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

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 env...

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Autores principales: Jiang, Ailian, Ahamed, Tofael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223392/
https://www.ncbi.nlm.nih.gov/pubmed/37430726
http://dx.doi.org/10.3390/s23104808
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author Jiang, Ailian
Ahamed, Tofael
author_facet Jiang, Ailian
Ahamed, Tofael
author_sort Jiang, Ailian
collection PubMed
description 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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spelling pubmed-102233922023-05-28 Navigation of an Autonomous Spraying Robot for Orchard Operations Using LiDAR for Tree Trunk Detection Jiang, Ailian Ahamed, Tofael Sensors (Basel) Article 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. MDPI 2023-05-16 /pmc/articles/PMC10223392/ /pubmed/37430726 http://dx.doi.org/10.3390/s23104808 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jiang, Ailian
Ahamed, Tofael
Navigation of an Autonomous Spraying Robot for Orchard Operations Using LiDAR for Tree Trunk Detection
title Navigation of an Autonomous Spraying Robot for Orchard Operations Using LiDAR for Tree Trunk Detection
title_full Navigation of an Autonomous Spraying Robot for Orchard Operations Using LiDAR for Tree Trunk Detection
title_fullStr Navigation of an Autonomous Spraying Robot for Orchard Operations Using LiDAR for Tree Trunk Detection
title_full_unstemmed Navigation of an Autonomous Spraying Robot for Orchard Operations Using LiDAR for Tree Trunk Detection
title_short Navigation of an Autonomous Spraying Robot for Orchard Operations Using LiDAR for Tree Trunk Detection
title_sort navigation of an autonomous spraying robot for orchard operations using lidar for tree trunk detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223392/
https://www.ncbi.nlm.nih.gov/pubmed/37430726
http://dx.doi.org/10.3390/s23104808
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