Generalized joint model for robot manipulator kinematic calibration and compensation

Driels, Morris; Pathre, Uday S.

doi:10.1002/rob.4620040107

Cited by 28 publications

(5 citation statements)

References 7 publications

(1 reference statement)

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“…In accordance with Eq. (38), a lower bound on task reliability is provided by: 11 Rtoflb = rI Rr r=l Using the method suggested by Ramak~rnar,,~ a further lower bound can be computed by neglecting the higher-order terms:…”

Section: 4 Augmented Entropy Methodsmentioning

confidence: 99%

Task reliability assessment in robotic systems

Musto

Saridis

1995

J. Robotic Syst.

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A new method for assessing the performance of a robotic system performing a high‐precision task has been developed. The method utilizes an entropy‐based formulation to provide a measure of the reliability of a robotic system with respect to a task specification. The derivation of the proposed technique is presented, including two alternative formulations that differ in terms of their complexity and level of approximation. Methods for combining these measures for complex robotic tasks are detailed. The new method is demonstrated in a case study involving a robotic assembly system, in which the entropy formulation is used to select an optimal system design from a set of alternatives. (c) 1995 John Wiley & Sons, Inc.

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Section: 4 Augmented Entropy Methodsmentioning

confidence: 99%

Task reliability assessment in robotic systems

Musto

Saridis

1995

J. Robotic Syst.

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“…As geometric models are well known, we focus more on elastic models representing nongeometric defaults. Many elastic models are based on the DH parameterization, completed with some non-geometric defaults 33,34,35 . To define an accurate and complete model, three techniques go further:…”

Section: Model Selection and Definitionmentioning

confidence: 99%

Inline measurement strategy for additive manufacturing

Bordron

Anwer

Bruneau

2018

Proceedings of the Institution of Mechanical Engineers, Part B:

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Additive manufacturing (AM) takes a growing place in industry tanks to its ability to create free form parts with internal complex shape. Yet the final surfaces quality of the AM parts is still a challenge since it doesn't reach the required level for final use. To address this issue it is necessary to measure the form and dimensions deviation in order to plan post-process operations to be considerate. More over in a context of industry 4.0, this measurement step should be fully integrated into the manufacturing line as close as possible to the AM process and post-process. We introduce in this article an inline measurement solution based on a robot combined with a laser sensor. Robot allows reaching most of the orientation and positions necessary to digitize complex parts in a short time. The use of robot for digitizing is already addressed but not for metrological applications. Robots are perfectly design for velocity, ability and robustness but since now their poor positioning accuracy in not compatible with measuring requirements. The strategy adopted in this article is to provide an algorithm to generate path planning for digitizing AM parts at a given quality of the resulting cloud of points. After a discussion about the geometric and elastic model of the robot to identify the one that answers the quality requirements, the performances of the robot are evaluated. Thus several performances maps are introduced to characterize the behavior of the robot in its working volume. The qualification of the digitizing sensor is also performed to identify relation between digitizing parameters and the quality of final cloud of points. By using data resulting from the qualifications of sensor and robot and the part's CAD model the algorithm allows generating path planning to ensure the finale quality necessary to measure the shape deviation.

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“…sin(G) = e and cos(G) = 1). Furthermore, if the second and higher-order terms are neglected then equation (17) can be expressed as -ex 8y (19) [aT]= -'~ ,,:…”

Section: Joint Error Motions Identification Algorithmmentioning

confidence: 99%

“…The model accounts for errors due to inaccuracies in the link parameters and the errors resulting from the relative motion between the machine's structural elements. On the other hand, Driels and Pathre [19,20] proposed a generalised joint model which describes each joint as a rigid-body motion. The state of each joint is described with 10 parameters, of which 4 are the perturbation errors corresponding to the kinematic link parameters introduced by Denavit-Hartenberg, 3 are orientation-error motions and 3 are position-error motions.…”

Section: Introductionmentioning

confidence: 99%