TY - JOUR
T1 - Development of automatic reconfigurable robotic arms using vision-based control
AU - Zhang, Mingshao
AU - Zhang, Zhou
AU - Lotfi, Nima
AU - Esche, Sven K.
N1 - Publisher Copyright:
© American Society for Engineering Education, 2017.
PY - 2017/6/24
Y1 - 2017/6/24
N2 - 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.
AB - 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.
KW - Industrial robotics
KW - Reconfigurability
KW - Robotic arms
KW - Vision-based control
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M3 - Conference article
AN - SCOPUS:85030542766
VL - 2017-June
JO - ASEE Annual Conference and Exposition, Conference Proceedings
JF - ASEE Annual Conference and Exposition, Conference Proceedings
T2 - 124th ASEE Annual Conference and Exposition
Y2 - 25 June 2017 through 28 June 2017
ER -