A Tube Arranging System With a Generic And Efficient Grasp Reasoning Method.

Robotic manipulation systems are urgently necessary to replace the labor generated by a avalanche of repetitive test tube operations in pharmaceutical, chemical, and biological fields. However, the currently existing test-tube manipulation robot [1] have poor generality and inefficiency in object detection and grasping reasoning. To this end, we propose a robotic manipulation system for test tubes arrangement harnessing geometric insights to enhance generality and efficiency. For object detection, instead of template matching in [1], we propose a corner positioning method combining vision and depth to detect positions of the rack and its holes; For grasp reasoning, poses of the gripper are adaptively adjusted by the estimated attitudes of test tubes, instead of brute force search in the working space as in [1]. Experiments manifest our robotic manipulation system to be more highly generalized and efficient in contrast to [1], achieving success rates for randomly-posed and vertical-posed test tubes up to 96.6 % and 100 % respectively, and the execution efficiency is improved by 38.4%.