Model-based vs. model-free visual servoing: A Performance evaluation in microsystems

In this paper, model-based and model-free image based visual servoing (VS) approaches are implemented on a microassembly workstation, and their regulation and tracking performances are evaluated. A precise image based VS relies on computation of the image jacobian. In the model-based visual servoing, the image Jacobian is computed via calibrating the optical system. Precisely calibrated model based VS promises better positioning and tracking performance than the model-free approach. However, in the model-free approach, optical system calibration is not required due to the dynamic Jacobian estimation, thus it has the advantage of adapting to the different operating modes

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

Visual Servoing using the Sum of Conditional Variance

International audienceIn this paper we propose a new way to achieve direct visual servoing. The novelty is the use of the sum of conditional variance to realize the optimization process of a positioning task. This measure, which has previously been used successfully in the case of visual tracking, has been shown to be invariant to non-linear illumination variations and inexpensive to compute. Compared to other direct approaches of visual servoing, it is a good compromise between techniques using the illumination of pixels which are computationally inexpensive but non robust to illumination variations and other approaches using the mutual information which are more complicated to compute but offer more robustness towards the variations of the scene. This method results in a direct visual servoing task easy and fast to compute and robust towards non-linear illumination variations. This paper describes a visual servoing task based on the sum of conditional variance performed using a Levenberg-Marquardt optimization process. The results are then demonstrated through experimental validations and compared to both photometric-based and entropy-based techniques

Sliding mode control for robust and smooth reference tracking in robot visual servoing

[EN] An approach based on sliding mode is proposed in this work for reference tracking in robot visual servoing. In particular, 2 sliding mode controls are obtained depending on whether joint accelerations or joint jerks are considered as the discontinuous control action. Both sliding mode controls are extensively compared in a 3D-simulated environment with their equivalent well-known continuous controls, which can be found in the literature, to highlight their similarities and differences. The main advantages of the proposed method are smoothness, robustness, and low computational cost. 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Visual Servoing For Robotic Positioning And Tracking Systems

Visual servoing is a robot control method in which camera sensors are used inside the control loop and visual feedback is introduced into the robot control loop to enhance the robot control performance in accomplishing tasks in unstructured environments. In general, visual servoing can be categorized into image-based visual servoing (IBVS), position-based visual servoing (PBVS), and hybrid approach. To improve the performance and robustness of visual servoing systems, the research on IBVS for robotic positioning and tracking systems mainly focuses on aspects of camera configuration, image features, pose estimation, and depth determination. In the first part of this research, two novel multiple camera configurations of visual servoing systems are proposed for robotic manufacturing systems for positioning large-scale workpieces. The main advantage of these two multiple camera configurations is that the depths of target objects or target features are constant or can be determined precisely by using computer vision. Hence the accuracy of the interaction matrix is guaranteed, and thus the positioning performances of visual servoing systems can be improved remarkably. The simulation results show that the proposed multiple camera configurations of visual servoing for large-scale manufacturing systems can satisfy the demand of high-precision positioning and assembly in the aerospace industry. In the second part of this research, two improved image features for planar central symmetrical-shaped objects are proposed based on image moment invariants, which can represent the pose of target objects with respect to camera frame. A visual servoing controller based on the proposed image moment features is designed and thus the control performance of the robotic tracking system is improved compared with the method based on the commonly used image moment features. Experimental results on a 6-DOF robot visual servoing system demonstrate the efficiency of the proposed method. Lastly, to address the challenge of choosing proper image features for planar objects to get maximal decoupled structure of the interaction matrix, the neural network (NN) is applied as the estimator of target object poses with respect to camera frame based on the image moment invariants. Compared with previous methods, this scheme avoids image interaction matrix singularity and image local minima in IBVS. Furthermore, the analytical form of depth computation is given by using classical geometrical primitives and image moment invariants. A visual servoing controller is designed and the tracking performance is enhanced for robotic tracking systems. Experimental results on a 6-DOF robot system are provided to illustrate the effectiveness of the proposed scheme

Visual Servoing in Robotics

Visual servoing is a well-known approach to guide robots using visual information. Image processing, robotics, and control theory are combined in order to control the motion of a robot depending on the visual information extracted from the images captured by one or several cameras. With respect to vision issues, a number of issues are currently being addressed by ongoing research, such as the use of different types of image features (or different types of cameras such as RGBD cameras), image processing at high velocity, and convergence properties. As shown in this book, the use of new control schemes allows the system to behave more robustly, efficiently, or compliantly, with fewer delays. Related issues such as optimal and robust approaches, direct control, path tracking, or sensor fusion are also addressed. Additionally, we can currently find visual servoing systems being applied in a number of different domains. This book considers various aspects of visual servoing systems, such as the design of new strategies for their application to parallel robots, mobile manipulators, teleoperation, and the application of this type of control system in new areas

Dynamic visual servoing from sequential regions of interest acquisition.: On behalf of: Multimedia Archives Dynamic visual servoing from sequential regions of interest acquisition.

International audienceOne of the main drawbacks of vision-based control that remains unsolved is the poor dynamic performances caused by the low acquisition frequency of the vision systems and the time latency due to processing. We propose in this paper to face the challenge of designing a high-performance dynamic visual servo control scheme. Two versatile control laws are developed in this paper: a position-based dynamic visual servoing and an image-based dynamic visual servoing. Both control laws are designed to compute the control torques exclusively from a sequential acquisition of regions of interest containing the visual features to achieve an accurate trajectory tracking. The presented experiments on vision-based dynamic control of a high-speed parallel robot show that the proposed control schemes can perform better than joint-based computed torque control

Positioning and trajectory following tasks in microsystems using model free visual servoing

In this paper, we explore model free visual servoing algorithms by experimentally evaluating their performances for various tasks performed on a microassembly workstation developed in our lab. Model free or so called uncalibrated visual servoing does not need the system calibration (microscope-camera-micromanipulator) and the model of the observed scene. It is robust to parameter changes and disturbances. We tested its performance in point-to-point positioning and various trajectory following tasks. Experimental results validate the utility of model free visual servoing in microassembly tasks