Information Perception Method for Fruit Trees Based on 2D LiDAR Sensor

Wang, Yong; Geng, Changxing; Zhu, Guo‐Qiang; Shen, Renyuan; Gu, Haiyang; Liu, Wanfu

doi:10.3390/agriculture12070914

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…There are several methods used to process LiDAR data. The density-based clustering algorithm (DBSCAN) is capable of detecting arbitrary shapes of clusters in spaces of any dimension, and this method is very suitable for LiDAR data segmentation [ 15 ]. To set the parameter searching radius and to make it more consistent with different point cloud environments, automatic searching radius ε estimation was proposed based on the average of nearest neighbors’ maximum distance [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Navigation of an Autonomous Spraying Robot for Orchard Operations Using LiDAR for Tree Trunk Detection

Jiang

Ahamed

2023

Sensors

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Traditional Japanese orchards control the growth height of fruit trees for the convenience of farmers, which is unfavorable to the operation of medium- and large-sized machinery. A compact, safe, and stable spraying system could offer a solution for orchard automation. Due to the complex orchard environment, the dense tree canopy not only obstructs the GNSS signal but also has effects due to low light, which may impact the recognition of objects by ordinary RGB cameras. To overcome these disadvantages, this study selected LiDAR as a single sensor to achieve a prototype robot navigation system. In this study, density-based spatial clustering of applications with noise (DBSCAN) and K-means and random sample consensus (RANSAC) machine learning algorithms were used to plan the robot navigation path in a facilitated artificial-tree-based orchard system. Pure pursuit tracking and an incremental proportional–integral–derivative (PID) strategy were used to calculate the vehicle steering angle. In field tests on a concrete road, grass field, and a facilitated artificial-tree-based orchard, as indicated by the test data results for several formations of left turns and right turns separately, the position root mean square error (RMSE) of this vehicle was as follows: on the concrete road, the right turn was 12.0 cm and the left turn was 11.6 cm, on grass, the right turn was 12.6 cm and the left turn was 15.5 cm, and in the facilitated artificial-tree-based orchard, the right turn was 13.8 cm and the left turn was 11.4 cm. The vehicle was able to calculate the path in real time based on the position of the objects, operate safely, and complete the task of pesticide spraying.

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Section: Introductionmentioning

confidence: 99%

Navigation of an Autonomous Spraying Robot for Orchard Operations Using LiDAR for Tree Trunk Detection

Jiang

Ahamed

2023

Sensors

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“…The first category has eleven papers under the following sub-heading: Intelligent sensing for the crop or machine system [1,4,7,9,10,12,16,17,22,23,26]. Currently, a large number of studies focus on deep learning techniques, which have shown their superb impact on robotic sensing applications, as reflected in this issue.…”

mentioning

confidence: 99%

“…The paper by Liu et al [10] proposed a 3D localization algorithm to fuse the depth information based on multiangle image matching and YOLOv5 detection information. Some papers utilized the manual features combined with machine learning, such as the adaptive recognition boundary model [4], density-based lightning connection clustering [22], random forest [17], etc., to achieve target detection, due to a small training dataset or more efficient features.…”

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confidence: 99%

Robots and Autonomous Machines for Sustainable Agriculture Production

Yuan

Feng

2023

Agriculture

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Design of an Autonomous Orchard Navigation System Based on Multi-Sensor Fusion

Su,

Zou,

Zhai

et al. 2024

Agronomy

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To address the limitations of traditional GNSS-based navigation systems in orchard environments, we propose a multi-sensor fusion-based autonomous navigation method for orchards. A crawler-type agricultural platform was used as a test vehicle, and an autonomous orchard navigation system was constructed using a 2D LiDAR, a dynamic electronic compass, and an encoder. The proposed system first filters LiDAR point cloud data and uses the DBSCAN–ratio–threshold method to process data and identify clusters of tree trunks. By matching the center coordinates of trunk clusters with a fruit tree distribution map, the platform’s positional measurements are determined. An extended Kalman filter fusion algorithm is then employed to obtain a posterior estimate of the platform’s position and pose. Experimental results demonstrate that in localization accuracy tests and navigation tests, the proposed system provides high navigation accuracy and robustness, making it suitable for autonomous walking operations in orchard environments.

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Information Perception Method for Fruit Trees Based on 2D LiDAR Sensor

Cited by 3 publications

References 25 publications

Navigation of an Autonomous Spraying Robot for Orchard Operations Using LiDAR for Tree Trunk Detection

Navigation of an Autonomous Spraying Robot for Orchard Operations Using LiDAR for Tree Trunk Detection

Robots and Autonomous Machines for Sustainable Agriculture Production

Design of an Autonomous Orchard Navigation System Based on Multi-Sensor Fusion

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