Robot Visual Servo Control Research Articles

Visual Positioning of Nasal Swab Robot Based on Hierarchical Decision.

This study focuses on a robot vision localization method for coping with the operational task of automatic nasal swab sampling. The application is important in the detection and epidemic prevention of Corona Virus Disease 2019 (COVID-19) to alleviate the large-scale negative impact of individuals suffering from pneumonia owing to COVID-19. In this method, the idea of a hierarchical decision network is used to consider the strong infectious characteristics of the COVID-19, which is followed by processing the robot behavior constraint condition. The visual navigation and positioning method using a single-arm robot for sampling is also planned, which considers the operation characteristics of medical staff. In the decision network, the risk factor for potential contact infection caused by swab sampling operations is established to avoid the spread among personnel. A robot visual servo control with artificial intelligence characteristics is developed to achieve a stable and safe nasal swab sampling operation. Experiments demonstrate that the proposed method can achieve good vision positioning for the robots and provide technical support for managing new major public health situations.

Random vector functional link network with L21 norm regularization for robot visual servo control with feature constraint

Random vector functional link network with L21 norm regularization for robot visual servo control with feature constraint

Constrained Image-Based Visual Servoing of Robot Manipulator with Third-Order Sliding-Mode Observer

A new image-based robot visual servo control strategy based on a third-order sliding-mode observer (TOSM) model predictive control is proposed in this study. This new control strategy solves the problem of robot visual servo control with system constraints and time-varying disturbances when the camera and model of the robot manipulator are uncertain and the joint velocity is unknown. In the proposed method, the joint velocity and system centralized uncertainties are estimated simultaneously based on a third-order sliding-mode observer, and the image-based visual servoing problem is transformed into a nonlinear optimization problem based on a model predictive control method considering both visibility constraints and actuator constraints, which minimizes the predicted trajectory cost function to generate the control signal for each cycle. Simulations were carried out to verify the effectiveness of the proposed control scheme.

Recent Advances on Robot Visual Servo Control Methods

Background: In order to enable robots to complete complex tasks, robots are required to have higher intelligence and adaptability. RVSC (Robot Visual Servo Control) is an organic combination of machine vision and servo control and is a complex system with strong coupling and nonlinearity, which improves the performance of robots and provides feasible solutions for solving practical problems in many fields. Objective: To review the latest development of RVSC methods and provide references for researchers in related fields. Methods: RVSC related patents and literature were analyzed from the aspects of the algorithm, innovation and application. In addition, the typical RVSC methods are summarized. Results: This paper analyzes the advantages and disadvantages of various methods in many patents and literature. Moreover, the typical applications, potential problems and future development directions of this field is put forward. Conclusion: RVSC has better performance than traditional control methods. In addition, it can also be combined with some algorithms or knowledge to further improve performance. But, the further improvements of RVSC are needed in the following areas: the convenience of operation, the optimization of trajectory planning, the combination of multiple sensors, etc. Furthermore, more patents of the RVSC also should be invented.

An Uncalibrated Visual Servo Method Based on Projective Homography

An uncalibrated visual servo method based on projective homography, denoted as Projective Homography based Uncalibrated Visual Servoing (PHUVS), is proposed in this paper, in which a novel task function based on the element of projective homography is devised to realize visual servo without a prior knowledge of the camera intrinsic parameters and hand-eye relationships. The main advantage of this method is that it is not only suitable for totally uncalibrated scenarios but also cheap in computation costs when compared with classical image-based uncalibrated visual servoing methods. Numerical experiments are performed and the results confirm that the new approach is capable of both static positioning and dynamic tracking tasks, and presents competitive computational efficiency and accuracy performance. Note to Practitioners —Vision guided robot has been widely used in industries. However the industrial state-of-art robot visual servo systems usually require system calibration, including camera calibration, hand-eye calibration, and robot calibration. These calibration process are time consuming and requires some expertise, which is rare for ordinary workers. Moreover, when hand-eye configuration changed due to certain reasons such as adjustment of production line layout or displacement due to machining force or switch of robot end effector, the calibrated parameters are no longer accurate. This paper offers a plug and play approach for eye-in-hand robot visual servo tasks. When a robot-camera system is set up, there is no need for the operator to calibrate the system. With reference (goal) image and the current images obtained by the moving camera, the robot can autonomously achieve the goal pose which is described simply by the reference camera. With our novel task function, the computation cost is reduced, which will benefit the real-time application. The accuracy and some dynamic performance in both static positioning and dynamic target tracking are evaluated by robotic toolbox. This approach has the potential to improve the flexibility and robustness of robot system in robotic manipulation tasks such as assemble and painting, or robotic machining tasks such as matching the origin point of operating trajectory in large part manufacturing. Furthermore, this approach is generic, and can be extended to mobile robot visual servo control tasks.

Visual Servo Technology Research of Industrial Palletizing Robot

With the wide application of palletizing robot in the field of logistics automation, The market is making higher demand for the adaptability and accuracy of the palletizing robot. In order to solve the problems of single function and poor adaptability for the ordinary palletizing robot, we combined the machine vision and palletizing robot control technology, built the palletizing robot visual servo control system platform, carried out the related graphic processing algorithms research. By test and verification, this palletizing robot visual servo control system platform can finish the detection, sorting and stacking for different goods fast and accurately. It can offer reference for the subsequent research of the palletizing robot visual servo technology.

Based on Compression Sensing of Video Coding in Robot Servo System

The robot visual servo control is the current robot control of a main research direction, this design based on the analysis of the theory of compression sensing, distributed video decoding and image super-resolution reconstruction. Simulation and experimental results show that the compression sensing theory applied to image super-resolution reconstruction, make high resolution image reconstruction can fully exert the original low resolution image of the structural characteristics, in order to protect the original low resolution image edge details such as information, and the traditional calibration approach compared with high resolution image can improve the image edge, texture, and other details of the characteristics of reconstruction effect and improve the precision of the recognition.

A performance criterion for the depth estimation with application to robot visual servo control

AbstractThis article deals with the depth observability problem of a robot visual system with a moving camera. In the visual system, the unknown depth of a feature point is estimated from the input of the camera velocity and the output of the image of the feature point. Although it is well known that the linear velocity of the camera must satisfy some constraints for successful depth estimation, this proposes a criterion to measure the performance of the depth estimation, which is a heuristic extension from an estimation result of a linear system. This performance criterion depends on both the image position and the linear velocity of the camera. Some simulation and experiment examples demonstrate and verify the proposed performance criterion. Furthermore, this criterion is used to develop a new visual servo control scheme that has good performance in both the depth estimation and the visual control. This control scheme is also verified by a simulation example. © 2001 John Wiley & Sons, Inc.

Adaptive Control of Robots Using Visual Feedback

Vision based robot control systems provide capabilities for modifying positions to accommodate uncertainty in the task environment. Vision based robot control research has focused on vision processing issues, while control system design has been limited to ad hoc strategies In this paper, we formalize an analytical approach to the design of dynamic robot visual servo control systems An image-based structure represents a new approach to visual servo control which uses local image features as feedback control signals image-based control presents formidable engineering problems for controller design, including complex dynamics, kinematics, and feature transformations, as well as unknown parameters, and measurement noise and delays A model reference adaptive Controller (MRAC) is designed to satisfy these requirements Simulation studies are described which indicate the feasibility and expected performance of this approach for systems with 1, 2. and 3 degrees of freedom.