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Development of Automatic Reconfigurable Robotic Arms using Vision-based Control

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Conference

2017 ASEE Annual Conference & Exposition

Location

Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

Design & Development Projects and Practices

Tagged Division

Manufacturing

Page Count

13

Permanent URL

https://peer.asee.org/28170

Download Count

242

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Paper Authors

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Mingshao Zhang Southern Illinois University, Edwardsville Orcid 16x16 orcid.org/0000-0002-6331-4010

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Mingshao Zhang is an Assistant Professor of Mechanical Engineering Department. He received his Ph.D. degree in Mechanical Engineering from Stevens Institute of Technology (2016). Prior to this, He also holds a M.Eng. degree in Mechanical Engineering from Stevens Institute of Technology (2012) and a B.E. in Mechanical Engineering and Automation from University of Science and Technology of China (2010). His research interests include Vision-based Control for Industrial Robotics, Internet of Things, Mechatronics Laboratory for Education, Machine Vision and Motion Tracking.

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Zhou Zhang Stevens Institute of Technology (School of Engineering and Science) Orcid 16x16 orcid.org/0000-0003-4599-4339

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Ph.D Candidate, Mechanical Engineering Department, Stevens Institute of Technology, Hoboken, NJ, 07030.

Email: zzhang11@stevens.edu

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Nima Lotfi Yagin Southern Illinois University, Edwardsville

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Nima Lotfi received his B.S. degree in electrical engineering from Sahand University of Technology, Tabriz, Iran, in 2006, his M.S. degree in electrical engineering from Sharif University of Technology, Tehran, Iran, in 2010, and his Ph.D. degree in mechanical engineering from Missouri University of Science and Technology,
Rolla, MO, USA, in 2016. He is currently an Assistant Professor with the Mechanical Engineering Department at Southern Illinois University Edwardsville, Edwardsville, IL, USA. His current research interests include characterization and electrochemical modeling of Li-ion batteries, traditional and electrochemical model-based Li-ion battery management system design, and real-world applications of
control and estimation theory especially in alternative and renewable energy systems, mechatronics, robotics, and electrified and autonomous transportation. Dr. Lotfi is a member of the IEEE Control Systems Society and ASME Dynamic Systems and Control Division.

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Sven K. Esche Stevens Institute of Technology (School of Engineering and Science)

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Sven Esche is a tenured Associate Professor at the Department of Mechanical Engineering at Stevens Institute of Technology. He received a Diploma in Applied Mechanics in 1989 from Chemnitz University of Technology, Germany, and was awarded M.S. and Ph.D. degrees from the Department of Mechanical Engineering at The Ohio State University in 1994 and 1997, respectively. He teaches both undergraduate and graduate courses related to mechanisms and machine dynamics, integrated product development, solid mechanics and plasticity theory, structural design and analysis, engineering analysis and finite element methods and has interests in remote laboratories, project-based learning and student learning assessment. His research is in the areas of remote sensing and control with applications to remote experimentation as well as modeling of microstructure changes in metal forming processes. He publishes regularly in peer-reviewed conference proceedings and scientific journals. At the 2006 ASEE Annual Conference and Exposition in Chicago, USA, he received the Best Paper Award for his article ‘A Virtual Laboratory on Fluid Mechanics’.

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Abstract

The traditional industrial robotic systems are designed for mass production, in which each robot needs to be calibrated and programmed for a specific task. These systems are expensive, only effective in specific applications, and vulnerable to any changes in the working environment or the task. However, mass customization has become the new frontier in product manufacturing and marketing. In order to satisfy the changes in market needs, especially for small and medium enterprises (SMEs), it is desirable to have low-cost industrial robotic systems that can be automatically reconfigured for different applications. As a supplement to traditional robotic courses, students should be educated about how to design a robust, flexible, reconfigurable and redeployable industrial robotic system. However, these contents are missing from most of current engineering curriculum due to the lack of appropriate educational robotic platforms.

The research presented here uses an assembly line robotic arm as a prototype to prove the feasibility of automatic reconfiguration. The system first uses cameras to detect and recognize objects in the assembly line and then automatically chooses the best manipulator for the assembly task. Next, the system predicts the end-effector’s error using cameras in a markerless approach. The error is compensated in the last step, in which the system automatically generates the control commands for the robotic arm using visual results as feedback. Using this robotic system as an educational platform, the students will be able to learn about several important aspects of flexible/reconfigurable manufacturing systems (e.g. robustness, flexibility, reconfigurability, redeployability, etc.) through one low-cost and easy-to-use experimental setup.

Zhang, M., & Zhang, Z., & Lotfi Yagin, N., & Esche, S. K. (2017, June), Development of Automatic Reconfigurable Robotic Arms using Vision-based Control Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. https://peer.asee.org/28170

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