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.
Increasing demand for higher production flexibility and smaller production batch size pushes the development of manufacturing expertise towards robotic solutions with fast setup and reprogram capability. Aiming to facilitate assembly lines with robots, the learning from demonstration (LfD) paradigm has attracted attention. A robot LfD framework designed for skillful small parts assembly applications is developed, which takes position, orientation and wrench demonstration data into consideration while utilizes impedance control to deal with the motion error. In view of constraints in industrial assembly applications, we propose a robot LfD framework where policy learning is carried out with separated assembly demonstration data to avoid potential under-fitting problem. With the proposed assembly policies, reference orientation and wrench trajectories are generated as well as coupled with the position data to boost their generalization and robust performance. Effectiveness of the proposed LfD framework is validated by a printed circuit board assembly experiment with a 7-DOF torque-controlled robot.
This paper presents a novel Schönflies-motion Parallel Manipulator with Rotational Pitch motion (SPM-RP) based on a single-platform fully-parallel mechanism. The analysis of position, workspace, velocity, and singularity of the SPM-RP is carried out in detail, and a dimensionless Jacobian is proposed to evaluate the manipulability of the SPM-RP. It is shown that the SPM-RP is kinematically position-decoupled, which possesses a large singularity-free workspace and excellent manipulability. The SPM-RP is actuated by four parallel prismatic actuators, enabling the manipulator to provide identical kinematic performance at all generic cross-sections perpendicular to the prismatic joint axes within its workspace. This paper thus proposes a reduced design optimization formulation, where the traditional optimization over the entire workspace is reduced to one on a representative workspace cross-section of the SPM-RP. The design optimization of the SPM-RP has been carried out by maximizing its manipulability over the total orientation workspace, which is crucial for precision assembly. A SPM-RP prototype has been developed based on the achieved optimal design. The mobility, orientation capability, total orientation workspace, and repeatability are tested and verified for the developed SPM-RP prototype. Experiments show that the SPM-RP achieves a large total orientation workspace with excellent precision performance.
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