Visual Servo Control Method Research Articles

Feature-based visual servo control preserving wide-angle properties of super wide-angle lens

Feature-based visual servo control methods determine camera motion by comparing a current feature image and the goal feature image. Because these methods do not require 3D information for the object, no geometric model of the object is needed, and it is therefore robust against various disturbances. Several feature-based visual servo control methods have been proposed, such as using a wide-angle lens allows the camera to capture the object in a wide angular range. However, when using a wide-angle lens with a conventional control scheme the camera undergoes wasted motion due to the peripheral distortion of wide-angle lenses. In addition, correction of the image distortion results in the loss of the wide-angle properties of the lens. This paper proposes a new feature-based visual servo control method based on a super wide-angle lens that preserves wide-angle properties.

Autonomous Rendezvous and Docking with Nonfull Field of View for Tethered Space Robot

In the ultra-close approaching phase of tethered space robot, a highly stable self-attitude control is essential. However, due to the field of view limitation of cameras, typical point features are difficult to extract, where commonly adopted position-based visual servoing cannot be valid anymore. To provide robot’s relative position and attitude with the target, we propose a monocular visual servoing control method using only the edge lines of satellite brackets. Firstly, real time detection of edge lines is achieved based on image gradient and region growing. Then, we build an edge line based model to estimate the relative position and attitude between the robot and the target. Finally, we design a visual servoing controller combined with PD controller. Experimental results demonstrate that our algorithm can extract edge lines stably and adjust the robot’s attitude to satisfy the grasping requirements.

CLFs-based optimization control for a class of constrained visual servoing systems

In this paper, we use the control Lyapunov function (CLF) technique to present an optimized visual servo control method for constrained eye-in-hand robot visual servoing systems. With the knowledge of camera intrinsic parameters and depth of target changes, visual servo control laws (i.e. translation speed) with adjustable parameters are derived by image point features and some known CLF of the visual servoing system. The Fibonacci method is employed to online compute the optimal value of those adjustable parameters, which yields an optimized control law to satisfy constraints of the visual servoing system. The Lyapunov's theorem and the properties of CLF are used to establish stability of the constrained visual servoing system in the closed-loop with the optimized control law. One merit of the presented method is that there is no requirement of online calculating the pseudo-inverse of the image Jacobian's matrix and the homography matrix. Simulation and experimental results illustrated the effectiveness of the method proposed here.

A Hybrid Switched Reactive-Based Visual Servo Control of 5-DOF Robot Manipulators for Pick-and-Place Tasks

This paper addresses the problem of visual servo control for a five-degree-of-freedom robot manipulator to perform pick-and-place tasks. A new hybrid switched reactive-based visual servo control structure, inspired from a sensing-and-reaction behavior without the inverse interaction matrix computing, is proposed to handle this problem efficiently. The proposed structure is similar to the logic-based approaches, but the requirements of the fuzzy modeling and/or the fuzzy-rule base learning are omitted. To achieve this, a novel hardware-aware reactive function approach is presented to directly map an image position error vector to a desired end-effector velocity vector under the consideration of hardware limitations. This approach helps to simplify the implementation of visual servo systems with improved reliability. Moreover, the proposed control system is a hybrid switching controller consisting of both image-based and position-based reactive planning schemes, which allows improving the robustness and effectiveness of the visual servo system. Experimental results validate the performance of the proposed visual servo control method in a realistic scenario setting.

Planar Path Following of 3-D Steering Scaled-Up Helical Microswimmers

Helical microswimmers that are capable of propulsion at low Reynolds numbers have great potential for numerous applications. Several kinds of artificial magnetic-actuated helical microswimmers have been designed by researchers. However, they are primarily open-loop controlled. This paper aims to investigate methods of closed-loop control of a magnetic-actuated helical swimmer at low Reynolds number by using visual feedback. For many in-vitro applications, helical swimmers should pass through a defined path, for example along channels with no prerequisite on the velocity profile along the path. Therefore, the main objective of this paper is to achieve a velocity-independent planar path following task. Since the planar path following is based on 3-D steering control of the helical swimmer, a 3-D pose estimation of a helical swimmer is introduced based on the real-time visual tracking with a stereo vision system. The contribution of this paper is in two parts: The 3-D steering of a helical swimmer is demonstrated by visual servo control; and the path following of a straight line with visual servo control is achieved, then compared with open-loop control. We further expect that with this visual servo control method, the helical swimmers will be able to follow reference paths at the microscale.

