A Novel Dual-Robot Accurate Calibration Method Using Convex Optimization and Lie Derivative

Jiang, Cheng; Li, Wen-long; Li, Wen-pan; Wang, Dong-fang; Zhu, Li-jun; Xu, Wei; Zhao, Huan; Ding, Han

doi:10.1109/tro.2023.3344025

Cited by 14 publications

(3 citation statements)

References 37 publications

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“…Mesh data acquired by high-resolution and high-speed [1] three-dimensional (3D) scanners have been widely used in industrial applications [2] such as reverse engineering [3], deformation monitoring [4], quality evaluation of machining [5], etc. The measurement process of industrial parts such as blades and engine bodies, produces a lot of noise.…”

Section: Introductionmentioning

confidence: 99%

An adaptive anisotropic bilateral filtering method for mesh data in scale space

Su,

Li,

Cheng

et al. 2024

Meas. Sci. Technol.

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Three-dimensional mesh data of parts, such as blades and engine bodies, have been widely used in industrial fields. Due to the different kinds of noise during mesh acquisition and the machining deficiency of parts, the mesh quality tends to be insufficient for subsequent operations. Therefore, mesh denoising is a necessary and critical procedure to improve mesh quality. Existing methods commonly apply geometry features smoothing, which may also create unexpected results, such as volume shrinkage and blurring of sharp edges. This paper proposed an adaptive anisotropic bilateral filtering method for mesh data in scale space. Firstly, the mesh is decomposed into a smooth base with low frequency and a height vector field with high frequency based on scale space theory. The denoising of the vertex spatial field is transformed into the denoising of the height vector field, aiming to only consider high-frequency information. Secondly, the bilateral filter scheme with the anisotropic Gaussian kernel is proposed to denoise the height vector field, removing noise mixed with features. The parameters in the bilateral filter scheme are chosen adaptively by maximizing the designed probability density function. The mean square angular error of the proposed method is less than 0.15rad, which is superior to the general-purpose algorithms, for instance, Laplacian filtering, bilateral mesh filtering and bilateral normal filtering algorithm.

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Section: Introductionmentioning

confidence: 99%

An adaptive anisotropic bilateral filtering method for mesh data in scale space

Su,

Li,

Cheng

et al. 2024

Meas. Sci. Technol.

Self Cite

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“…Li et al pointed out that the existing industrial robot error compensation method ignores the uncertainty of the tool centre calibration, and proposed a new regional compensation method to improve the positioning accuracy of the robot [10]. Jiang et al proposed a new precise calibration method for dual robots, which utilizes convex optimization and Lie derivatives to simultaneously solve the dual robot calibration problem [11]. Based on the above research, it can be seen that there are very few studies on the measurement uncertainty of industrial robot position accuracy.…”

Section: Introductionmentioning

confidence: 99%

Small sample uncertainty evaluation of industrial robot position accuracy measurement based on grey model

Cheng,

Zhu,

Gao

et al. 2024

Meas. Sci. Technol.

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The measurement of position accuracy of industrial robots has high complexity, and its uncertainty assessment is one of the important research topics. This study analyzes the principle of measuring the position accuracy of industrial robots by laser tracker, the evaluation model of the measurement uncertainty of the position accuracy of industrial robots is established, and various factors affecting the measurement results are analyzed, including industrial robot itself, the measurement instrument, the measurement personnel, the measurement strategy and the data processing method. There is a very small sample problem in the uncertainty evaluation process of the position accuracy of industrial robots. The literature and experiments show that the reliability of The Guide to the Expression of Uncertainty in Measurement(GUM)is low under the condition of very small sample. In this study, an improved grey method is proposed to evaluate the uncertainty. The principle of the improved grey method is discussed in detail, and the general steps of the improved grey method are summarized. The experimental data prove that the standard deviation of very small sample calculated by the improved grey method has higher accuracy. Taking the position accuracy of an industrial robot measured by a laser tracker as an example, the improved grey method is successfully applied to the measurement uncertainty evaluation of the position accuracy of industrial robots.

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“…(1) APE based on cooperative target can be further subdivided into manual markers [10,11], auxiliary projection [12], laser tracker [13], and structured light measurement [14]. In these methods, easily identifiable features are supplemented to the target manually or by automated devices to alleviate the difficulty of feature extraction and analysis during APE.…”

Section: Introductionmentioning

confidence: 99%

A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product

Zhao,

Kong,

2024

Applied Sciences

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Balancing adaptability, reliability, and accuracy in vision technology has always been a major bottleneck limiting its application in appearance assurance for complex objects in high-end equipment production. Data-driven deep learning shows robustness to feature diversity but is limited by interpretability and accuracy. The traditional vision scheme is reliable and can achieve high accuracy, but its adaptability is insufficient. The deeper reason is the lack of appropriate architecture and integration strategies between the learning paradigm and empirical design. To this end, a learnable viewpoint evolution algorithm for high-accuracy pose estimation of complex assembled products under free view is proposed. To alleviate the balance problem of exploration and optimization in estimation, shape-constrained virtual–real matching, evolvable feasible region, and specialized population migration and reproduction strategies are designed. Furthermore, a learnable evolution control mechanism is proposed, which integrates a guided model based on experience and is cyclic-trained with automatically generated effective trajectories to improve the evolution process. Compared to the m of the state-of-the-art data-driven method and the m of the classic strategy combination, the pose estimation error of complex assembled product in this study is m, which proves the effectiveness of the proposed method. Meanwhile, through in-depth exploration, the robustness, parameter sensitivity, and adaptability to the virtual–real appearance variations are sequentially verified.

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A Novel Dual-Robot Accurate Calibration Method Using Convex Optimization and Lie Derivative

Cited by 14 publications

References 37 publications

An adaptive anisotropic bilateral filtering method for mesh data in scale space

An adaptive anisotropic bilateral filtering method for mesh data in scale space

Small sample uncertainty evaluation of industrial robot position accuracy measurement based on grey model

A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product

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