| The transmission power line icing can cause various electrical accidents which will bring huge losses. These accidents include circuit breaker, line breaking, tower falling down, insulator flashover, communication interruption and so on. The traditional de-icing methods are not only inefficient but also unsafe. Hence, it becomes more urgent to develop new effective de-icing methods.Nowadays, more and more researchers pay attentions to a new de-icing equipment called as De-icing robot, which can realize online clearing and de-icing operation. However, as complex operating environment, there are many key technical problems to be resolved for de-icing robot researching, such as obstacle-surmounting mechanism design, sensor and control system development, and so on. The processing and perception of the environment can be achieved by visual information analysis for Vision-based robot control. The visual information can be used further to guide and control robot accomplishing the assigned tasks. To solve the on-line running and obstacle-surmounting problems, a visual control method based on vision sensor is presented in this thesis. The main research is summarized as follow:(1) Online visual images processing and analysis.(2) Guide and control de-icing robot online running and obstacle-surmounting by using feedback image information. The contents involve robotology, image processing, target recognition and space positioning technology, visual servo technology, and so on.This dissertation focuses on the methods for solving visual image servo control of de-icing robot. Main results and contributions of this dissertation are as follows:1. Based on the research experiences of the inspection robots, we design two-arm and tree-arm deicing robot mechanisms. Considering the complexity of crawling mechanism, the screw theory is used to simplify the kinematics analysis. Consequently, the forward and inverse kinematics models of robot arms are established successfully. The realizations of de-icing robot controls are on the basis of the above results.2. As there are several obstacles on the power line, such as counterweight, strain clamp, suspension clamp, the work environment of de-icing robot is very complex. Therefore, robot needs to be able to identify and locate various obstacles in front of transmission lines. By observing large number of actual images taken by camera of de-icing robot, we use the local characteristics of images to distinguish and locate the obstacles target. First, collect the sample photographs of obstacles and extract its SURF (Speeded-Up Robust Features), meanwhile build the template library of obstacles images’SURF feature. In practical applications, through matching the SURF characteristics of real-time and the template images, robot can accurately determine the image recognition results based on matching condition. If the match is successful, we think the obstacle in the current real-time image is the same as the template obstacle. Following, select more than four match points between the template image and real-time image to calculate homography matrix. Furthermore, we can estimate the distance between robot and obstacle after putting closest point coordinates into formula of distance measuring by monocular vision. Finally, robot can realize autonomous online navigation control when it determines obstacles information above.3. A recognition and location method based on external shape feature of obstacles is developed. As the external shape and contour are great different for different obstacles, robot can identify the obstacles by using its contour features. After pretreatment, optimal threshold segmentation, wavelet modulus image contour extraction for real-time image that captured by camera, the wavelet moment feature vector can be constructed after calculated the obstacle contour image’s wavelet moment. Then the SVM neural network can identify obstacles after putting image wavelet moment feature vectors input to the SVM neural network. In the process of target location, the straight line, circle, ellipse can be detected by Hough transform and the condition structure constraints in obstacle contour images. The distance between robot and obstacle also can be determined after putting the center point coordinates into formula of distance measuring. Then, the de-icing robot can realize autonomous navigation control meanwhile knowing the category and distance information.4. A visual servo control scheme is developed on the basis of analyzing the work environment characteristic and surmounting theory of de-icing robot. Firstly, since the moment feature have many superiority in global, general, it is set as the servo characteristics. Secondly, the wavelet neural network has strong learning and generalization abilities, a high performance visual servo controller can be designed by combining the two respect’s advantage. Then the trained ANN will have servo control capability. At the stage of obstacle striding movement, the visual servo controller direct mapping the error of feedback image characteristics and desired features for arm joint controller, and realize visual servo control for robot motion. This can avoid camera calibration and inverse Jocobian matrix of the traditional visual servo control. As a result, it can greatly reduce the amount of calculation and improve the response speed of the image visual servo.5. Based on above researches, a tree-arm de-icing prototype is developed. Meanwhile the difficulties and key techniques are summarized. Furthermore, the mechanical structure and design method of de-icing robot, motor device and control system are reported. Following, each division was tested respectively and the whole machine trial test. Finally, the online walking and de-icing experiments of de-icing robot are elaborated in the thesis.In this thesis ending part, the main innovations of the thesis are summarized, and prospected the next research work. |
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