Jellyfish Grasping and Transportation with a Wire-Driven Gripper and Deep Learning Based Recognition

Abstract

Automation has been adopted and realized in many industrial fields in recent years. However, it has been barely implemented in the field of dealing with living creatures. Therefore in this paper, we propose a robotic system for grasping and transporting jellyfish to automate operations involving living creatures. We created a wire-driven robotic gripper specialized for grasping jellyfish that is very soft with an extremely low friction coefficient. We trained a YOLOv5 model for recognizing jellyfish and obtain the grasping position. In experiments, we used gel jellyfish fabricated using a 3D gel printer instead of living jellyfish. Experiments were conducted to evaluate grasping range and height of the gripper. Results indicated that the grasping height limit was 15 mm from tank bottom, and the grasping range was a circular area with a diameter of 140 mm. Finally, we conducted an experiment to automatically detect, grasp, and transport jellyfish from one tank to another with the proposed robotic gripper and deep learning based recognition method.