A Robot 3C Assembly Skill Learning Method by Intuitive Human Assembly Demonstration

Cao, Zhiqi; Hu, Haopeng; Yang, Xiansheng; Lou, Yunjiang

doi:10.1109/wrc-sara.2019.8931930

Cited by 14 publications

(4 citation statements)

References 18 publications

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“…For 3C product assembly applications, no matter which LfD method is selected to learn the assembly skill, it is more efficient to carry out the indirect demonstration. It is because human operators can naturally perform bimanual assembly indirect demonstration with their experience [3], [14]. Accordingly, what matters in 3C product assembly demonstration data is the absolute error since what we obtained are multiple motion trajectories of 3C parts without the robots' actively tracking.…”

Section: ) Trajectory Performance Evaluationmentioning

confidence: 84%

“…Dynamic movement primitive based methods [26] utilize second-order dynamic systems to imitate the shape of motion trajectories. Gaussian mixture model based methods [7], [13], [14] learn the latent locations based on multiple demonstrated motion trajectories. Hence trajectory performance of optical MoCaps ought to be considered.…”

Section: ) Trajectory Performance Evaluationmentioning

confidence: 99%

“…finding a suitable motion capture technique [8] [9]. As the 3C product assembly motions involve complicated motions of fingers and bimanual collaboration, the optical motion capture sensors, which have been included in studies of LfD for assembly applications [10]- [14], provides an effective way to record the workers' 3C product assembly motions [15]. Considering the practical factory environment, the optical MoCap has better robustness than other motion capture techniques, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of Optical Motion Capture Sensors for Assembly Motion Capturing

Cao

Yang

et al. 2021

IEEE Access

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The optical motion capture (MoCap) sensor provides an effective way to capture human motions and transform them into valuable data that can be applied to certain tasks, e.g. robot learning from demonstration (LfD). In spite of the wide utilization of optical MoCaps in LfD studies, there are few works that explore their potentiality in small parts robotic assembly. Robot manipulation skill learning from demonstration has gained the attention of researchers recently and robotic 3C (Computer, Communication, and Consumer Electronics) product assembly turns out to be a promising application thanks to the increasing consumption of 3C products. To further explore the potential of optical MoCaps in robotic 3C product assembly. This work proposes a performance evaluation protocol that takes the characters of both optical MoCaps and 3C product assembly operations into account. The proposed evaluation protocol includes static and trajectory evaluations. The former refers to the widely used evaluation indicators such as precision and accuracy. Meanwhile, the trajectory evaluation takes contour error as an error metric. Three popular optical MoCaps are studied in the experiment. Experiment results show that the static performance of all of the three optical MoCaps can meet the requirements of the 3C product assembly task. What's more, Prime X41 possesses the best trajectory performance. This work sheds light on the wider usage of optical MoCaps in manufacturing industries.

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Section: ) Trajectory Performance Evaluationmentioning

confidence: 84%

Section: ) Trajectory Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of Optical Motion Capture Sensors for Assembly Motion Capturing

Cao

Yang

et al. 2021

IEEE Access

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“…Flexible Flat Cable (FFC), as one of the critical components of mobile phones to connect hardware and transfer data, need to be accurately detected, located, grasped and installed. However, 3C product assembly lines are mainly designed for manual assembly [1], which makes the fact that the FFCs to be assembled are often placed randomly on the convey or belt to transfer. The rapid development of computer vision has made it possible to apply visual technology to automatically detect and locate FFC, so as to replace manual operation and improve the efficiency of operation.…”

Section: Introductionmentioning

confidence: 99%

YOLOOD: an arbitrary-oriented flexible flat cable detection method in robotic assembly

et al. 2023

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Flexible Flat Cable (FFC) detection is the premise of robot 3C assembly and is challenging because FFCs are often non-axis aligned with arbitrary orientations having cluttered surroundings. However, to date, the traditional robotic object detection methods mainly regress the object horizontal bounding box (HBB), in which the size and aspect ratios do not reflect the actual shape of the target object and hardly separate the FFCs in dense. In this paper, rotated object detection was introduced into FFC detection, and a YOLO-based arbitrary-oriented FFC detection method named YOLOOD was proposed. Firstly, oriented bounding boxes (OBB) are used to reflect the object's physical size and angle information and better separate the FFCs from the dense background. Secondly, the circular smooth label (CSL) angular classification algorithm is adopted to obtain the angle information of FFCs. Finally, the head point regression branch is introduced to distinguish between the head and the tail of the FFC and expand the range of FFC detection angle to [0 •, 360 • ). The proposed YOLOOD can reach the detection performance with an average precision of 90.82% and a detection speed of 112 FPS on an FFC dataset. Meanwhile, an actual FFC grasping experiment demonstrated the proposed YOLOOD's effectiveness and feasibility

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Applications of Learning Algorithms to Industrial Robotics

Cipriani

Bottin

Rosati

2020

Mechanisms and Machine Science

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A Robot 3C Assembly Skill Learning Method by Intuitive Human Assembly Demonstration

Cited by 14 publications

References 18 publications

Performance Evaluation of Optical Motion Capture Sensors for Assembly Motion Capturing

Performance Evaluation of Optical Motion Capture Sensors for Assembly Motion Capturing

YOLOOD: an arbitrary-oriented flexible flat cable detection method in robotic assembly

Applications of Learning Algorithms to Industrial Robotics

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