Integrating Features Acceleration in Visual Predictive Control

Fusco, Franco; Kermorgant, Olivier; Martinet, Philippe

doi:10.1109/lra.2020.3004793

Cited by 15 publications

(7 citation statements)

References 24 publications

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“…First, the dynamic criterion is not mentioned in these specifications. In fact, for the visual servoing, high-speed motion is not the purpose, except for a few specific scenarios [ 32 , 33 ]. Therefore, only the geometry and kinematics performance of the robot will be considered.…”

Section: Robot Architecture and Specificationmentioning

confidence: 99%

Design of a Gough–Stewart Platform Based on Visual Servoing Controller

Zhu

Huang

Song

et al. 2022

Sensors

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Designing a robot with the best accuracy is always an attractive research direction in the robotics community. In order to create a Gough–Stewart platform with guaranteed accuracy performance for a dedicated controller, this paper describes a novel advanced optimal design methodology: control-based design methodology. This advanced optimal design method considers the controller positioning accuracy in the design process for getting the optimal geometric parameters of the robot. In this paper, three types of visual servoing controllers are applied to control the motions of the Gough–Stewart platform: leg-direction-based visual servoing, line-based visual servoing, and image moment visual servoing. Depending on these controllers, the positioning error models considering the camera observation error together with the controller singularities are analyzed. In the next step, the optimization problems are formulated in order to get the optimal geometric parameters of the robot and the placement of the camera for the Gough–Stewart platform for each type of controller. Then, we perform co-simulations on the three optimized Gough–Stewart platforms in order to test the positioning accuracy and the robustness with respect to the manufacturing errors. It turns out that the optimal control-based design methodology helps get both the optimum design parameters of the robot and the performance of the controller {robot + dedicated controller}.

show abstract

Section: Robot Architecture and Specificationmentioning

confidence: 99%

Design of a Gough–Stewart Platform Based on Visual Servoing Controller

Zhu

Huang

Song

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Sequentially, it can be quite difficult to be applied to the real system. In order to alleviate and tackle this problem, various methods have been developed to guarantee a real-time solving optimization problem such as in [37]- [39]. In [37], the authors utilized the primal-dual neural network (PDNN) as a promising computational tool for solving the quadratic programming (QP) problem and achieving a real-time implementation of MPC, instead of using the sequential quadratic programming (SQP) method which requires repeatedly a calculation of Hessian matrix to solve a QP problem [38].…”

Section: A Related Workmentioning

confidence: 99%

“…In [37], the authors utilized the primal-dual neural network (PDNN) as a promising computational tool for solving the quadratic programming (QP) problem and achieving a real-time implementation of MPC, instead of using the sequential quadratic programming (SQP) method which requires repeatedly a calculation of Hessian matrix to solve a QP problem [38]. Fusco et al [39] presented a real-time IBVS prediction control scheme for a robotic arm based on integrating the acceleration of the visual features, which allows the controller to produce better input signals compared to classical predictive strategies.…”

Section: A Related Workmentioning

confidence: 99%

MPPI-VS: Sampling-Based Model Predictive Control Strategy for Constrained Image-Based and Position-Based Visual Servoing

Mohamed

2021

Preprint

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In this paper, we open up new avenues for visual servoing systems built upon the Path Integral (PI) optimal control theory, in which the non-linear partial differential equation (PDE) can be transformed into an expectation over all possible trajectories using the Feynman-Kac (FK) lemma. More precisely, we propose an MPPI-VS control strategy, a real-time and inversion-free control strategy on the basis of samplingbased model predictive control (namely, Model Predictive Path Integral (MPPI) control) algorithm, for both image-based, 3D point, and position-based visual servoing techniques, taking into account the system constraints (such as visibility, 3D, and control constraints) and parametric uncertainties associated with the robot and camera models as well as measurement noise. Contrary to classical visual servoing control schemes, our control strategy directly utilizes the approximation of the interaction matrix, without the need for estimating the interaction matrix inversion or performing the pseudo-inversion. We validate the MPPI-VS control strategy as well as the classical control schemes on a 6-DoF Cartesian robot with an eye-in-hand camera based on the utilization of four points in the image plane as visual features.To better assess and demonstrate the robustness and potential advantages of our proposed control strategy compared to classical schemes, intensive simulations under various operating conditions are carried out and then discussed. The obtained results demonstrate the effectiveness and capability of the proposed scheme in coping easily with the system constraints, as well as its robustness in the presence of large errors in camera parameters and measurements.

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“…First, the dynamic criterion is not mentioned in these specifications. Actually, for the visual servoing, high-speed motion is not the purpose, except for a few specific scenarii [25,26]. Therefore, only the geometry and kinematics performance of the robot will be considered.…”

Section: Robot Architecture and Specificationmentioning

confidence: 99%

Advanced Optimal Control-Based Design of a Gough-Stewart Platform

Zhu

Song

Gong

2021

Preprint

View full text Add to dashboard Cite

Designing a robot with the best accuracy is always the attractive research direction in robot community. In order to create a Gough-Stewart platform with guaranteed accuracy performance for a dedicated controller, this paper describes a novel advanced optimal design methodology: control-based design methodology. This advanced optimal design method considers the controller positioning accuracy in the design process for getting the optimal geometric parameters of the robot. In this paper, three types of visual servoing controllers are applied to control the motions of the Gough-Stewart platform: leg-direction-based visual servoing, line-based visual servoing and image moment visual servoing. Depend on these controllers, the positioning error models considering the camera observation error together with the controller singularities are analyzed. In the next step, the optimization problems are formulated in order to get the optimal geometric parameters of the robot and the placement of the camera for the Gough-Stewart platform for each type of controller. Then, we perform the co-simulations on the three optimized Gough-Stewart platforms in order to test the positioning accuracy and the robustness with respect to the manufacturing errors. It turns out that the optimal control-based design methodology helps getting both the optimum design parameters of the robot and the performance of the controller {robot + dedicated controller}.

show abstract

Integrating Features Acceleration in Visual Predictive Control

Cited by 15 publications

References 24 publications

Design of a Gough–Stewart Platform Based on Visual Servoing Controller

Design of a Gough–Stewart Platform Based on Visual Servoing Controller

MPPI-VS: Sampling-Based Model Predictive Control Strategy for Constrained Image-Based and Position-Based Visual Servoing

Advanced Optimal Control-Based Design of a Gough-Stewart Platform

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