Research On Vision System And Obstacle Avoidance Planning For Manipulator Of Eggplant Harvesting Robot

Eggplant, as a common vegetable in people's life, is widely planted in all parts of China. In the study of this paper, research object is natural growth long-purple eggplant which is familiar in Jiangsu and Anhui province. Automatic recognition and location of the ripe eggplant and obstacle avoidance planning for manipulator were the research purpose in this paper. The work mainly has been accomplished as followed:1. According to the gray-level and color characteristics of natural growth eggplant image, analysis on six kinds of color-difference combination was implemented in RGB space. It was found that there was great difference between target and background among R-B, G-B and G-R. In HSI space, target and background had great difference in H channel. According to the above analysis, the auto threshold-adaptive method of Otsu operation was specially used for threshold segmentation based on genetic algorithm. And upon the elimination of the residue noise, labeling method for statistical calculation was introduced for the connected regions of the binary image. The maximum areas were preserved to improve segmentation effects. In addition, multi-indices were applied to assess the effect of segmentation, in order that to the great extent the assessment was subjective and reasonable.2. Although the effect had been achieved on eggplant image segmentation by using single threshold method, it also leaved more residue noises. The purpose of this article was to segment eggplant from its complex background. R-B, G-B and H were selected as the input-vectors of the SOFM network based on analyzing the color-difference and hue characteristics of eggplant image. The input-vectors were classified by the characteristics of self-organizing of this network. In order to make the segmentation results objective and reasonable, signal-noise ratio, area ratio, segmentation time and Fourier-Descriptor were adopted to evaluate the segmentation precision. Many SOFM network parameters were determined such as the number of input feature vector and output neurons, training steps, topology function, distance function and learning function, etc. The energy spectrum of Fourier descriptors was already adopted to evaluate the similar degree of segmentation edge. This overcame other descriptor limitations which depended on the number of edge points and initial position. The experiment demonstrated that SOFM network was better than the single-threshold segmentation and more suitable for the color image segmentation with complex background.3. One method has been introduced for recognizing the partially occluded eggplant based on improved generalized Hough transform in three-dimensional space. The shape of eggplant was approximately described by using generalized-cylinders from the view of biology. In order to describe the pose of eggplant, three main parameters such as two location parameters and a rotation parameter were selected from six factors. Different edges of projection were obtained through coordinate transformation. Scaling and rotation operation was ahead of parameter index table establishment. Shape similarity degree method was adopted to primarily select scaling index and rotation index. It could effectively reduce the searching time and improve the searching precision. 4-D parameter index tables were established to describe the shape of eggplant. Properly increasing step size of gradient index could avoid across dispersion of array in accumulator. And it was also easy to search the final reference point coordinates from potential points. Improved Generalized Hough Transform was used to count the potential pose of object and the real pose was screened by comparing the area-ratio of different rotation angles. The experiment demonstrated that it was feasible and effective to recognize different pose and partially occluded object by using Improved Generalized Hough Transform.4. The model of camera calibration was discussed and the calibration results of the left and right cameras were provided based on calibration method. The intrinsic and extrinsic parameters were obtained by the least square method with the pinhole model. The parallel model of stereo vision was selected according to the existing devices of lab. Based on the characteristics of natural growth eggplant, one effective method for selecting baseline length was introduced. The appropriate baseline length between cameras was more than 132mm and the feasible measuring distance range was from 700mm to 1200mm. According to the intrinsic and extrinsic parameters of left and right cameras, we made adjustment on the intrinsic and extrinsic parameters of cameras to fit Marr constraints, and thus the binocular stereovision system was established. We selected centroid of the target as match features. Under this condition, the measure relative error could be controlled within 2% which was satisfied with the requirements of the manipulator in agricultural environment.5. An avoiding obstacle method has been introduced for manipulator in 3D space. Obstacles were made equivalent to cylindrical-rings mathematical model which axial section was circle or rectangular and a three-dimensional planning path problem was reduced to two-dimensional one, which greatly improved real-time control performance. Obstacle transforming from Work-space to Configuration-space could directly control the robot joints so as to avoid complex coordinate translation using Jacobian inverse matrix. Path planning failure under A-star algorithm could be evaded by processing properly image matrix which mapped from C-space. Experimental results showed this algorithm which was small computational amount and good real-time performance was suitable for natural growth eggplant harvest.