It is a new field for applying stereovision into guidance system. The objective was to find out a better correlation method and develop an algorithm for detecting vegetable rows for field robot. Several Area correlation methods were compared for obtaining disparities, such as sum of absolute differences and Mahalanobis Distance. A method to eliminate error matched results was also studied, which was comparing of minimum extremum and the second minimum extremum. The 3D data of fields were calculated based on the disparity images and they were matched with a trapezium model to detect vegetable rows. It shows that stereovision could obtain the landforms of fields, especially that of vegetable rows. In future, a real time tilt angle sensor might be added for more reliable 3D data.
In a machine vision-based guidance system, a camera must be corrected precisely to calculate the position of vehicle, however, it is not easy to obtain the intrinsic and extrinsic parameters of the camera, while neural nets have the advantage to set up a mapping relationship for a nonlinear system. We intended to use the CMAC neural net to construct two map relationships: image coordinates and offsets of the vehicle, and image coordinates and the heading angle of the vehicle. The net inputs were the coordinates of top and bottom points in the detected guidance line in the image coordinate system. The outputs were offsets and heading angles. The verified results show that the RMS of inferred offset is 10.5 mm, and the STD is 11.3 mm; the RMS of inferred heading is 1.1°, and the STD is 0.99°.
Automation of agricultural equipments in the near term appears both economically viable and technically feasible. This paper describes measurement and control system for agriculture robot. It consists of a computer, a pair of NIR cameras, one inclinometer, one potentionmeter and two encoders. Inclinometer, potentionmeter and encoders are used to measure obliquity of camera, turning angle of front-wheel and velocity of rear wheel, respectively. These sensor data are filtered before sending to PC. The test shows that the system can measure turning angle of front-wheel and velocity of rear wheel accurately whether robot is at stillness state or at motion state.
A stereovision-based disparity evaluation algorithm was developed for rice crop field recognition. The gray level intensities and the correlation relation were integrated to produce the disparities of stereo-images. The surface of ground and rice were though as two rough planes, but their disparities waved in a narrow range. The cut/uncut edges of rice crops were first detected and track through the images. We used a step model to locate those edge positions. The points besides the edges were matched respectively to get disparity values using area correlation method. The 3D camera coordinates were computed based on those disparities. The vehicle coordinates were obtained by multiplying the 3D camera coordinates with a transform formula. It has been implemented on an agricultural robot and evaluated in rice crop field with straight rows. The results indicated that the developed stereovision navigation system is capable of reconstructing the field image.
The trapezium models are designed for matching with the intensity outlines to locate the crop rows. Tow kinds of model were designed, single trapezium and double trapezium model. The former was applied to single grass row, while the later was applied to double maize rows. The intensity outlines were extracted by summing the intensities in each column. To locate the crop row quickly, a fast position algorithm was designed that a predigested trapezium model was constructed first according to the distribution of gray level, and then detail model located the row position accurately. The location of maximum correlation coefficients between the model and real intensity data were thought as the position of crop row. The mean correlation coefficient of single trapezium model at the location of row is 0.91, and that of double model is 0.7. This approach has been experimented on field of ZJU in real time and it is proved work robust.
It is necessary to perceive and avoid collision with obstacles, such as ridges, for an agricultural robot. In this paper we regarded weeds as the prominent feature of the ridge and used stereovision to infer their depth. The mixed moments and mixed central moments were used to characterize the weeds in two disparity images, and the Bayes’ rule was applied to segment the weeds from background. The weeds were matched based on their approximate contours. Then the disparity was the difference between the two centers of the contours, which were extracted using the method of Cartesian moments. Since the contour of weed was random, it showed that stereovision could be applied for agricultural robot to detect complex obstacles.