An adapted visual servo algorithm for substation equipment inspection robot

In order to enhance the accuracy of the target image captured by the substation inspection robot, this paper proposes an AVS algorithm based control optimization method for the substation inspection robot, which uses the feature transformation method to match the image and template image of the substation equipment captured by the robot, verifies the existence status of the target image elements in the image, and calculates the offset pixel correction results to capture more Accurate target image. The experimental results show that the method effectively improves the efficiency of image capture of inspection robot in substation equipment, and has good practical performance.

Vision allows a robotic system to obtain a lot of information on the surrounding environment to be used for motion planning and control. When the control is based on feedback of visual information is called Visual Servoing. Visual Servoing is a powerful tool which allows a robot to increase its interaction capabilities and tasks complexity. In this chapter we describe the architecture of the Robotenis system in order to design two different control strategies to carry out tracking tasks. Robotenis is an experimental stage that is formed of a parallel robot and vision equipment. The system was designed to test joint control and Visual Servoing algorithms and the main objective is to carry out tasks in three dimensions and dynamical environments. As a result the mechanical system is able to interact with objects which move close to 2m=s. The general architecture of control strategies is composed by two intertwined control loops: The internal loop is faster and considers the information from the joins, its sample time is 0:5ms. Second loop represents the visual Servoing system and it is an external loop to the first mentioned. The second loop represents the main study purpose, it is based in the prediction of the object velocity that is obtained from visual information and its sample time is 8:3ms. The robot workspace analysis plays an important role in Visual Servoing tasks, by this analysis is possible to bound the movements that the robot is able to reach. In this article the robot jacobian is obtained by two methods. First method uses velocity vector-loop equations and the second is calculated from the time derivate of the kinematical model of the robot. First jacobian requires calculating angles from the kinematic model. Second jacobian instead, depends on physical parameters of the robot and can be calculated directly. Jacobians are calculated from two different kinematic models, the first one determines the angles each element of the robot. Fist jacobian is used in the graphic simulator of the system due to the information that can be obtained from it. Second jacobian is used to determine off-line the work space of the robot and it is used in the joint and visual controller of the robot (in real time). The work space of the robot is calculated from the condition number of the jacobian (this is a topic that is not studied in article). The dynamic model of the mechanical system is based on Lagrange multipliers, and it uses forearms and end effector platform of non-negligible inertias for the

Asymptotic Stability of Uncalibrated Eye-in-Hand Visual Servoing: An Affine Invariance Perspective

Substation has always been the hub of power grid transmission, supply and distribution. Its construction has become a key link in the process of power grid construction. In the operation of the power grid, the secondary system provides the basic guarantee for the safe operation of the substation. The safety and reliability of the secondary work is of great significance to the power grid. In order to ensure the safe and stable operation of the secondary system, the protection still adopts the way of directly inputting the voltage and current of the primary system from the current and voltage transformer, and directly controlling the circuit breaker outlet through the relay. This paper mainly aims at the acceptance status of secondary equipment in the intelligent substation. Based on the analytic hierarchy process for data acquisition, analysis and research, obtain the operation acceptance status of secondary equipment in smart substation and make fault prediction. In the example test, the final evaluation score of the secondary equipment in the smart substation is 97.54, which verifies the accuracy of the fuzzy comprehensive evaluation method. Establish a scientific, consistent, feasible and standard state index for the power system.

In smart substations, how to use multi-interval system information to realize online monitoring of substations, so as to obtain abnormal operation status early through state analysis, is a topic that needs to be solved in smart substations. The paper uses the multi-interval information correlation method to realize the online monitoring of the status of the primary equipment of the substation. By analysing the correlation factors of the electrical sampling of the multi-interval primary equipment, and the online monitoring of the multi-interval start-up information, combined with the traditional substation interval setting, the abnormal inspection of the primary equipment of the substation can be realized. The function of condition monitoring.

