Alignment control using visual servoing and mobilenet single-shot multi-box detection (SSD): a review

The concept is highly critical for robotic technologies that rely on visual feedback. In this context, robot systems tend to be unresponsive due to reliance on pre-programmed trajectory and path, meaning the occurrence of a change in the environment or the absence of an object. This review paper aims to provide comprehensive studies on the recent application of visual servoing and DNN. PBVS and Mobilenet-SSD were chosen algorithms for alignment control of the film handler mechanism of the portable x-ray system. It also discussed the theoretical framework features extraction and description, visual servoing, and Mobilenet-SSD. Likewise, the latest applications of visual servoing and DNN was summarized, including the comparison of Mobilenet-SSD with other sophisticated models. As a result of a previous study presented, visual servoing and MobileNet-SSD provide reliable tools and models for manipulating robotics systems, including where occlusion is present. Furthermore, effective alignment control relies significantly on visual servoing and deep neural reliability, shaped by different parameters such as the type of visual servoing, feature extraction and description, and DNNs used to construct a robust state estimator. Therefore, visual servoing and MobileNet-SSD are parameterized concepts that require enhanced optimization to achieve a specific purpose with distinct tools

Image-based Visual Servoing of a Gough-Stewart Parallel Manipulator using Leg Observations

International audienceIn this paper, a tight coupling between computer vision and paral- lel robotics is exhibited through the projective line geometry. Indeed, contrary to the usual methodology where the robot is modeled indepen- dently from the control law which will be implemented, we take into ac- count, since the early modeling stage, that vision will be used for con- trol. Hence, kinematic modeling and projective geometry are fused into a control-devoted projective kinematic model. Thus, a novel vision-based kinematic modeling of a Gough-Stewart manipulator is proposed through the image projection of its cylindrical legs. Using this model, a visual ser- voing scheme is presented, where the image projection of the non-rigidly linked legs are servoed, rather than the end-effector pose

Vision-based control of a knuckle boom crane with online cable length estimation

A vision-based controller for a knuckle boom crane is presented. The controller is used to control the motion of the crane tip and at the same time compensate for payload oscillations. The oscillations of the payload are measured with three cameras that are fixed to the crane king and are used to track two spherical markers fixed to the payload cable. Based on color and size information, each camera identifies the image points corresponding to the markers. The payload angles are then determined using linear triangulation of the image points. An extended Kalman filter is used for estimation of payload angles and angular velocity. The length of the payload cable is also estimated using a least squares technique with projection. The crane is controlled by a linear cascade controller where the inner control loop is designed to damp out the pendulum oscillation, and the crane tip is controlled by the outer loop. The control variable of the controller is the commanded crane tip acceleration, which is converted to a velocity command using a velocity loop. The performance of the control system is studied experimentally using a scaled laboratory version of a knuckle boom crane

Image Based Visual Servoing: Estimated Image Jacobian by Using Fundamental Matrix VS Analytic Jacobian

This paper describes a comparative study of performance between the estimated image Jacobian that come from taking into account the geometry epipolar of a system of two cameras, and the well known analytic image Jacobian that is utilized for most applications in visual servoing. Image Based Visual Servoing architecture is used for controlling a 3 d.o.f. articular system using two cameras in eye to hand configuration. Tests in static and dynamic cases were carried out, and showed that the performance of estimated Jacobian by using the properties of the epipolar geometry is such as good and robust against noise as the analytic Jacobian. This fact is considered as an advantage because the estimated Jacobian does not need laborious previous work prior the control task in contrast to the analytic Jacobian does

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