2022 Fiscal Year Research-status Report
Development of Smart Viticulture System Using Autonomous Robots
| Project/Area Number |
21K14117
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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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| Keywords | Autonomous Robots / Artificial Intelligence / SLAM / Agriculture Robots |
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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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| Causes of Carryover |
The model is being robustly evaluated in simulation software. Actual fabrication of mechanical component is planned in FY2023 after finalizing the model in simulation environment and performing several tests in dynamic scenarios. A sturdy platform which can withstand the weight of robot arm, processing computer, cameras, and other sensors will be purchased and tested. The need of using additional sensors like spectral camera, high precision IMU for accurate SLAM, and long range stereo cameras were realized during the execution of the project. Therefore, budget has been allocated for special sensors like spectral cameras and high precision IMU.
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Research Products
(8 results)
