Calibration-free and model-independent method for high-DOF image-based visual servoing

This paper presents a novel method to improve the performance of high-DOF image base visual servoing （IBVS） with an uncalibrated camera. Firstly, analysis and comparison between point-based and moment-based features are carried out with respect to a 4-DOF positioning task. Then, an extended interaction matrix （IM） related to the digital image, and a Kalman filter （KF）-based estimation algorithm of the extended IM without calibration and IM model are proposed. Finally, the KF-based algorithm is extended to realize an approximation to decoupled control scheme. Experimental results conducted on an industrial robot show that our proposed methods can provide accurate estimation of IM, and achieve similar performance compared with traditional calibration-based method. Therefore, the proposed methods can be applied to any robot control system in variational environments, and can realize instant operation to planar object with complex and unknown shape at large displacement.

Sliding Mode Variable Structure Control for Visual Servoing System

A visual servoing tracking controller is proposed based on the sliding mode control theory in order to achieve strong robustness against parameter variations and external disturbances. A sliding plane with time delay compensation is presented by the pre-estimate of states. To reduce the chattering of the sliding mode controller, a modified exponential reaching law and hyperbolic tangent function are applied to the design of visual controller and robot joint controller. Simulation results show that the visual servoing control scheme is robust and has good tracking performance.

Design of Robotic Visual Servo Control Based on Neural Network and Genetic Algorithm

A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP Mgorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.

Visual Object Tracking and Servoing Control of a Nano-Scale Quadrotor:System,Algorithms,and Experiments

There are two main trends in the development of unmanned aerial vehicle(UAV)technologies:miniaturization and intellectualization,in which realizing object tracking capabilities for a nano-scale UAV is one of the most challenging problems.In this paper,we present a visual object tracking and servoing control system utilizing a tailor-made 38 g nano-scale quadrotor.A lightweight visual module is integrated to enable object tracking capabilities,and a micro positioning deck is mounted to provide accurate pose estimation.In order to be robust against object appearance variations,a novel object tracking algorithm,denoted by RMCTer,is proposed,which integrates a powerful short-term tracking module and an efficient long-term processing module.In particular,the long-term processing module can provide additional object information and modify the short-term tracking model in a timely manner.Furthermore,a positionbased visual servoing control method is proposed for the quadrotor,where an adaptive tracking controller is designed by leveraging backstepping and adaptive techniques.Stable and accurate object tracking is achieved even under disturbances.Experimental results are presented to demonstrate the high accuracy and stability of the whole tracking system.

SVM-based Identification and Un-calibrated Visual Servoing for Micro-manipulation

This paper presents an improved support vector machine （SVM） algorithm, which employs invariant moments-based edge extraction to obtain feature attribute. A heuristic attribute reduction algorithm based on rough set＇s discernible matrix is proposed to identify and classify micro-targets. To avoid the complicated calibration for intrinsic parameters of camera, an improved Broyden＇s method is proposed to estimate the image Jacobian matrix which employs Chebyshev polynomial to construct a cost function to approximate the optimization value. Finally, a visual controller is designed for a robotic micromanipulation system. The experiment results of micro-parts assembly show that the proposed methods and algorithms are effective and feasible.

Grasp Planning and Visual Servoing for an Outdoors Aerial Dual Manipulator

This paper describes a system for grasping known objects with unmanned aerial vehicles (UAVs) provided with dual manipulators using an RGB-D camera. Aerial manipulation remains a very challenging task. This paper covers three principal aspects for this task: object detection and pose estimation, grasp planning, and in-flight grasp execution. First, an artificial neural network (ANN) is used to obtain clues regarding the object’s position. Next, an alignment algorithm is used to obtain the object’s sixdimensional (6D) pose, which is filtered with an extended Kalman filter. A three-dimensional (3D) model of the object is then used to estimate an arranged list of good grasps for the aerial manipulator. The results from the detection algorithm—that is, the object’s pose—are used to update the trajectories of the arms toward the object. If the target poses are not reachable due to the UAV’s oscillations, the algorithm switches to the next feasible grasp. This paper introduces the overall methodology, and provides the experimental results of both simulation and real experiments for each module, in addition to a video showing the results.

A method of initial welding position guiding for arc welding robot based on visual servo control

In order to solve the visual guiding task of initial welding position for arc welding robot, this paper presents a practice prone image based visual servo control strategy without calibration, and we perform validating experiments on a nine DOF arc welding robot system. Experimental results illustrate presented method has the function to fulfill the task of welding robot initial positioning with certain anti jamming ability. This method provides a basis for guiding welding gun to initial welding pose with real typical seam’s image properties to replace flag block properties, and is a significant exploit to realize visual guiding of initial welding position and seam tracing in robot welding system.

