Model predictive control to improve visual control of motion: applications in active tracking of moving targets

Barreto, João P.; Batista, Jorge; Araújo, Hélder

doi:10.1109/icpr.2000.903021

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…In literature there are presented approaches of advanced visual control that use a dynamic matrix controller [2], a multivariable ARIMAX (which allows a GPC controller implementation) [3] or an H ∞ scenario [4]. These approaches don't use information only from the image plane for robot control but mix them with camera pose information, thus having a more position based set of methods.…”

Section: Introductionmentioning

confidence: 98%

Visual predictive control architecture based on image moments for manipulator robots

Copot

Burlacu

Lazăr

2011

2011 IEEE International Symposium on Industrial Electronics

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Increasing performances of nonlinear predictive control techniques for visual servoing systems, by using image moments as visual features, is the main goal of this paper. An local model based predictor is developed and the cost function is constructed using a image moments based reference trajectory. A new type of visual predictive control architecture is designed and a simulator is developed. Simulation results for a 6 d.o.f eye-in-hand scheme are revealed.

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Section: Introductionmentioning

confidence: 98%

Visual predictive control architecture based on image moments for manipulator robots

Copot

Burlacu

Lazăr

2011

2011 IEEE International Symposium on Industrial Electronics

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“…The predictive controller is used in [13] and [14] for motion compensation in target tracking, in addition to a classical IBVS controller. In [4], a linear DMC controller is used to cope up with the process delay in vision-based control. In [22], the predictive controller is used from ultrasound images for a medical application.…”

Section: Introductionmentioning

confidence: 99%

“…The problem is also known as extrinsic camera-calibration problem with its solution playing a crucial rule in the success of many computer-vision applications, such as object recognition [1], intelligent surveillance [2], and robotic visual servoing (RVS) [3]. Estimation of the camera displacement (CD) between the current and desired pose for RVS [4], [5] is also relevant to this problem. However, the focus of this study will be on pose estimation for RVS where the relative pose between a camera and an object is used for real-time control of a robot motion [3].…”

Section: Introductionmentioning

confidence: 99%

Predictive Control for Constrained Image-Based Visual Servoing

2010

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Abstract-This paper deals with the image-based visual servoing (IBVS), subject to constraints. Robot workspace limitations, visibility constraints, and actuators limitations are addressed. These constraints are formulated into state, output, and input constraints, respectively. Based on the predictive-control strategy, the IBVS task is written into a nonlinear optimization problem in the image plane, where the constraints can be easily and explicitly taken into account. Second, the contribution of the image prediction and influence of the prediction horizon are pointed out. The image prediction is obtained due to a model. The latter can be a local model based on the interaction matrix or a nonlinear global model based on 3-D data. Its choice is discussed with respect to the constraints to be handled. Finally, simulations that were obtained with a 6-degree-of-freedom (DOF) free-flying camera highlight the potential advantages of the proposed approach with respect to the image prediction and the constraint handling.

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“…Applications include classical industrial automation where a robot arm grasps parts from a conveyor belt [1] [2], the navigation of a mobile robot incorporating an active camera on a robot [3][4] to orient itself on characteristic natural landmarks, observing people [5] and steering a car [6]. A technique needed for these applications is visual servoing, which has the task of controlling a mechanism according to information gathered from a visual sensor [7] [8].…”

Section: Introductionmentioning

confidence: 99%

Uncalibrated Direct Visual Servoing Based on State Estimation

Xie

2006

2006 International Conference on Mechatronics and Automation

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In order to cope with the low sampling rate of standard cameras and the time delay introduced by image processing, a predictor is constructed to estimate future positions of a moving target using BP neural network. The controller presented adopts adaptive PD control algorithm with uncertain gravity compensation and computes the demand torques for the robot based on current joint angles and joint angle rates. The direct visual servoing system is proved to be asymptotic stable with Lyapunov stability theory. The system does not require a robot kinematics model or a calibrated camera model. Simulation results show that this method provides a good steadystate tracking behaviour and keeps good robustness and adaptability at the same time. Additionally, the algorithm is very easy to be implemented with low computational complexity.Index Terms -Direct visual servoing. BP neural network. State estimation. Gravity compensation. Uncalibrated eye-inhand visual servoing.

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Model predictive control to improve visual control of motion: applications in active tracking of moving targets

Abstract: Abstract

Cited by 12 publications

References 8 publications

Visual predictive control architecture based on image moments for manipulator robots

Visual predictive control architecture based on image moments for manipulator robots

Predictive Control for Constrained Image-Based Visual Servoing

Uncalibrated Direct Visual Servoing Based on State Estimation

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