Estelle Courtial

Predictive Control for Constrained Image-Based Visual Servoing

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.

Posture Recognition Based on Fuzzy Logic for Home Monitoring of the Elderly

We propose in this paper a computer vision-based posture recognition method for home monitoring of the elderly. The proposed system performs human detection prior to the posture analysis; posture recognition is performed only on a human silhouette. The human detection approach has been designed to be robust to different environmental stimuli. Thus, posture is analyzed with simple and efficient features that are not designed to manage constraints related to the environment but only designed to describe human silhouettes. The posture recognition method, based on fuzzy logic, identifies four static postures and is robust to variation in the distance between the camera and the person, and to the persons morphology. With an accuracy of 74.29% of satisfactory posture recognition, this approach can detect emergency situations such as a fall within a health smart home.

Image Based Visual Servoing through Nonlinear Model Predictive Control

Image based visual servoing (IBVS) is a vision sensor based control architecture. In classical approach, an image Jacobian matrix maps image space errors into errors in Cartesian space. Then a simple proportional control law can be applied guaranteeing local convergence to a desired set point. One of the main advantage of IBVS is its robustness w.r.t camera and robot calibration errors and image measurement errors. Nevertheless, this scheme can not deal with nonlinear constraint such as joint limits and actuator saturation. Visibility constraint is not ensured with classical IBVS. A new IBVS scheme based on nonlinear model predictive control (NMPC) is proposed considering the direct dynamic model of the robot, its joint and torque limits, the camera projection model and the visibility constraint. Simulations exhibit the efficiency and the robustness of the proposed solution to control a 6 degrees of freedom mechanical system

Real-time visual predictive controller for image-based trajectory tracking of a mobile robot

Abstract This paper deals with the design of a real-time controller for trajectory tracking in the image plane. The Image-Based Visual Servoing (IBVS) task is addressed by a visual predictive approach. The trajectory tracking is formulated into a nonlinear optimization problem in the image plane. The unavoidable constraints in experiments are easily taken into account in the design of the predictive control law. The global model, combining the mobile robot and camera model, is used to predict the behavior of the process. The flatness property of this global model is proved in the general case, that is whatever the camera posture. The flat model permits to reduce the computational time by a factor 2. Experiments are performed on a non holonomic mobile robot with a deported perspective camera. Experimental results show the efficiency and the robustness of the real-time control approach. Visibility constraints are added to point out the capability of the control to avoid obstacles.

Visual predictive control for manipulators with catadioptric camera

This paper deals with image based visual servoing (IBSV) by a visual predictive control (VPC) approach. Based on nonlinear model predictive control (NMPC), the visual servoing problem is formulated into a nonlinear constrained minimization problem in the image plane. A global model describing the behavior of the robotic system equipped with the camera is used to predict the evolution of the visual feature on a future horizon. The main interest of this method is the capability to easily take into account different constraints like mechanical limitations and/or visibility constraints. Simulation experiments are performed on a planar manipulator with an omnidirectional camera. Comparisons with the classical control law based on the interaction matrix highlight the efficiency and the robustness of the proposed approach, especially in difficult initial configurations and large displacements.

Visual Servoing via Nonlinear Predictive Control

In this chapter, image-based visual servoing is addressed via nonlinear model predictive control. The visual servoing task is formulated into a nonlinear optimization problem in the image plane. The proposed approach, named visual predictive control, can easily and explicitly take into account 2D and 3D constraints. Furthermore, the image prediction over a finite prediction horizon plays a crucial role for large displacements. This image prediction is obtained thanks to a model. The choice of this model is discussed. A nonlinear global model and a local model based on the interaction matrix are considered. Advantages and drawbacks of both models are pointed out. Finally, simulations obtained with a 6 degrees of freedom (DOF) free-flying camera highlight the capabilities and the efficiency of the proposed approach by a comparison with the classical image-based visual servoing.

Multivariable Boundary Control Approach by Internal Model, applied to Irrigation Canals Regulation

Abstract This paper deals with the regulation problem of a class of irrigation canals using the Saint-Venant partial differential equations (pde). The Internal Model Boundary Control (IMBC) approach is used and the multireaches case is considered (several reaches in cascade). Perturbation theory of exponential semigroup used for control synthesis is extended here to nonhomogeneous hyperbolic systems, as the multireaches regulation model is described by hyperbolic pdes. Experimental results (on the Valence experimental canal) are encouraging for an extension to the real case. Additionally a multi-model approach was introduced to allow wider water level variations.

Estimation of the Blood Velocity for Nanorobotics

This paper aims to estimate the blood velocity to enhance the navigation of an aggregate in the human vasculature. The considered system is a polymer-binded aggregate of ferromagnetic nanorobots immersed in a blood vessel. The drag force depends on the blood velocity and especially acts on the aggregate dynamics. In the design of advanced control laws, the blood velocity is usually assumed to be known or set to a constant mean value to achieve the control objectives. We provide theoretical tools to online estimate the blood velocity from the sole measurement of the aggregate position and combine the state estimator with a backstepping control law. Two state estimation approaches are addressed and compared through simulations: a high-gain observer and a receding horizon estimator. Simulations illustrate the efficiency of the proposed approach combining online estimation and control for the navigation of an aggregate of nanorobots.

Tele-monitoring and diagnostic for fall prevention: The KINOPTIM concept

Falls among the elderly is a major public health problem due to its causality for increased dependency. It can be tackled thanks to innovative Information and Communication Technologies (ICT) along with new forms of service delivery organization such as home care. In the context of a Marie Curie project we have proposed the KINOPTIM concept, a comprehensive fall prevention and holistic management service. This paper provides a review of this innovative concept starting with a brief introduction and the rationale behind its development, the description of the platform and service to be eventually piloted and closing with conclusions and perspectives section since it is still in an ongoing research phase.

Switching controller for efficient IBVS

Image-based visual servoing (IBVS) is now recognized to be an efficient and robust control strategy to guide robots using only visual data. Classical IBVS is commonly based on the Cartesian coordinates of points in the image. Although the convergence of the visual features to the desired ones is generally achieved, classical IBVS can lead to unnecessary displacements of the camera, such as the camera retreat problem in the case of a pure rotation around the optical axis. In contrast, IBVS based on the polar coordinates of points, is well adapted to carry out rotations around the optical axis but less adapted to manage translations. To take advantage of the benefits of each approach, we propose a new strategy for visual servoing based on a switching controller. The controller switches between Cartesian-based and polar-based approaches thanks to a switching signal provided by a decision maker. With the proposed controller, unnecessary 3D displacements are minimized without any 3D reconstruction and visibility constraints can be taken into account. The local stability of the closed-loop switching system is proved. A comparison with classical controllers and advanced controllers is performed by simulations. This comparative study illustrates the effectiveness of the proposed controller in terms of displacement in the image space and in the 3D space.