Homography-Based Visual Servoing for Eye-in-Hand Robots with Unknown Feature Positions

Lai, Beixian; Li, Zhiwen; Li, Weibing

doi:10.1109/iccr55715.2022.10053865

Cited by 2 publications

(4 citation statements)

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“…More results on composite learning with function approximation can be found in [98]- [104]. Composite learning has also evolved to address several critical challenges in robotics regarding tracking control [101]- [117], interaction control [118]- [121], and visual servoing [122]- [126] (see Sec. VIII).…”

Section: Interval Data-driven Mrementioning

confidence: 99%

“…Concurrent learning has been applied to robot visual servoing in [65], where a concurrent learning homography-based visual servoing (HBVS) method was developed for a wheeled mobile robot to achieve trajectory tracking and estimate scene depth information. Composite learning has been applied to enhance visual tracking accuracy while achieving online fast calibration of camera parameters in robot visual servoing [122]- [126]. In [122], a composite learning HBVS controller was designed to achieve exact 3-D pose control of the Panda robot with 7 DoFs under a monocular eye-in-hand camera with unknown feature positions, where the depths of the feature points can be exactly estimated online.…”

Section: B Visual Servoing Applicationsmentioning

confidence: 99%

“…Composite learning has been applied to enhance visual tracking accuracy while achieving online fast calibration of camera parameters in robot visual servoing [122]- [126]. In [122], a composite learning HBVS controller was designed to achieve exact 3-D pose control of the Panda robot with 7 DoFs under a monocular eye-in-hand camera with unknown feature positions, where the depths of the feature points can be exactly estimated online. A significant extension of [122] can be found in [123].…”

Section: B Visual Servoing Applicationsmentioning

confidence: 99%

“…In [122], a composite learning HBVS controller was designed to achieve exact 3-D pose control of the Panda robot with 7 DoFs under a monocular eye-in-hand camera with unknown feature positions, where the depths of the feature points can be exactly estimated online. A significant extension of [122] can be found in [123]. In [124], a composite learning HBVS controller was developed for the Panda robot with a monocular eye-to-hand camera under unknown extrinsic parameters.…”

Section: B Visual Servoing Applicationsmentioning

confidence: 99%

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Adaptive Estimation and Control With Online Data Memory: A Historical Perspective

Pan,

Shi

2024

IEEE Control Syst. Lett.

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Online data memory is essential for adaptive estimation and control as it can enhance the performance and robustness of adaptive systems compared to adaptive systems without data memory. We provide an overview of four data memory-driven parameter estimation schemes for adaptive systems from a historical perspective, including forgetting-data memory regression extension (MRE), full-data MRE, discrete-data MRE, and interval-data MRE. For clear presentation and better understanding, a general class of nonlinear systems with linear-in-the-parameter uncertainties is applied as a unifying framework to demonstrate the motivation, synthesis, and characteristics of each MRE scheme for parameter estimation in adaptive control. Intensive comparisons of the four MRE schemes are provided to reveal their technical natures, and real-world applications are discussed to show their practicability. It is concluded that all the MRE schemes can achieve exponential parameter convergence under relaxed excitation conditions rather than the classical condition of persistent excitation which is too stringent to satisfy in practice. The distinctive features of intervaldata MRE termed composite learning are highlighted with respect to computational simplicity, estimation accuracy, robustness against perturbations, and widespread real-world applications to robot learning and control. Possible directions for future research in this area are suggested to conclude this survey.

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