Design and development of a vision-based micro-assembly system

Jayaram, Kaushik; Joshi, Suhas S.

doi:10.1177/0954405414560779

Cited by 3 publications

(1 citation statement)

References 12 publications

(13 reference statements)

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“…Robotic assembly attracted the interest of both academia and industry, due to its vital role in automating the production for many processes. Excellent robotic assembly strategies were reported in the literature that use different kinds of vision and laser sensors in order to have a precise location of the mated parts (see Biao et al, 1 Muelaner et al, 2 Jayaram and Joshi, 3 and the references therein). However, force-controlled robots appear to be one of the efficient systems in assembly, since it handles processes when the vision systems become less accurate.…”

Section: Introductionmentioning

confidence: 99%

Contact-state modelling in force-controlled robotic peg-in-hole assembly processes of flexible objects using optimised Gaussian mixtures

Jasim

Plapper

Voos

2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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This article proposes the distribution similarity measure–based Gaussian mixtures model for the contact-state (CS) modelling in force-guided robotic assembly processes of flexible rubber parts. The wrench (Cartesian force and torque) signals of the manipulated object are captured for different states of the given assembly process. The distribution similarity measure–based Gaussian mixtures model CS modelling scheme is employed in modelling the captured wrench signals for different CSs. The proposed distribution similarity measure–based Gaussian mixtures model CS modelling scheme uses the Gaussian mixtures model in modelling the captured signals. The parameters of the Gaussian mixtures models are computed using expectation maximisation. The optimal number of Gaussian mixtures model components for each CS model is determined by considering the classification success rate as an index for the similarity measure between the distribution of the captured signals and the developed models. The optimal number of Gaussian mixtures model components corresponds to the highest classification success rate; hence, object elasticity variation would be accommodated by properly choosing the optimal number of Gaussian mixtures model components. The performance of the proposed distribution similarity measure–based Gaussian mixtures model CS modelling strategy is evaluated by a test stand composed of a KUKA lightweight robot doing peg-in-hole assembly processes for flexible rubber objects. Two rubber objects with different elasticity are considered for two experiments; in the first experiment, an elastic peg of 30 Shore A hardness is considered and that of the second experiment has hardness of 6 Shore A which is even softer than the one used in experiment 1. Employing the proposed distribution similarity measure–based Gaussian mixtures model CS modelling strategy excellent classification success rate was obtained for both experiments. However, more Gaussian mixtures model components are required for the softer one that gives a strong impression of the non-stationarity behaviour increment for softer materials. Comparison is performed with the available CS modelling schemes and the distribution similarity measure–based Gaussian mixtures model is shown to provide the best classification success rate performance with a reduced computational time.

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Section: Introductionmentioning

confidence: 99%

Contact-state modelling in force-controlled robotic peg-in-hole assembly processes of flexible objects using optimised Gaussian mixtures

Jasim

Plapper

Voos

2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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Design and analysis of a flexure based passive gripper

Gupta

Perathara

Chaurasiya

et al. 2019

Precision Engineering

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C2F: Coarse-to-Fine Vision Control System for Automated Microassembly

2019

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Introduction: In this paper, authors present the development of a completely automated system to perform 3D micromanipulation and microassembly tasks. The microassembly workstation consists of a 3 degree-of-freedom (DOF) MM3A® micromanipulator arm attached to a microgripper, two 2 DOF PI® linear micromotion stages, one optical microscope coupled with a CCD image sensor, and two CMOS cameras for coarse vision. Methods: The whole control strategy is subdivided into sequential vision based routines: manipulator detection and coarse alignment, autofocus and fine alignment of microgripper, target object detection, and performing the required assembly tasks. A section comparing various objective functions useful in the autofocusing regime is included. Results: The control system is built entirely in the image frame, eliminating the need for system calibration, hence improving speed of operation. A micromanipulation experiment performing pick-and-place of a micromesh is illustrated. Conclusion: This demonstrates a three-fold reduction in setup and run time for fundamental micromanipulation tasks, as compared to manual operation. Accuracy, repeatability and reliability of the programmed system is analyzed.

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Design and development of a vision-based micro-assembly system

Cited by 3 publications

References 12 publications

Contact-state modelling in force-controlled robotic peg-in-hole assembly processes of flexible objects using optimised Gaussian mixtures

Contact-state modelling in force-controlled robotic peg-in-hole assembly processes of flexible objects using optimised Gaussian mixtures

Design and analysis of a flexure based passive gripper

C2F: Coarse-to-Fine Vision Control System for Automated Microassembly

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