Navigation path extraction for greenhouse cucumber-picking robots using the prediction-point Hough transform

Chen, Jiqing; Hu, Qiang; Wu, Jiahua; Xu, Guanwen; Wang, Zhikui

doi:10.1016/j.compag.2020.105911

Cited by 56 publications

(22 citation statements)

References 41 publications

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“…In view of the problems of poor recognition of visual navigation technology and vulnerability to illumination, Gao and Ming ( 2014 ) selected the H component in the HIS color space for subsequent image processing and introduced the K-means algorithm to cluster and to segment the image for the unique color characteristic information of greenhouse. Chen et al ( 2021 ) proposed a Hough transform algorithm for the prediction point by using a new graying factor to segment cucumber plants and soil, and this proposed algorithm is used for prediction points to fit the navigation paths. This algorithm is 35.20 ms faster than the traditional Hough Transform.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Navigation System of Greenhouse Mobile Robot Based on 3D Lidar and 2D Lidar SLAM

Jiang

Wang

et al. 2022

Front. Plant Sci.

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The application of mobile robots is an important link in the development of intelligent greenhouses. In view of the complex environment of a greenhouse, achieving precise positioning and navigation by robots has become the primary problem to be solved. Simultaneous localization and mapping (SLAM) technology is a hot spot in solving the positioning and navigation in an unknown indoor environment in recent years. Among them, the SLAM based on a two-dimensional (2D) Lidar can only collect the environmental information at the level of Lidar, while the SLAM based on a 3D Lidar demands a high computation cost; hence, it has higher requirements for the industrial computers. In this study, the robot navigation control system initially filtered the information of a 3D greenhouse environment collected by a 3D Lidar and fused the information into 2D information, and then, based on the robot odometers and inertial measurement unit information, the system has achieved a timely positioning and construction of the greenhouse environment by a robot using a 2D Lidar SLAM algorithm in Cartographer. This method not only ensures the accuracy of a greenhouse environmental map but also reduces the performance requirements on the industrial computer. In terms of path planning, the Dijkstra algorithm was used to plan the global navigation path of the robot while the Dynamic Window Approach (DWA) algorithm was used to plan the local navigation path of the robot. Through the positioning test, the average position deviation of the robot from the target positioning point is less than 8 cm with a standard deviation (SD) of less than 3 cm; the average course deviation is less than 3° with an SD of less than 1° at the moving speed of 0.4 m/s. The robot moves at the speed of 0.2, 0.4, and 0.6 m/s, respectively; the average lateral deviation between the actual movement path and the target movement path is less than 10 cm, and the SD is less than 6 cm; the average course deviation is <3°, and the SD is <1.5°. Both the positioning accuracy and the navigation accuracy of the robot can meet the requirements of mobile navigation and positioning in the greenhouse environment.

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Section: Introductionmentioning

confidence: 99%

Autonomous Navigation System of Greenhouse Mobile Robot Based on 3D Lidar and 2D Lidar SLAM

Jiang

Wang

et al. 2022

Front. Plant Sci.

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“…They use PHT to extract the candidate line segments and refine the detection results by visual saliency features. More recent HT variants such as the prediction point Hough transform, the rearranged wavelet Hough transform, and the Bezier-Based Hough transform can be seen in [25], [26], and [27], respectively.…”

Section: Introductionmentioning

confidence: 99%

A new hough transform operated in a bounded cartesian coordinate parameter space

Yang

Hou

et al. 2022

IET Image Processing

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In this paper, a new Hough transform is proposed to detect lines with geometric meanings. In the standard Hough transform, lines are parameterized by the length and orientation of the normal vector from the origin to the line. The parameter space of the standard Hough transform is presented in a polar coordinate system. Geometric measurements such as distance and angle are not suitable in polar coordinate space due to the inconsistency of the axes. To deal with this problem, a Hough transform which is carried out in a bounded Cartesian coordinate parameter space is developed. The lines are represented by their perpendicular feet with respect to a fixed point. Since the parameter space is in Cartesian coordinate space, it is intuitive to the researchers. When detecting the peaks in the parameter space, distance and angle constraints can be applied. The effectiveness of the proposed method has been illustrated by two applications, that is, lane line detection and convex polygon extraction. The results show that the proposed Cartesian coordinate Hough transform is suitable for detecting meaningful lines.

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“…Chen et al proposed a new algorithm for the fitting of a navigation path for greenhouse cucumber-picking robots. The proposed prediction point, the Hough transform, fits the navigation paths with an average error less than 0.5 • , which is 10.25 • lower than the average error of the least-square method [11]. Kim et al proposed a novel path detection approach for orchards using CNN-based partial area classification.…”

Section: Introductionmentioning

confidence: 99%

Navigation Path Extraction and Experimental Research of Pusher Robot Based on Binocular Vision

Tian

et al. 2022

Applied Sciences

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The pushing robot working in the complex farming environment encounters several problems. For example, the precision of its navigation path extraction is low, and its working quality is greatly affected by the weather. In view of this, a method of farm operation path extraction based on machine vision is proposed in this study in order to solve the problems above and realize the autonomous and intelligent operation of the robot. First of all, the RGB images of the working area in front of the robot are obtained by using an RGB camera installed on the machine. Then, the collected images are preprocessed by means of sky removal, denoising and grayscale transformation. After that, the image is segmented to obtain the front fence, feed belt and ground data. Finally, the navigation path is obtained by extracting the features of the feed belt. The test results show that the absolute deviation of the pushing robot at different initial lateral distances is less than ±15 cm, and the deviation between the actual navigation route and the target route is within the expected range. The absolute value of the maximum lateral deviation in five test areas is 8.9 cm, and the absolute value of the average maximum lateral deviation is 7.6 cm. These experimental results show that the pushing robot can work stably without disturbing the feeding of cows. Particle swarm optimization is used to optimize the parameters of the PID and find the optimal parameters. This makes the system balanced and more responsive. Through this test, it is found that the initial direction of the robot will have a certain impact on the path production and tracking efficiency, and this effect is more significant when the robot changes the working area or turns. In which case, the trajectory of the robot should be in such a way that it immediately faces the next row at a small angular deviation, thus ensuring smoother motion. The method proposed in this study can provide support for the automatic navigation of pushing robots in dairy farms.

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Navigation path extraction for greenhouse cucumber-picking robots using the prediction-point Hough transform

Cited by 56 publications

References 41 publications

Autonomous Navigation System of Greenhouse Mobile Robot Based on 3D Lidar and 2D Lidar SLAM

Autonomous Navigation System of Greenhouse Mobile Robot Based on 3D Lidar and 2D Lidar SLAM

A new hough transform operated in a bounded cartesian coordinate parameter space

Navigation Path Extraction and Experimental Research of Pusher Robot Based on Binocular Vision

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