Vision is in fact the richest source of information for ourself and also for outdoors Robotics, and can be considered the most complex and challenging problem in signal processing for pattern recognition. The first results using Vision in the control loop have been obtained in indoors and structured environments, in which a line or known patterns are detected and followed by a robot (Feddema & Mitchell (1989), Masutani et al. (1994)). Successful works have demonstrated that visual information can be used in tasks such as servoing and guiding, in robot manipulators and mobile robots (Conticelli et al. (1999), Mariottini et al. (2007), Kragic & Christensen (2002).) Visual Servoing is an open issue with a long way for researching and for obtaining increasingly better and more relevant results in Robotics. It combines image processing and control techniques, in such a way that the visual information is used within the control loop. The bottleneck of Visual Servoing can be considered the fact of obtaining robust and on-line visual interpretation of the environment, which can be usefully treated by control structures and algorithms. The solutions provided in Visual Servoing are typically divided into Image Based Control Techniques and Pose Based Control Techniques, depending on the kind of information provided by the vision system that determine the kind of references that have to be sent to the control structure (Hutchinson et al. (1996), Chaumette & Hutchinson (2006) and Siciliano & Khatib (2008)). Another classical division of the Visual Servoing algorithms considers the physical disposition of the visual system, yielding to eye-in-hand systems and eye-to-hand systems, that in the case of Unmanned Aerial Vehicles (UAV) can be translated as on-board visual systems (Mejias (2006)) and ground visual systems (Martinez et al. (2009)). The challenge of Visual Servoing is to be useful in outdoors and non-structured environments. For this purpose the image processing algorithms have to provide visual information that has to be robust and works in real time. UAV can therefore be considered as a challenging testbed for visual servoing, that combines the difficulties of abrupt changes in the image sequence (i.e. vibrations), outdoors operation (non-structured environments) and 3D information changes (Mejias et al. (2006)). In this chapter we give special relevance to the fact of obtaining robust visual information for the visual servoing task. In section (2).we overview the main algorithms used for visual tracking and we discuss their robustness when they are applied to image sequences taken from the UAV. In sections (3). and (4). we analyze how vision systems can perform 3D pose estimation that can be used for

In order to further enhance the security, stability and reliability of smart substation operation, we research and develop an intelligent vision system for monitoring the operational environment of smart substation and the operational status of intelligent substation equipment. At the recent stage, this system includes the intelligent environment surveillance subsystem based on visual tracking technology and machine learning algorithm, and the intelligent equipment surveillance subsystem based on feature matching and image fusion technology.

This paper introduces region of interest (ROI) as a functionality to local tone mappings of high dynamic range (HDR) radiance maps. In the proposed scheme, a new quadratic regularization method is provided to decompose the luminance of an HDR radiance map into a base layer with large gradients and a detail layer with small gradients. An ROI based global tone mapping is designed to compress the base layer. Since the dynamic range of a display device is limited, more dynamic ranges are assigned to the base layer in the ROI such that the visual quality of the ROI is preserved better. The detail layer can be amplified to enhance local contrasts such that the output LDR images look sharper. Both automatic and interactive modes are provided. With our automatic mode, the generated low dynamic range (LDR) images look natural and local details are also preserved very well. In addition, a user friendly interactive mode is provided which is also able to produce dynamic LDR images in real time.

In view of the problem of low reliability and automation of advanced application functions such as sequential control in smart substation, the logic configuration mode of sequential control is analyzed based on the principle of logic virtual circuit of bay level equipment in smart substation, and the realization of sequential control function based on bay level equipment is proposed. This method improves the operational reliability and configuration flexibility of sequential control, and fundamentally solves the technical application problem, so as to improve the intelligent level of power grid.

For the implementation of stereo image-based visual servoing algorithm in the eye-in-hand robotics applications, one of the main concerns is the accurate point feature detection and matching algorithm. Since the visual servoing is carried out in the textureless environment, the feature detection process is even more challenging. To fulfill the requirement of a robust and reliable point feature detection process, in this paper we present the novel deep learning-based algorithm. The approach based on convolutional neural networks and algorithm for detection of manufacturing entities is proposed and detected regions of interest are utilized for the improvement of the point feature detection algorithm. The proposed algorithm is experimentally evaluated in real-world settings by using wheeled nonholonomic mobile robot RAICO equipped with stereo vision system. The experimental results show the improvement of 58% in the accuracy of matched point features in the images obtained during the visual servoing process. Moreover, with the implementation of the proposed deep learning-based approach, the number of successful experimental runs has increased by 80%.

