Robot Visual Guidance System In The Port Handling Automation Key Technologies

In this paper, we develop some vital techniques of vision system guiding robot to autonomously complete container stevedore mission at port. The researched topic originates from the science and technology research fund project 'the application of computer vision to container-stevedore automation at ports'.The key modules of the visual guidance system are image segmentation, edge detection, breakpoint detection, camera calibration, 3D reconstruction, etc. When the images taken by camera have been processed by the system, the information of the spatial position and attitude of the interested object as well as the 3D motion speed of the camera relative to the object, which is necessary for guiding a manipulator to autonomously load and unload the containers, is extracted from them.Image segmentation and edge detection are the basic techniques of computer vision and their performances are vital for the result of the system. In the edge detection module, we study a new adaptive smooth algorithm, whose critical parameter is automatically selected in terms of entropy theory, and the threshold segmentation algorithm based on maximal total fuzzy entropy criterion. The experiments have demonstrated that they are superior to the traditional techniques. In the breakpoint detection module, we develop three techniques of breakpoint detection that are based on linear Fisher discrimination function, maximal Bhattacharyya-distance function, and wavelet analysis, respectively. In order to obtain the position information from the scene images accurately, the camera must be calibrated a priori. We develop a calibration algorithm, which doesn't need any precise equipment but an easily made calibration board. And the calibration technology is found very effective during experiments. In the last module, according to the special environment of container stevedore, we develop several techniques of single-view 3D reconstruction, including the three-point, algorithm, the method based on four points lying at different planes or at the same plane. Furthermore, we develop the dimension-reduced EVD algorithm of two-view 3D reconstruction, which can efficiently suppress the effect from noise and give unbiased parameter estimation with small MSE. In addition, the algorithm is much simpler than and faster than the regularized algorithm developed by Hartley, so it is prevalent in practice. And it is one of the most important parts of this paper.Considering the vision guidance technique in this paper is developed for container-stevedore, the environment is very hostile and the stevedore must be real-time, we put the emphasis on the robustness and the real-time performance of each algorithm. Of course, there are still a lot of steps to be done before those techniques can be successfully applied in practice. But considering the present study situation in China, the work of the paper not only has attributed to the development of computer vision theory, but also has somepractical significance.