Cardboard Box Depalletizing Robot Using Two-Surface Suction and Elastic Joint Mechanisms: Mechanism Proposal and Verification

Author:

Tanaka Junya, ,Ogawa Akihito

Abstract

This paper proposes a new method for a depalletizing robot in distribution center to transfer cardboard boxes. Through the use of elastic joint mechanisms, the proposed method reduces the deformation and breakage of cardboard boxes as well as shifts in position and posture due to collapses of the stacks. To validate the proposed method, we developed a linear depalletizing robot that consists of a main arm that supports a vacuum suction type end effector via elastic joint mechanisms and a conveyor arm for conveying cardboard boxes. The proposed transfer method is characterized by a series of actions using the elastic joint mechanisms of the end effector to pick up and lift a cardboard box by two of its sides and then tilt and take it out of a roll box pallet on a conveyor. Tests show that the robot can successfully transfer cardboard boxes using only simple motions in spite of various changes in box position and posture, and that the new joint mechanisms operate effectively.

Publisher

Fuji Technology Press Ltd.

Subject

Electrical and Electronic Engineering,General Computer Science

Reference21 articles.

1. W. Echelmeyer, A. Kirchheim, and E. Wellbrock, “Robotics-logistics: Challenges for automation of logistic processes,” Proc. of IEEE Int. Conf. on Automation and Logistics 2008, pp. 2099-2103, 2008.

2. D. Katsoulas, L. Bergen, and L. Tassakos, “A versatile depalletizer of boxes based on range imagery,” Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 4313-4319, 2002.

3. D. K. Katsoulas and D. I. Kosmopoulos, “An efficient depalletizing system based on 2D range imagery,” Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 305-312, 2001.

4. S. Levine, P. Pastor, A. Krizhevsky, and D. Quillen, “Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection,” The Int. J. of Robotics Research, Vol.37, Nos.4-5, pp. 421-436, 2018.

5. W. T. Townsend, “The BarrettHand grasper, programmably flexible part handling and assembly,” Industrial Robots, Vol.27, No.3, pp. 181-188, 2000.

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