Vision-Based Robotic Solution for Wire Insertion With an Assigned Label Orientation

Cirillo, Pasquale; Laudante, Gianluca; Pirozzi, Salvatore

doi:10.1109/access.2021.3098472

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Due to the fact that DLOs have a high degree of freedom, which makes the modeling and controlling of these objects highly difficult and expensive, the automation of DLOs manipulation is still an open challenge in robotics community. Over the past several years there has been an increase of interest in research conducted into the robotic manipulation of DLOs [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Robotic Comanipulation for Deforming Cables

Almaghout¹,

Klimchik²

2022

Nelin. Dinam.

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Although deformable linear objects (DLOs), such as cables, are widely used in the majority of life fields and activities, the robotic manipulation of these objects is considerably more complex compared to the rigid-body manipulation and still an open challenge. In this paper, we introduce a new framework using two robotic arms cooperatively manipulating a DLO from an initial shape to a desired one. Based on visual servoing and computer vision techniques, a perception approach is proposed to detect and sample the DLO as a set of virtual feature points. Then a manipulation planning approach is introduced to map between the motion of the manipulators end effectors and the DLO points by a Jacobian matrix. To avoid excessive stretching of the DLO, the planning approach generates a path for each DLO point forming profiles between the initial and desired shapes. It is guaranteed that all these intershape profiles are reachable and maintain the cable length constraint. The framework and the aforementioned approaches are validated in real-life experiments.

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Section: Introductionmentioning

confidence: 99%

Vision-Based Robotic Comanipulation for Deforming Cables

Almaghout¹,

Klimchik²

2022

Nelin. Dinam.

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“…This study aims to empower the research community with valuable and useful insights that can enhance the design and development of improved and efficient deep learning-based eye-tracking models. In [ 14 ], the authors propose a vision-based robotic solution for wire insertion in which a priori knowledge of the scenario is used with a calibrated RGB camera and a robotic arm. The solution presented combines different techniques (gradient-based, trained classifiers, and stereovision) to obtain standard images.…”

Section: Introductionmentioning

confidence: 99%

Calibration of a Stereoscopic Vision System in the Presence of Errors in Pitch Angle

Felipe

Sigut

Sánchez

2022

Sensors

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This paper proposes a novel method for the calibration of a stereo camera system used to reconstruct 3D scenes. An error in the pitch angle of the cameras causes the reconstructed scene to exhibit some distortion with respect to the real scene. To do the calibration procedure, whose purpose is to eliminate or at least minimize said distortion, machine learning techniques have been used, and more specifically, regression algorithms. These algorithms are trained with a large number of vectors of input features with their respective outputs, since, in view of the application of the procedure proposed, it is important that the training set be sufficiently representative of the variety that can occur in a real scene, which includes the different orientations that the pitch angle can take, the error in said angle and the effect that all this has on the reconstruction process. The most efficient regression algorithms for estimating the error in the pitch angle are derived from decision trees and certain neural network configurations. Once estimated, the error can be corrected, thus making the reconstructed scene appear more like the real one. Although the authors base their method on U-V disparity and employ this same technique to completely reconstruct the 3D scene, one of the most interesting features of the method proposed is that it can be applied regardless of the technique used to carry out said reconstruction.

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Hole-Finding Learning Strategy for a Robot Assembly With Keyed Circular Peg

et al. 2023

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Taking the gear assembly of the reducer assembly line as a research background, the holefinding strategy for robot assembly with keyed circular pegs is proposed to solve the problem of low holefinding efficiency and success rate. in the circular hole-finding task, deviation domains were divided based on a static mechanism, position vector trajectory optimization in the step distance and direction was designed, and a deviation domain-force mapping relationship was established using a genetic algorithm-support vector machine (GA-SVM) classification algorithm; the accuracy of this algorithm is approximately 90.00%. Thirty groups completed the circular hole-finding task in an average time of 5.4 s. For the square hole-finding task, dual monocular cameras were integrated to identify corner points of the flat key and keyway. Image semantic segmentation based on deep learning was used for the corner-point recognition of the flat key to suppress the effect of changes in light intensity; the recognition has an average error of 0.39 mm. Coarse and fine adjustment circumferential deflection strategies were adopted sequentially. The 30 groups of square holefinding tasks exhibited a 96.70% success rate in an average time of 10.2 s. The proposed hole-finding strategy improves the efficiency and success rate of the gear assembly.INDEX TERMS keyed circular peg; learning-based robot hole finding; support vector machine; image semantic segmentation based on deep learning; genetic algorithm

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Vision-Based Robotic Solution for Wire Insertion With an Assigned Label Orientation

Cited by 4 publications

References 17 publications

Vision-Based Robotic Comanipulation for Deforming Cables

Vision-Based Robotic Comanipulation for Deforming Cables

Calibration of a Stereoscopic Vision System in the Presence of Errors in Pitch Angle

Hole-Finding Learning Strategy for a Robot Assembly With Keyed Circular Peg

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