Abstract
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.
Highlights
The consumption of 3C (Computer, Communication, and Consumer Electronics) products has been increasing rapidly in recent years, leading to a compelling demand for the productivity of 3C products
Results of the printed circuit board (PCB) assembly experiment indicates that Prime X41 is capable of acquiring the bimanual assembly motion data for robot learning from demonstration (LfD) application
Robot learning from demonstration framework provides a feasible solution to automate 3C product assembly lines that have to change over frequently
Summary
The consumption of 3C (Computer, Communication, and Consumer Electronics) products has been increasing rapidly in recent years, leading to a compelling demand for the productivity of 3C products. Root mean square error (RMSE) are chosen to evaluate the accuracy performance in each test block To calculate the absolute contour error, three stagnation points p(skta)g, k = 1, 2, 3 are selected to determine the plane V where the reference circle lies in and its normal vector n These stagnation points can be determined by setting the circular motion agent at three random angles and acquiring the markers’ position by the optical MoCap for seconds. Based on both the linear and the circular trajectory performance evaluation results, it can be seen that Prime X41 has better trajectory performance
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