Development of Smart Viticulture System Using Autonomous Robots
| Project/Area Number |
21K14117
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 20020:Robotics and intelligent system-related
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| Research Institution | Kitami Institute of Technology |
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Principal Investigator |
ラワンカル アビジート 北見工業大学, 工学部, 准教授 (70802594)
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Project Status |
Granted (Fiscal Year 2022)
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| Budget Amount *help |
¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2023: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2022: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2021: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | Autonomous Robots / Artificial Intelligence / SLAM / Agriculture Robots / Robotics / Smart Agriculture / Computer Vision / 自律ロボティクス / 人工知能 |
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| Outline of Research at the Start |
北海道では，第一次産業である大規模農業においてブドウ園は重労働作業が多いことが特徴であり，農業従事者の作業軽減のためにロボットやAI技術を活用したスマート農業のニーズが非常に大きい．そこで，本研究では，ブドウ園における作業負担を減少するために自律移動ロボットによる下記の3つの重労働作業の自動化を目的とする：
(1) GPSを使用せずに安価なセンサーでマップ生成及び自己位置推定のシステム開発．
(2) 自律ロボットによる果樹の真下にある雑草まで除草するシステムの開発．
(3) 自律ロボットによる収穫と剪定システムの開発．
本研究で開発するシステムは，北海道のブドウ園で実証実験を行う予定である．
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| Outline of Annual Research Achievements |
This research aims at developing a smart viticulture system through the use of autonomous robots and AI. Smart viticulture aims at reducing the burden of farmers by automating tasks like vineyard monitoring, inspection, yield estimation, and harvesting of grapes.
In FY2022, the accuracy of the monitoring system developed in previous FY was improved through the use of Deep Learning based landmark detection. The previously developed system relied on landmark (pillar) detection based on color, which caused inaccuracies during the change of illumination conditions and rain. A convolutional neural network based model was trained and evaluated to be robust against dynamic changes in the environment and gave better estimation of the landmarks. This improved the overall SLAM (Simultaneous Localization and Mapping) module which was developed without the use of GPS, and relies on local-only features for cost-cutting. In Addition, a deep learning CNN model was developed for detecting grapes/branches and their conditions (ripe, almost-ripe, raw). A weed detection model has been developed. The harvesting module is developed in simulator and a real-world manufacturing and test is planned for FY2023. Algorithms for robot navigation in structured vineyard environment, obstacle avoidance, alarm system, multi-robot cooperation algorithms for vineyard, have also been developed and results have been published in conferences.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
Smart Viticulture project is realized through Software component and Hardware component. The Software component comprises of robust landmark detection, pose estimation, precise SLAM development, navigation algorithms for narrow lanes of vineyard, deep-learning algorithms for grape/weed detection, etc. The software component is being developed as per the plan and several modules have been completed and tested. Important datasets for harvesting, cutting, and navigation have been collected. The datasets recorded in the previous years have improved the accuracy of detection of grapes, branches, and weeds. The hardware component comprises of developing the robot arm for harvester. Particularly, a dual-model cutter-grasper module is required for automation of harvesting. Although the mechanical component development is slightly delayed, it is important to first test the model in simulation environments before actual fabrication. A 6-DoF robot harm is being currently trained using reinforcement learning while the dual-model harvester module is being developed in simulation software. In FY2023, an actual model is planned to be developed.
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| Strategy for Future Research Activity |
The hardware component of Smart Viticulture project includes developing the robot arm for harvester/cutting. Particularly, a dual-model cutter-grasper module is required for automation of harvesting. A 6-DoF robot harm is being currently trained using reinforcement learning while the dual-model harvester module is being developed in simulation software. In FY2023, the algorithm will be improved to also include obstacles around the robot arm to prevent potential accidents. Upon finalizing the model, an actual mechanical fabrication is planned to be developed and tested in real environments. There is a need to place a spectral camera on the robot for efficient classification of nutrition and infection in the field. The arm module will be placed on the top of mobile ground robot platform which will increase the overall weight and dynamics of the system. Hence, the previously developed navigation and SLAM algorithms will be re-tested. Actual tests in vineyard environment will be tested. Additional datasets will be recorded throughout the fiscal year. The system will be integrated and tests will be performed in different conditions of weather. The results will be published in conferences.
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Report
(2 results)
Research Products
(14 results)
