In this letter, a constrained visual predictive control strategy (C-VPC) is developed for a robotic flexible endoscope to precisely track target features in narrow environments while adhering to visibility and joint l...
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In this letter, a constrained visual predictive control strategy (C-VPC) is developed for a robotic flexible endoscope to precisely track target features in narrow environments while adhering to visibility and joint limit constraints. The visibility constraint, crucial for keeping the target feature within the camera's field of view, is explicitly designed using zeroing control barrier functions to uphold the forward invariance of a visible set. To automate the robotic endoscope, kinematic modeling for image-based visual servoing is conducted, resulting in a state-space model that facilitates the prediction of the future evolution of the endoscopic state. The C-VPC method calculates the optimal control input by optimizing the model-based predictions of the future state under visibility and joint limit constraints. Both simulation and experimental results demonstrate the effectiveness of the proposed method in achieving autonomous target tracking and addressing the visibility constraint simultaneously. The proposed method achieved a reduction of 12.3% in Mean Absolute Error (MAE) and 56.0% in variance (VA) compared to classic IBVS.
This paper proposes a visual predictive control solution adapted to mobile manipulators and able to cope with several issues related to visibility, manipulability, and stability. To address these problems, the propose...
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This paper proposes a visual predictive control solution adapted to mobile manipulators and able to cope with several issues related to visibility, manipulability, and stability. To address these problems, the proposed strategy relies on (i) the use of two complementary cameras, (ii) the definition of a cost function depending on both the vision-based task and the manipulability, (iii) the integration of time-varying constraints allowing to prioritize the former against the latter. The strategy has been analyzed through simulation using ROS and Gazebo and implemented on our TIAGo robot. The obtained results fully validate the proposed approach.
in order to increase the flexibility and intelligence of robotics visualcontrol system, Broyden estimation of composite Jacobian is combined with nonlinear model predictivecontrol in robotics visual servoing. This m...
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ISBN:
(纸本)9781509018970
in order to increase the flexibility and intelligence of robotics visualcontrol system, Broyden estimation of composite Jacobian is combined with nonlinear model predictivecontrol in robotics visual servoing. This method does not require the knowledge of robot model, camera model and object model, while visibility constraints, limitations of joint angles and joint velocities are all considered. Robotics control is achieved by solving the constrained optimization problem over a predictive horizon at each step, corresponding predictive model is designed with estimated composite Jacobian. Finally, simulation results are provided to demonstrate the validity of proposed method.
In the past two decades, Unmanned Aerial Vehicles (UAVs) have gained attention in applications such as industrial inspection, search and rescue, mapping, and environment monitoring. However, the autonomous navigation ...
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In the past two decades, Unmanned Aerial Vehicles (UAVs) have gained attention in applications such as industrial inspection, search and rescue, mapping, and environment monitoring. However, the autonomous navigation capability of UAVs is aggravated in GPS-deprived areas such as indoors. As a result, vision-based control and guidance methods are sought. In this paper, a vision-based target-tracking problem is formulated in the form of a cascaded adaptive nonlinear Model predictivecontrol (MPC) strategy. The proposed algorithm takes the kinematics/dynamics of the system, as well as physical and image constraints into consideration. An Extended Kalman Filter (EKF) is designed to estimate uncertain and/or time-varying parameters of the model. The control space is first divided into low and high levels, and then, they are parameterised via orthonormal basis network functions, which makes the optimisation- based control scheme computationally less expensive, therefore suitable for real-time implementation. A 2-DoF model helicopter, with a coupled nonlinear pitch/yaw dynamics, equipped with a front-looking monocular camera, was utilised for hypothesis testing and evaluation via experiments. Simulated and experimental results show that the proposed method allows the model helicopter to servo toward the target efficiently in real-time while taking kinematic and dynamic constraints into account. The simulation and experimental results are in good agreement and promising.
Model predictivecontrol (MPC) is a very attractive control algorithm used to solve the complex problems of image-based visual servoing (IBVS) systems. Many image-based predictivecontrollers were reported, each being...
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ISBN:
(纸本)9783319212906;9783319212890
Model predictivecontrol (MPC) is a very attractive control algorithm used to solve the complex problems of image-based visual servoing (IBVS) systems. Many image-based predictivecontrollers were reported, each being different regarding the implementing of the MPC concept. In this paper, we present a MPC framework for IBVS applications, the main contributions being a new visual predictor and the introduction of the reference trajectory.
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