Homography based visual servo control with scene reconstruction

Parikh, Anup; Kamalapurkar, Rushikesh; Chen, Hsi-Yuan; Dixon, Warren E.

doi:10.1109/cdc.2015.7403318

Cited by 17 publications

(5 citation statements)

References 28 publications

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“…Inspired by Parikh et al (2016), on the basis of the concurrent learning framework, the adaptive updating law for the depth estimate trued^*(t) is designed as follows: where Γ 1 , Γ 2 ∈ ℝ + are updating gains, constant N ∈ ℤ + denotes the number of sampling periods from the beginning and t k ∈ [0, t ] is sampling time instant.…”

Section: Adaptive Control Developmentmentioning

confidence: 99%

“…It can be seen that, concurrent learning items in the adaptive updating law [equation (14)] are capable of using data recorded in N sampling periods, and sufficient smoothers can be used to estimate ė 1 (t k ) and dtrueṪ*hz(tk) accurately. Consequently, a prominent improvement can be achieved for parameter estimation (Parikh et al , 2016):…”

Section: Adaptive Control Developmentmentioning

confidence: 99%

“…Some researchers have developed the method of depth identification for robot manipulators while completing trajectory tacking tasks. By using concurrent leaning techniques with rich recorded data, Parikh et al (2016) developed an adaptive tracking controller for a robot manipulator to complete the task of identifying scene depth while tracking a trajectory, and exponential convergence is guaranteed. Moreover, unlike industrial robots, wheeled mobile robots are subject to complex nonholonomic constraints, which bring more difficulty for design of control laws (Zhang et al , 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by the visual servo tracking method in Chen et al ’s work (2016) and the depth identification method in Parikh et al ’s work (2016), a novel visual servoing strategy is developed to achieve simultaneous mobile robot trajectory tracking and scene depth identification task. First, the robotic system is described in detail and relationships between each frame are introduced.…”

Section: Introductionmentioning

confidence: 99%

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Concurrent-learning-based visual servo tracking and scene identification of mobile robots

Qiu

Shi

et al. 2019

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Purpose The purpose of this paper is to present a visual servo tracking strategy for the wheeled mobile robot, where the unknown feature depth information can be identified simultaneously in the visual servoing process. Design/methodology/approach By using reference, desired and current images, system errors are constructed by measurable signals that are obtained by decomposing Euclidean homographies. Subsequently, by taking the advantage of the concurrent learning framework, both historical and current system data are used to construct an adaptive updating mechanism for recovering the unknown feature depth. Then, the kinematic controller is designed for the mobile robot to achieve the visual servo trajectory tracking task. Lyapunov techniques and LaSalle’s invariance principle are used to prove that system errors and the depth estimation error converge to zero synchronously. Findings The concurrent learning-based visual servo tracking and identification technology is found to be reliable, accurate and efficient with both simulation and comparative experimental results. Both trajectory tracking and depth estimation errors converge to zero successfully. Originality/value On the basis of the concurrent learning framework, an adaptive control strategy is developed for the mobile robot to successfully identify the unknown scene depth while accomplishing the visual servo trajectory tracking task.

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Section: Adaptive Control Developmentmentioning

confidence: 99%

Section: Adaptive Control Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Concurrent-learning-based visual servo tracking and scene identification of mobile robots

Qiu

Shi

et al. 2019

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“…A line of approach to HBVS is characterized by the decomposition of the homography matrix to obtain the orientation and the scaled translation and use them in a pose-based control scheme. Following this approach, it is necessary to compensate for the unknown depth [9], [10], and to estimate the rotation matrix [11], [12]. The applications mainly concern mobile robotics [13], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Homography-Based Sampled-Data Visual Servoing

Costanzo,

Maria,

Natale

2024

IEEE Control Syst. Lett.

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Classical position-based and image-based visual servoing methods suffer from some drawbacks, the former require a three-dimensional model of the observed scene and they are sensitive to camera calibration errors, the convergence of the latter cannot be ensured in the whole feature space. In the last two decades, the homography-based visual servoing approaches have been widely investigated. They overcome the above drawbacks and avoid the intermediate process of feature extraction. The main results on homography-based control schemes are formulated in the continuous time domain. Due to the limited camera frame rate, actuation delay, and computation time, the intrinsic sampled-data nature of such control schemes should be taken into account. To this aim, in this paper, we propose an exact sampled-data model of the homography dynamics and an adaptive visual controller that directly uses the homography matrix and explicitly takes into account camera velocity limits ensuring global asymptotic stability at the same time.

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Homography-Based Visual Servo Tracking Control of Wheeled Mobile Robots with Simultaneous Depth Identification

Qiu

Shi

et al. 2017

Neural Information Processing

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Homography based visual servo control with scene reconstruction

Cited by 17 publications

References 28 publications

Concurrent-learning-based visual servo tracking and scene identification of mobile robots

Concurrent-learning-based visual servo tracking and scene identification of mobile robots

Homography-Based Sampled-Data Visual Servoing

Homography-Based Visual Servo Tracking Control of Wheeled Mobile Robots with Simultaneous Depth Identification

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