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

A nonlinear vision-based guidance law is presented in this paper for a missile-target scenario in the presence of model uncertainty and unknown target evasive maneuvers. To this end, 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.

Visual attention servo control for task-specific robotic applications

This paper proposes a visual attention servo control (VASC) method which uses the Gaussian mixture model (GMM) for task-specific applications of mobile robots. In particular, low dimensional bias feature template is obtained using GMM to get an efficient attention process. An image-based visual servo (IBVS) controller is used to search for a desired object in a scene through an attention system which forms a task-specific state representation of the environment. First, task definition and object representation in semantic memory (SM) are proposed, and bias feature template is obtained using GMM deduction for features from high dimension to low dimension. Second, the features intensity, color, size and orientation are extracted to build the feature set. Mean shift method is used to segment the visual scene into discrete proto-objects. Given a task-specific object, top-down bias attention is evaluated to generate the saliency map by combining with the bottom-up saliency-based attention. Third, a visual attention servo controller is developed to integrate the IBVS controller and the attention system for robotic cognitive control. A rule-based arbitrator is proposed to switch between the episodic memory (EM)-based controller and the IBVS controller depending on whether the robot obtains the desired attention point on the image. Finally, the proposed method is evaluated on task-specific object detection under different conditions and visual attention servo tasks. The obtained results validate the applicability and usefulness of the developed method for robotics.

Automatic Button Cell Sorting Manipulator System Research Based on Visual Servo Control

Automatic machine vision-based manipulator used for button cell sorting has high academic value. Firstly, a detailed analysis of the visual system, using an image-based visual servo control method, is provided, so as to establish a set of button batteries automatic sorting manipulator platform. the detail composition of this platform is introduced in this paper. Through analysis of PID control method, a mathematical model is established, and its motor control is simulated.Experimental results show that, the platform of an automatic manipulator system has char high accuracy and higher real-time characteristic.

Uncalibrated Visual Servoing through the Efficient Estimation of the Image Jacobian for Large Residual

An uncalibrated visual servo control method for tracking a target is presented. We define the robot-positioning problem as an unconstrained optimization problem to minimize the image error between the target feature and the robot end-effector feature. We propose a method to find the residual term for more precise modeling using the secant approximation method. The composite image Jacobian is estimated by the proper method for eye-to-hand configuration without knowledge of the kinematic structure, imaging geometry and intrinsic parameter of camera. This method is independent of the motion of a target feature. The algorithm for regulation of the joint velocity for safety and stability is presented using the cost function. Adaptive regulation for visibility constraints is proposed using the adaptive parameter.

Image Based and Hybrid Visual Servo Control of an Unmanned Aerial Vehicle

The use of unmanned aerial vehicles (UAVs) for military, scientific, and civilian sectors are increasing drastically in recent years. This study presents algorithms for the visual-servo control of an UAV, in which a quadrotor helicopter has been stabilized with visual information through the control loop. Unlike previous study that use pose estimation approach which is time consuming and subject to various errors, the visual-servo control is more reliable and fast. The method requires a camera on-board the vehicle, which is already available on various UAV systems. The UAV with a camera behaves like an eye-in-hand visual servoing system. In this study the controller was designed by using two different approaches; image based visual servo control method and hybrid visual servo control method. Various simulations are developed on Matlab, in which the quadrotor aerial vehicle has been visual-servo controlled. In order to show the effectiveness of the algorithms, experiments were performed on a model quadrotor UAV, which suggest successful performance.

마커인식과 혼합 비주얼 서보잉 기법을 통한 이동로봇의 자세 안정화 제어

This paper proposes a posture stabilization control algorithm for a wheeled mobile robot using hybrid visual servo control method with a position based and an image based visual servoing (PBVS and IBVS). To overcome chattering phenomena which were shown in the previous researches using a simple switching function based on a threshold, the proposed hybrid visual servo control law introduces the fusion function based on a blending function. Then, the chattering problem and rapid motion of the mobile robot can be eliminated. Also, we consider the nonlinearity of the wheeled mobile robot unlike the previous visual servo control laws using linear control methods to improve the performances of the visual servo control law. The proposed posture stabilization control law using hybrid visual servoing is verified by a theoretical analysis and simulation and experimental results.

