Greenhouse intelligent mobile robot vision navigation path identification method

Abstract

The invention discloses a greenhouse intelligent mobile robot vision navigation path identification method. Original image information is converted from an RGB color space to an HSI color space, H, S and I component information pictures are extracted respectively, the H component information picture is subjected to denoising processing, a K-means algorithm is used to carry out clustering segmentation on the H component information picture, and the segmentation effect picture of a road is obtained. A morphological erosion method is employed to carry out secondary denoising processing, the picture which is subjected to the erosion process is subjected to gray conversion, and the complete road information is obtained. The Candy operator edge detection is employed, edge discrete points are extracted, navigation discrete points are converted and acquired, the navigation discrete points are fitted to obtain final navigation path information which is subjected to coordinate transformation, and the navigation angle of a mobile robot is calculated. The robustness of navigation path identification to illumination inequality is effectively raised, the subsequent image processing operation is facilitated, and the rapid real-time performance of the whole path identification system is raised.

Description

technical field [0001] The invention relates to the application of the vision system of an intelligent mobile robot, in particular to a navigation path recognition method for an intelligent mobile robot operating in a greenhouse. Background technique [0002] The use of intelligent mobile robots to complete autonomous navigation operations in greenhouses can not only greatly reduce the physical labor of workers, but also prevent workers from being injured when working in harsh environments such as toxic, high temperature and high humidity. Navigation path recognition technology is one of the primary technologies to realize the autonomous navigation of greenhouse mobile robots. Compared with industrial robots, the working environment of greenhouse mobile robots is more complex, and it is usually a discrete, uncertain, diverse and inconsistent environment. In an unstructured environment, the recognition effect of the navigation path is greatly affected by the illumination info...

AI Technical Summary

Problems solved by technology

From the perspective of color space selection, although the RGB color space has the advantages of being consistent with the color methods of most image acquisition and display devices, and the color value is easy to obtain, easy to store and calculate, but because its three color components and light intensity are increasing Therefore, under the condition of variable illumination in the greenhouse environment, it is difficult to process the image directly in the RGB color space, and the image processing algorithm is difficult to meet the robustness requirements for uneven illumination; in addition, image segmentation is an important part of the navigation path acquisition of mobile robots , its segmentation effect not only directly affects the accuracy of navigation path recognition, but also determines whether the entire navigation path recognition system can meet the real-time rapidity requirements of the autonomous operation of greenhouse mobile robots

In the selection of image segmentation methods, threshold segmentation has become the most widely used image segmentation algorithm because of its advantages of simple calculation, high computing efficiency, and fast speed. The impact cannot be ignored, so for greenhouse images with complex background information, it is even more difficult to determine a unified threshold to completely separate objects from the background, which will inevitably bring inconvenience to subsequent image processing, thus affecting the autonomous navigation of greenhouse mobile robots and job performance

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