New approach to visual servo control using terminal constraints

Mehta, Siddhartha S.; Ton, Chau; Rysz, Maciej; Kan, Zhen; Doucette, Emily A.; Curtis, J. Willard

doi:10.1016/j.jfranklin.2019.04.026

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…It can be seen from equation ( 12) that the image Jacobian matrix based on image features in the form of pixel coordinates is shown in equation (13).…”

Section: Selection Of Image Features and Image Jacobian Matrixmentioning

confidence: 99%

“…8 At present, most of the research work on visual servoing based on feature points uses objects with simple geometric features and backgrounds as the research objects. [9][10][11][12][13][14] For instance, four black dots on a flat square part were used as image feature points, and the visual servoing of a 10-DOF articulated maintenance arm robot was simulated. 9 Four corners of a green rectangle were used as image feature points to verify the effectiveness of the IBVS control method based on the eye-in-hand monocular camera.…”

Section: Introductionmentioning

confidence: 99%

“…10 In order to improve the accuracy of image processing, four low-noise and the same feature shapes were carefully designed on A4 paper, which were used as image features for incremental visual servoing research. 11 There are also many literatures that selected the four corners of a black rectangular block, 12 four dots on a square object, 13 and four dots on A4 paper 14 as image features to carry out related IBVS research. The above studies focus on the influence of visual information and motion mapping or control law design on the control effect of the visual servoing system.…”

Section: Introductionmentioning

confidence: 99%

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Robotic grasping method of bolster spring based on image-based visual servoing with YOLOv3 object detection algorithm

Liu

Tao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this paper, to address the problem of automatic positioning and grasping of bolster spring with complex geometric features and cluttered background, a novel image-based visual servoing (IBVS) control method based on the corner points features of YOLOv3 object detection bounding box is proposed and applied to the robotic grasping system of bolster spring. The YOLOv3 object detection model is used to detect and position the bolster spring and then based on the corner points features of the bolster spring bounding box, the IBVS controller is designed to drive the end effector of the robot to the desired pose. This method adopts the approach-align-grasp control strategy to achieve the grasping of the bolster spring, which is very robust to the calibration parameter errors of the camera and the robot model. With the help of Robotics and Machine Vision Toolboxes in Matlab, IBVS simulation analysis based on feature points is carried out. The results show that it is reasonable to use the corner points of the object detection bounding box as image features under the initial pose where the image plane is parallel to the object plane. The positioning and grasping experiments are conducted on the robotic grasping platform of bolster spring. The results show that this method is effective for automatic positioning and grasping of bolster spring with complex geometric features and cluttered background, and it has strong robustness to the change of illumination.

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“…It can be seen from equation ( 12) that the image Jacobian matrix based on image features in the form of pixel coordinates is shown in equation (13).…”

Section: Selection Of Image Features and Image Jacobian Matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robotic grasping method of bolster spring based on image-based visual servoing with YOLOv3 object detection algorithm

Liu

Tao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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show abstract

“…Visual servo control, which fuses the concepts from several engineering fields ranging from control theory, real-time computing, image processing and kinematics, aims to enhance the control performance by utilizing the visual information acquired from camera (Hutchinson et al, 1996; Mehta et al, 2019). Versatility, non-invasive measurement and accuracy are the key factors that motivate visual feedback in control applications.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear stabilization and reference tracking of visual servo system using TS fuzzy augmented iterative learning control: Experimental validation

Jonnalagadda¹,

Elumalai

2023

Transactions of the Institute of Measurement and Control

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To address the nonlinear stabilization problem and improve the tracking control feature of ball on plate system (BPS), this paper puts forward a novel Takagi Sugeno (TS) fuzzy control augmented with the current cycle feedback iterative learning control (CCF-ILC) scheme. According to Bode’s sensitivity integral, the performance of linear controllers is always a trade-off between reference tracking and robustness. Hence, to deal with the so-called ‘waterbed’ effect, this work exploits the capability of TS fuzzy to handle the nonlinear dynamics and synthesizes a learning control scheme based on current iteration error to capitalize the information rich error signal for enhancing the robustness and trajectory tracking features. The global asymptotic stability of the proposed TS fuzzy augmented ILC scheme is proved using the Lyapunov function and linear matrix inequalities (LMIs). Moreover, the monotonic convergence of ILC is presented based on the singular value condition. For identifying the rolling mass from the video stream, a background subtraction algorithm based on thresholding technique is implemented. Finally, the robustness and tracking features of the proposed scheme are evaluated on a two degrees of freedom (DoF) laboratory scale BPS system through hardware in loop (HIL) testing for three realistic test cases. The tracking performance quantified using the root mean square error (RMSE) and power spectral density plot corroborates that the proposed scheme can offer better setpoint tracking and robustness feature compared to state-of-the-art fuzzy and ILC control techniques implemented on BPS.

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“…In the reference [12], a robust adaptive control method based on the dynamic structure of the fuzzy wavelet neural network is proposed, which is used to track the trajectory of the robot with uncertainties and disturbances by the adaptive sliding-mode control. Mehta and Ton et al introduced a continuous terminal sliding mode visual servo controller to adjust the camera to the desired position, but this has many requirements for the camera, and requires the correction of the camera position [13]. In the field of robotic citrus picking, there is a non-linear robust image visual servo controller that can adjust the end effector to the fruit position under the condition of unknown fruit movement, but the speed of the picking process is slow, and is not conducive to agricultural application [14].…”

Section: Introductionmentioning

confidence: 99%

Picking Robot Visual Servo Control Based on Modified Fuzzy Neural Network Sliding Mode Algorithms

Chen

Liu

et al. 2019

Electronics

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Through an analysis of the kinematics and dynamics relations between the target positioning of manipulator joint angles of an apple-picking robot, the sliding-mode control (SMC) method is introduced into robot servo control according to the characteristics of servo control. However, the biggest problem of the sliding-mode variable structure control is chattering, and the speed, inertia, acceleration, switching surface, and other factors are also considered when approaching the sliding die surface. Meanwhile, neural network has the characteristics of approaching non-linear function and not depending on the mechanism model of the system. Therefore, the fuzzy neural network control algorithm can effectively solve the chattering problem caused by the variable structure of the sliding mode and improve the dynamic and static performances of the control system. The comparison experiment is carried out through the application of the PID algorithm, the sliding mode control algorithm, and the improved fuzzy neural network sliding mode control algorithm on the picking robot system in the laboratory environment. The result verified that the intelligent algorithm can reduce the complexity of parameter adjustments and improve the control accuracy to a certain extent.

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New approach to visual servo control using terminal constraints

Cited by 9 publications

References 24 publications

Robotic grasping method of bolster spring based on image-based visual servoing with YOLOv3 object detection algorithm

Robotic grasping method of bolster spring based on image-based visual servoing with YOLOv3 object detection algorithm

Nonlinear stabilization and reference tracking of visual servo system using TS fuzzy augmented iterative learning control: Experimental validation

Picking Robot Visual Servo Control Based on Modified Fuzzy Neural Network Sliding Mode Algorithms

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