혼합 비주얼 서보 제어 기법을 이용한 이동로봇의 목표물 추종

This paper proposes a target tracking algorithm for wheeled mobile robots using in various fields. For the stable tracking, we apply a vision system to a mobile robot which can extract targets through image processing algorithms. Furthermore, this paper presents an algorithm to position the mobile robot at the desired location from the target by estimating its relative position and attitude. We show the problem in the tracking method using the Position-Based Visual Servo(PBVS) control, and propose a tracking method, which can achieve the stable tracking performance by combining the PBVS control with Image-Based Visual Servo(IBVS) control. When the target is located around the outskirt of the camera image, the target can disappear from the field of view. Thus the proposed algorithm combines the control inputs with of the hyperbolic form the switching function to solve this problem. Through both simulations and experiments for the mobile robot we have confirmed that the proposed visual servo control method is able to enhance the stability compared to of the method using only either PBVS or IBVS control method.

Binocular tracking with ultrasonic sensors

This paper proposes an Image-Based Visual Servo control (IBVS) method that integrates the kinematics of both a robotic binocular head and cameras with the dynamics of the binocular head and uses a fuzzy sliding mode control algorithm. The proposed method utilises ultrasonic sensors to supply distance information to the visual servo system. In image processing, an attention window is estimated to mark the 3D moving object: the size of the window can increase or decrease according to the size of the object. Finally, two experiments based on the multiple-sensor binocular head are performed to verify the theoretical results.

Visually Servoed 3-D Alignment of Multiple Objects with Subnanometer Precision

This paper presents a visual measurement technique and the associated visual servo control method that enable 3-D alignment of multiple objects with subnanometer precision. Such high precision is achieved through eliminating the bias error caused by spatial sampling in the in-plane motion estimation, using a highly sensitive measurement technique based on interference for the out-of-plane motion, and continuously compensating for time varying uncertainties induced by mechanical forces and thermal drifts. The developed measurement technique is integrated with a motion stage to experimentally demonstrate visually servoed 3-D alignment, in which two microcantilevers are aligned in the 3-D space with alignment errors below 1 nm (rms) in all axes. Nanostepping of 0.5 nm magnitude along the out-of-plane z-axis is also performed between the two microcantilevers to illustrate the super precision of the visually served 3-D alignment.

Image-Based Visual PID Control of a Micro Helicopter Using a Stationary Camera

This paper proposes an image-based visual servo control method for a micro helicopter. The helicopter does not have any sensors to measure its position or posture on the body. A stationary camera is placed on the ground and it obtains image features of the helicopter. The differences between current features and given reference features are computed. PID controllers then make control input voltages for helicopter control and they drive the helicopter. The proposed controller can avoid some major difficulties in computer vision such as numerical instability due to image noise or model uncertainties, since the reference is defined in the image frames. An experimental result demonstrates that the proposed controller can keep the helicopter in a stable hover.

Approximate Exploring Motion Control for Eye-in-Hand Visual Servoing

This paper proposes a visual servo control method for exploring motion of eye-in-hand robot to recognize a three-dimensional object. Optimal motion is based on the strategy in which the position estimation precision of the feature points gets improved when the feature points move widely, and the optimal robot motion provides the widest transitions of the feature points per unit robot motion, so that the robot should be controlled to purse such motion. In addition, this paper describes the way to reduce a large number of calculation efforts. The validity of our approach is confirmed in the several experimental results.

Intelligent control for the vision-based indoor navigation of an A-life mobile robot

Realizing steady and reliable navigation is a prerequisite for a mobile robot, but this facility is often weakened by an unavoidable slip or some irreparable drift errors of sensors in long-distance navigation. Although perceptual landmarks were solutions to such problems, it is impossible not to miss landmarks occasionally at some specific spots when the robot moves at different speeds, especially at higher speeds. If the landmarks are put at random intervals, or if the illumination conditions are not good, the landmarks will be easier to miss. In order to detect and extract artificial landmarks robustly under multiple illumination conditions, some low-level but robust image processing techniques were implemented. The moving speed and self-location were controlled by the visual servo control method. In cases where a robot suddenly misses some specific landmarks when it is moving, it will find them again in a short time based on its intelligence and the inertia of the previous search motion. These methods were verified by the reliable vision-based indoor navigation of an A-life mobile robot.