| 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 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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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 | Image classification / Deep Learning / Robot Navigation / 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 |
Experiments were done in vineyard using RGBD sensors, IMU, and Lidar sensors and data was collected in different phases of cultivation. The navigation part was finished considering the efficient tracking of people working in the vineyard using on-board and external cameras. High resolution RGB and point cloud data of grape data with and without occlusion has been collected. A small dataset has been labeled and tested using a deep network. The results show that more data is required for acceptable operation. Earlier systems used A-star path planner with obstacle avoidance. A new method with reinforcement learning based navigation is being tested on a small dataset. Labeling and processing of entire dataset will take more time and planned to complete in extension time. The following papers have been published in International conferences.
"Semantic Scene Understanding and Region Classification for Navigation of Service Robots in Care Scenarios", The Twenty-Ninth International Symposium on Artificial Life and Robotics 2024 (AROB 29th 2024), A. Ravankar et. al.
"Tracking People Across Multiple Cameras", International Conference on Communication, Computing and Data Security 2023, ICCCDS-2023, Multicon-W-2023, Thakur College of Engineering and Technology, Mumbai, India, Mumbai, India, Ryota Ishikawa, Abhijeet Ravankar.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
High resolution RGB and point cloud data of grape data with and without occlusion has been collected. A small dataset has been labeled and tested using a deep network. Data has also been collected for reinforcement learning of mobile robots in vineyard scenarios. This allows robots to work in very new and very dynamic scenarios with obstacles. The results show that more data is required for acceptable operation. Labeling and processing of entire dataset will take more time and planned to complete in extension time.
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| Strategy for Future Research Activity |
The plan for this year is to process the high resolution RGB and point cloud data of grape data with and without occlusion. The first task is labeling of the data, and later processing of entire dataset will be done. With the new dataset, a new reinforcement model will be trained for robot navigation. The classification of grape data based on clusters will enable to find the best cutting location in real-time. Moreover PCD data will enable to filter occlusion. The system will be integrated will all the sub modules of navigation, actuators, SLAM, grape classification and operation. The system will be tested in this fiscal year.
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