Research on visual navigation path extraction method of combine harvester based on machine vision

2021 ◽  
Author(s):  
Jin Chen ◽  
Zhiping Wu ◽  
Yaoming Li ◽  
Zhenghua Xu ◽  
Kejiu Wang
Keyword(s):  
Machine Vision ◽  
Extraction Method ◽  
Visual Navigation ◽  
Navigation Path ◽  
Combine Harvester ◽  
Path Extraction

scholarly journals Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 590
Author(s):  
Zhenqian Zhang ◽  
Ruyue Cao ◽  
Cheng Peng ◽  
Renjie Liu ◽  
Yifan Sun ◽  
...  
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Detection Method ◽  
Region Of Interest ◽  
Average Error ◽  
Vertical Projection ◽  
Cut Edge ◽  
Fitting Method ◽  
Combine Harvester ◽  
Edge Detection Method

A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop row, judging the target demarcation and getting the feature points, the region of interest (ROI) was automatically gained. Subsequently, the vertical projection was applied to reduce the noise. All the points in the ROI were calculated, and a dividing point was found in each row. The hierarchical clustering method was used to extract the outliers. At last, the polynomial fitting method was used to acquire the straight or curved cut-edge. The results gained from the samples showed that the average error for locating the cut-edge was 2.84 cm. The method was capable of providing support for the automatic navigation of a combine harvester.

Research on the method of predicting feeding volume of rice combine harvester base on machine vision

2021 ◽  
Author(s):  
Jin Chen ◽  
Shengjie Fu ◽  
Zhiwen Wang ◽  
Linjun Zhu ◽  
Hui Xia
Keyword(s):  
Machine Vision ◽  
Combine Harvester

A Novel 3-D Path Extraction Method for Arc Welding Robot Based on Stereo Structured Light Sensor

2019 ◽  
Vol 19 (2) ◽  
pp. 763-773 ◽  
Author(s):  
Lei Yang ◽  
En Li ◽  
Teng Long ◽  
Junfeng Fan ◽  
Zize Liang
Keyword(s):  
Arc Welding ◽  
Extraction Method ◽  
Structured Light ◽  
Welding Robot ◽  
Light Sensor ◽  
Path Extraction

Navigation Path Detection for Cotton Field Operator Robot Based on Horizontal Spline Segmentation

2017 ◽  
Vol 12 (3) ◽  
pp. 28-41 ◽  
Author(s):  
Dongchen Li ◽  
Shengyong Xu ◽  
Yuezhi Zheng ◽  
Changgui Qi ◽  
Pengjiao Yao
Keyword(s):  
Connected Domain ◽  
Least Squares Method ◽  
Visual Navigation ◽  
Morphological Processing ◽  
Binary Image ◽  
Cotton Field ◽  
Boundary Line ◽  
Cotton Wool ◽  
Navigation Path ◽  
Starting Point

Visual navigation is one of the fundamental techniques of intelligent cotton-picking robot. Cotton field composition is complex and the presence of occlusion and illumination makes it hard to accurately identify furrows so as to extract the navigation line. In this paper, a new field navigation path extraction method based on horizontal spline segmentation is presented. Firstly, the color image in RGB color space is pre-processed by the OTSU threshold algorithm to segment the binary image of the furrow. The cotton field image components are divided into four categories: furrow (ingredients include land, wilted leaves, etc.), cotton fiber, other organs of cotton and the outside area or obstructions. By using the significant differences in hue and value of the HSV model, the authors segment the threshold by two steps. Firstly, they segment cotton wool in the S channel, and then segment the furrow in the V channel in the area outside the cotton wool area. In addition, morphological processing is needed to filter out small noise area. Secondly, the horizontal spline is used to segment the binary image. The authors detect the connected domains in the horizontal splines, and merger the isolate small areas caused by the cotton wool or light spots in the nearby big connected domains so as to get connected domain of the furrow. Thirdly, they make the center of the bottom of the image as the starting point, and successively select the candidate point from the midpoint of the connected domain, according to the principle that the distance between adjacent navigation line candidate is smaller. Finally, the authors count the number of the connected domains and calculate the change of parameters of boundary line of the connected domain to make sure whether the robot reaches the outside of the field or encounters obstacles. If there is no anomaly, the navigation path is fitted by the navigation points using the least squares method. Experiments prove that this method is accurate and effective, which is suitable for visual navigation in the complex environment of a cotton field in different phases.

INFORMATION ACQUISITION IN DESERT ANT NAVIGATION

2006 ◽  
Vol 03 (01) ◽  
pp. 33-43 ◽  
Author(s):  
JUN GAO ◽  
LEI WANG ◽  
MEI BO ◽  
ZHIGUO FAN
Keyword(s):  
Body Length ◽  
Information Acquisition ◽  
Path Integration ◽  
Research Work ◽  
Polarized Light ◽  
Visual Navigation ◽  
Desert Ant ◽  
Navigation Path ◽  
Straight Line ◽  
Ant Navigation

Desert ant (Cataglyphis) is famous for its ability in navigation. In deserts with very few visual and odor information, the ant can return to its den almost along a straight line after foraging away in a distance of much more than thousands of times longer than its body length. Several kinds of information must be acquired during its trip, and the most important two are: path integration and visual navigation. Path integration is achieved by using sky light compass based on polarized light and odometer, while visual navigation relies on landmark based memory and matching. In this paper, a survey of research work on desert ant navigation from the viewpoint of information acquisition and fusion is presented, as well as the application of these kinds of information to navigate robots, especially bionic robots cruising in strange environment.

Sign in / Sign up

Export Citation Format

Share Document