As secondary equipment in smart substation all follow IEC 61850, it makes information sharing and interoperability the biggest differences between the smart and the conventional. It makes condition-based maintenance (CBM) which relies on high performance condition monitoring system feasible. In smart substations, the condition of protection and control equipment, network and communication links can be acquired, which makes developing a condition monitoring system feasible. This paper analyzes current research and application situations of key technologies. Works on information modeling, data analysis and process are introduced. Since the switches haven't been modeled, we give out an information modeling method based on existed standards. We summarize and compare the advantages and shortcomings of various data analysis and processing methods of condition monitoring. Considering the condition data acquisition and storage, we introduce and analyze a method of secondary equipment condition data acquisition and storage based on Substation Configuration Description (SCD). Finally, combining with the current research situations and practical application, we discussed the prospect and future research directions of secondary equipment condition monitoring.

The design and implementation of a real-time object motion sensing and trajectory replicating system, dependent on the image-based visual servoing capabilities of an electrohydraulic eye-in-hand parallel manipulator has been described in this paper. The tracking system consists of a monocular vision apparatus affixed to the top platform of a six-cylinder parallel manipulator constructed with industry grade double-acting single rod hydraulic actuators and budget proportional valves. The control algorithm is an amalgamation of three principles: machine vision based object tracking, image based visual servoing and actuator-level feedback control utilizing the inverse kinematics of the parallel manipulator. The real-time experiments are performed on a laboratory scale parallel manipulator setup with the aid of a virtual tracking object rendered on a computer monitor. The results for a few reference trajectories reveal that accurate tracking of target motions can be achieved with visual servoing algorithms designed for parallel manipulators in real-time.

Lung Perfused Blood Volume Calculated From Dynamic Contrast Enhanced DECT for Radiation Treatment Planning of Lung Cancer

In this study, we proposed a region of interest (ROI) compression algorithm under the deep learning self-encoder framework to improve the reconstruction performance of the image and reduce the distortion of the ROI. First, we adopted a remote sensing image cloud detection algorithm for detecting important targets in images, that is, separating the remote sensing background from important regions in remote sensing images and then determining the target regions because most traditional ROI-based image compression algorithms utilize the manual labeling of the ROI to achieve region separation in images. We designed a multiscale ROI self-coding network from coarse to fine with a hierarchical super priority layer to synthesize images to reduce the spatial redundancy more effectively, thus greatly improving the distortion rate performance of image compression. By using a spatial attention mechanism for the ROI in the image compression network, we achieved better compression performance.

Health pandemics such as Covid-19 have drastically shifted the world economics and boosted the development of automation technologies in the industries for continuous operation without human intervention. This paper elaborates on an approach to dynamically track and grasp moving objects using a robot arm. The robot arm has an eye-in-hand (EIH) configuration, where a camera is installed on the robot arm’s end effector. The working principle of the robot arm in this paper is mainly dependent on the recognition of augmented reality markers, i.e., Aruco markers, placed on the dynamically moving target object with continuous tracking. Then, the proposed system updates the predicted location for the markers using the Kalman filter for performing grasping. The proposed approach identifies the Aruco marker on the target object and estimates the object’s location using previous states, and performs grasping at the exact predicted location. When extracted information is updated, the vision system also implements a feedback control system for stability and reliability. The proposed approach is tested using simulation of the dynamic moving object with different speeds and directions. The robot arm with the Kalman filter can track and grasp the dynamic object at a speed of 0.2m/s with a 100% success rate while obtaining an 80% success rate at a speed of 0.3m/s. In conclusion, the moving object’s speed is directly proportional to the grasping time until it reaches the threshold speed for the camera in identifying the Aruco markers. Future works are required to improve the dynamic visual servo algorithm with motion planning when obstacles are present in the path of robot grasping.