's position. The online learning ability of the reinforcement learning-based controller endowed strong adaptability for the soft manipulator against underwater disturbances. The water tank grasping tests show a 91.7% successful grasping rate without flow disturbance and 83.3% with flow disturbances. We demonstrated that the soft robotic collecting system gripped seafood animals in a lab aquarium as well as the natural seabed environment. The real-world experimental results showed that the robot successfully collected 28 shells within 40 min at a water depth of 15 m and even completed grasping tasks in a dark environment. Our results demonstrated that this manipulator prototype is potentially applicable for fully autonomous delicate objects underwater.