Visual servoing with linearized observer

One of the most important problems in feature-based visual servoing is the slow sampling rate and the delay of the camera that can make the closed loop system oscillative or unstable easily. In the paper, a linearized observer that estimates the object velocity and updates the visual information with the joint sampling rate is proposed. Stability of the observer-based control system and effectiveness of the observer are verified by experiments on a PUMA 560 robot</p

Flexible Force-Vision Control for Surface Following using Multiple Cameras

A flexible method for six-degree-of-freedom combined vision/force control for interaction with a stiff uncalibrated environment is presented. An edge-based rigidbody tracker is used in an observer-based controller, and combined with a six-degree-of-freedom force- or impedance controller. The effect of error sources such as image space measurement noise and calibration errors are considered. Finally, the method is validated in simulations and a surface following experiment using an industrial robot

Robust Image-Based Visual Servo Control of an Uncertain Missile Airframe

A nonlinear vision-based guidance law is presented for a missile-target scenario in the presence of model uncertainty and unknown target evasive maneuvers. To ease the readability of this thesis, detailed explanations of any relevant mathematical tools are provided, including stability definitions, the procedure of Lyapunov-based stability analysis, sliding mode control fundamentals, basics on visual servo control, and other basic nonlinear control tools. To develop the vision-based guidance law, projective geometric relationships are utilized to combine the image kinematics with the missile dynamics in an integrated visual dynamic system. The guidance law is designed using an image-based visual servo control method in conjunction with a sliding-mode control strategy, which is shown to achieve asymptotic target interception in the presence of the aforementioned uncertainties. A Lyapunov-based stability analysis is presented to prove the theoretical result, and numerical simulation results are provided to demonstrate the performance of the proposed robust controller for both stationary and non-stationary targets

Homography-based pose estimation to guide a miniature helicopter during 3D-trajectory tracking

This work proposes a pose-based visual servoing control, through using planar homography, to estimate the position and orientation of a miniature helicopter relative to a known pattern. Once having the current flight information, the nonlinear underactuated controller presented in one of our previous works, which attends all flight phases, is used to guide the rotorcraft during a 3Dtrajectory tracking task. In the sequel, the simulation framework and the results obtained using it are presented and discussed, validating the proposed controller when a visual system is used to determine the helicopter pose information.Fil: Brandão, Alexandre . Universidade Federal Do Espirito Santo. Centro Tecnologico. Departamento de Ingenieria Electrica; BrasilFil: Sarapura, Jorge Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina. Universidad Nacional de San Juan; ArgentinaFil: Sarcinelli Filho, Mario . Universidade Federal Do Espirito Santo. Centro Tecnologico. Departamento de Ingenieria Electrica; BrasilFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina. Universidad Nacional de San Juan; Argentin

Fluid Flows Control using Visual Servoing

International audienceThis paper introduces for the first time the control of a fluid flow using visual servoing. The control consists to regulate a flow around a desired velocities profile. The originality here is the possibility to use optical flow measurements, computed from the observed flow, in a visual servo control scheme. Compared to existing approaches that uses a limited set of measurements to control a fluid flow, our approach is shown to be a major improvement in terms of the flow state estimation and control

Unfalsified visual servoing for simultaneous object recognition and pose tracking

In a complex environment, simultaneous object recognition and tracking has been one of the challenging topics in computer vision and robotics. Current approaches are usually fragile due to spurious feature matching and local convergence for pose determination. Once a failure happens, these approaches lack a mechanism to recover automatically. In this paper, data-driven unfalsified control is proposed for solving this problem in visual servoing. It recognizes a target through matching image features with a 3-D model and then tracks them through dynamic visual servoing. The features can be falsified or unfalsified by a supervisory mechanism according to their tracking performance. Supervisory visual servoing is repeated until a consensus between the model and the selected features is reached, so that model recognition and object tracking are accomplished. Experiments show the effectiveness and robustness of the proposed algorithm to deal with matching and tracking failures caused by various disturbances, such as fast motion, occlusions, and illumination variation