Improving automated insertion applications by restoring rigid-body
                        condition in point-based registration

Cheok, Geraldine S.; Franaszek, Marek; Wyk, Karl Van; Marvel, Jeremy A.

doi:10.6028/nist.ir.8198

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…As the authors in [21] concluded: "As with most engineering questions, there is no definite answeronly a set of performance tradeoffs to consider." It was shown in a related study that reduced RMST is correlated with a better outcome of peg insertion [41]. When RMST was reduced by 60 %, this level of improvement led to a 4-to 11fold reduction in the failed insertion rate, depending on the peg-hole tolerances.…”

Section: Discussionmentioning

confidence: 93%

Improving 3D vision-robot registration for assembly tasks

Franaszek¹,

Cheok²,

Wyk³

et al. 2020

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Good registration between the coordinate frames of a perception system and a robot is important for the efficient operation of autonomous systems in vision-guided assembly lines. Rigid-body registration, which is based on the measurement of corresponding points (fiducials) in both frames, is a commonly used method. Noise and possible bias in the measured points degrade the quality of registration as gauged by the Target Registration Error (TRE). A method to restore the rigid body condition (RRBC) was recently developed to reduce TRE when the bias is larger than the noise. This was achieved by applying corrections to target locations in the vision sensor frame. This paper presents two procedures to improve the performance of the RRBC method by: 1) selecting the location of fiducials used for calculating correction to the target location and 2) selecting the correct arm configuration for fiducial from multiple configurations as calculated by inverse kinematics. Experiments performed with a motion tracking system and two different robot arms show that these two procedures can further reduce the Root Mean Square of TRE (RMST) to less than 20 % of the uncorrected RMST.

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Section: Discussionmentioning

confidence: 93%

Improving 3D vision-robot registration for assembly tasks

Franaszek¹,

Cheok²,

Wyk³

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

Improving Automated Insertion Task in Robotics by Reducing Registration Error

Cheok¹,

Franaszek²,

Wyk³

et al. 2021

IFIP Advances in Information and Communication Technology

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A peg-in-hole operation is representative of common tasks performed in assembly lines by robots. It requires registration of the coordinate frame where a part resides to the robot frame where it is acted upon. Poor registration causes misalignment of the peg and the hole which will result in a failed insertion of the peg. In this study, the dependence of the outcome of the insertion task on the quality of registration is investigated. It was shown in earlier studies that Restoring Rigid-Body Condition (RRBC) leads to decreased target registration error. This study quantifies the improvement in the Pass/Fail rate when the target registration error is reduced for a given peg-hole tolerance. A robot arm operated in position control mode was used to perform the insertion without performing any search algorithms or force/torque sensing. Results show that use of the RRBC method yielded substantial reduction in failed insertions, especially for tighter tolerances.

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Improving automated insertion applications by restoring rigid-body condition in point-based registration

Cited by 2 publications

References 6 publications

Improving 3D vision-robot registration for assembly tasks

Improving 3D vision-robot registration for assembly tasks

Improving Automated Insertion Task in Robotics by Reducing Registration Error

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