An improved self-calibration approach based on adaptive genetic algorithm for position-based visual servo

An improved self-calibrating algorithm for visual servo based on adaptive genetic algorithm is proposed in this paper. Our approach introduces an extension of Mendonca-Cipolla and G. Chesi＇s self-calibration for the positionbased visual servo technique which exploits the singular value property of the essential matrix. Specifically, a suitable dynamic online cost function is generated according to the property of the three singular values. The visual servo process is carried out simultaneous to the dynamic self-calibration, and then the cost function is minimized using the adaptive genetic algorithm instead of the gradient descent method in G. Chesi＇s approach. Moreover, this method overcomes the limitation that the initial parameters must be selected close to the true value, which is not constant in many cases. It is not necessary to know exactly the camera intrinsic parameters when using our approach, instead, coarse coding bounds of the five parameters are enough for the algorithm, which can be done once and for all off-line. Besides, this algorithm does not require knowledge of the 3D model of the object. Simulation experiments are carried out and the results demonstrate that the proposed approach provides a fast convergence speed and robustness against unpredictable perturbations of camera parameters, and it is an effective and efficient visual servo algorithm.

Adaptive Stabilization for Uncertain Nonholonomic Dynamic Mobile Robots Based on Visual Servoing Feedback

nonholonomic 的稳定问题动态活动机器人与一修理(装天花板) 照相机在这份报纸被探讨。首先，一个照相机目标视觉 servoing 运动学的模型被利用针孔照相机模型介绍，一个运动学的稳定控制器为运动学的模型被给。然后，一个适应滑动模式控制器被设计面对与照相机系统联系的参量的无常稳定不明确的动态活动机器人。建议控制器不仅对象集体变化那样的结构化的无常而且到象骚乱那样的未组织的柔韧。建议控制系统的稳定性和估计的参数的固定被 Lyapunov 方法严厉地证明。模拟结果被介绍说明控制法律的表演。

Predictive Control for Visual Servo Stabilization of Nonholonomic Mobile Robots

活动机器人获得了的 nonholonomic 的视觉伺服稳定广泛的注意。当前，这个问题的解决方案不管多么不认为可见性限制和致动器是限制，因此设计控制器是困难的在实际申请认识到令人满意的性能。在这份报纸，为一个活动机器人的视觉伺服稳定的一个预兆的控制器被介绍。第一，利用产生速度的命令的一个运动学的预兆的稳定控制器被介绍。然后，来临以便 asymptotically 做活动机器人的实际速度到需要的，一个动态预兆的控制器被设计。建议预兆的控制器能容易处理限制。最后，几模拟被执行，并且结果说明建议控制计划是有效的解决视觉伺服稳定问题。

Robust visual servoing based Chinese calligraphy on a humanoid robot

A robust visual servoing system is investigated on a humanoid robot which grasps a brush in Chinese calligraphy task.The system is implemented based on uncalibrated visual servoing controller utilizing Kalman-Bucy filter,with the help of an object detector by continuously adaptive MeanShift(CAMShift) algorithm.Under this control scheme,a humanoid robot can satisfactorily grasp a brush without system modeling.The proposed method is shown to be robust and effective through a Chinese calligraphy task on a NAO robot.

A dual working mode mobile robot system based on visual guiding and visual servoing

A dual operational modes mobile robot system based on visual guiding and visual servo control is presented. This system consists of a mobile robot with a two-axis manipulator and a tele-operation station. In the visual guiding mode, for the robot works in an open loop visual servo control mode, the manipulating burden of the operator is reduced largely. In the visual servo mode the robot can locate the position of the target assigned by the operator and pick it up by its manipulator. With the help of the operator, the diffieuh problems of finding and handling a target in a complicated environment by the robot can be solved easily.

CONTROL SCHEMES FOR CMAC NEURAL NETWORK-BASED VISUAL SERVOING

In IBVS (image based visual servoing), the error signal in image space should be transformed into the control signal in the input space quickly. To avoid the iterative adjustment and complicated inverse solution of image Jacobian, CMAC (cerebellar model articulation controller) neural network is inserted into visual servo control loop to implement the nonlinear mapping. Two control schemes are used. Simulation results on two schemes are provided, which show a better tracking precision and stability can be achieved using scheme 2.

Path Planning of Planar Kinked Line Seam by Visual Servoing for Robotic Welding

Welding path planning can substitute for the manual teaching process of the robot and can promote the autonomous level of the robotic welding. A path planning method by visual servoing was presented, in which the optimal angle of charge-coupled device (CCD) camera was also planned. Aiming at planning two forms of kinked line seams, obtuse angle seam and right angle seam, a practicable solution was put forward. In this solution, the intersection of two adjacent straight segments is detected in each local seam image, and if intersection is found, the seam errors are calculated using the next straight segment. The experimental results show that kinked line seam can be well planned using this solution.

Uncalibrated visual servoing design for competitive networked robots

In this paper,a method with an eye-in-hand configuration is developed to hit targets during visual tracking for the TLS(Tele-Light Saber) game.It is not necessary to calibrate camera parameters and predict the trajectory of the moving object.Firstly,the expression of the image Jacobian matrix for the eye-in-hand configuration is proposed,and then an update law is designed to estimate the image Jacobian online.Furthermore,a control scheme is presented and the Lyapunov method is employed to prove asymptotic convergence of image errors.No assumption for the moving objects is needed.Finally,both simulation and experimental results are shown to support the approach in this paper.

Study on algorithm of dynamic uncalibrated eye-in-hand visual servoing system

Currently, most visual servoing system must be calibrated, while it is impossible to calibrate cameras and robot models precisely in industrial practice, so a novel dynamic uncalibrated eye-in-hand visual servoing system of tracking a moving target is proposed. The method does not require calibration of camera and robot kinematic models. Vision guided algorithm for tracking dynamic image is developed through minimizing nonlinear objective function. For the large residual has not been approximated in dynamic environment and the change of composite image Jacobian with time increment has not been computed in visual servoing system now,large residuals are dynamic approximated and the change of composite image Jacobian at each iterative step is computed. Simulation results demonstrate the validity of these approaches.

SPATIAL TRAJECTORY PREDICTION OF VISUAL SERVOING

Target tracking is one typical application of visual servoing technology. It is still a difficult task to track high speed target with current visual servo system. The improvement of visual servoing scheme is strongly required. A position-based visual servo parallel system is presented for tracking target with high speed. A local Frenet frame is assigned to the sampling point of spatial trajectory. Position estimation is formed by the differential features of intrinsic geometry, and orientation estimation is formed by homogenous transformation. The time spent for searching and processing can be greatly reduced by shifting the window according to features location prediction. The simulation results have demonstrated the ability of the system to track spatial moving object.

A Stable Switch Method Based on Fusion in Uncalibrated Visual Servoing

Stable switch control between multiple cameras for uncalibrated visual servoing was studied. Switch images based on fusion were presented to get the continuous dynamic image Jacobian matrix among robots and distributed visual sensors. The designed fusion algorithm is suitable to have dynamically adjustable fusion weights, and the fusion structure was analyzed. Simulations and experiments without any knowledge of mobile robots and uncalibrated visual sensors show that the method has higher adaptability than the traditional instant switch control method. The method can enhance the system stability at the switching process.

A PFM-based Global Convergence Visual Servo Path Planner

作为计划方法的一条古典本地路径，因为它的简洁和优雅，潜在的地方法(PFM ) 在机器人学地里广泛地被使用。然而，当产生一条路径让一个机器人列在后面时，这个方法的一个主要缺点是本地最小的存在。在这份报纸，我们在场由使用 PFM 方法在看法(FOV ) 的照相机地以内保留特征为视觉伺服计划途径的一条路径。严密分析然后被介绍证明计划方法的构造路径的全球稳定性。而且，怎么获得的问题一更好三维(3D ) 照相机路径也广泛地被学习。模拟结果被提供验证建议路径规划者的表演。

基于机器视觉的火箭贮箱焊缝射线检测

针对当前火箭贮箱焊缝采用工业机器人作为射线检测系统的执行机构,通常按照“示教-再现”进行既定轨迹运动,不能适应小批量多品种的柔性与智能生产线的问题,将伺服机构、图像处理技术与智能机器人相结合,采用基于图像的视觉伺服控制(IBVS)方法反馈当前图像特征信息,与期望图像特征进行对比形成图像特征偏差,再基于SSA-BP的控制方法,将得到的特征误差用于火箭贮箱焊缝检测中的焊缝位置误差补偿,以提高射线检测系统的作业精度。最后通过试验,验证了该方法具有良好的自适应性和鲁棒性。