http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : ( Takeshi Yusa ) (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Development of a Robot Boat for Aquatic Weed Management in Shallow Ponds
( Takeshi Yusa ),( Yutaka Kaizu ),( Kenji Imou ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1
There are 50 Ramsar sites in Japan; although efforts to conserve the wetlands and lakes are underway, deterioration of the ecosystem is a problem. Specifically, growth of the lotus plant tends to interfere with river improvement works. Lake Izunuma-Uchinuma, Miyagi prefecture, Japan, is a shallow lake with considerable levels of eutrophication, wherein lotus plants grow to cover 85% of the lake’s surface annually. These lotus plants are cut as they adversely affect the surrounding ecosystems and landscapes. However, the existing cutting methods require manual labor. Therefore, to decrease the cost of vegetation management work, we developed a robotic boat to cut the lotus plants. We used an open source system to reduce the cost of the robot system. The robot boat was developed by modifying a 2.4-m-long and 1.2-m-wide plastic boat. The boat was equipped with an electric clipper and an electric paddle propulsion system that can navigate on the surface of water with vegetation. We used Pixhawk/ArduPilot, an open source flight controller used in drones, as a navigation controller based on GNSS and IMU. We conducted lotus-cutting experiments in the months of June and August by autonomous navigation in Lake Izunuma-Uchinuma. The experimental areas were 30 × 100 m with 25 target paths at 1.2 m intervals. The experiment in June was completed within 70 min for all areas, and the experiment in August was completed within 69 min for 1/3<sup>rd</sup> of the experimental area. Although the navigation accuracy was not very high, a safe and labor-saving vegetation management method using a robot boat was achieved.
-
( Keishiro Kuma ),( Takeshi Yusa ),( Yutaka Kaizu ),( Kenji Imou ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1
Degradation of wetlands due to the overgrowth of aquatic plants is a problem in various areas; hence, vegetation management using robot boats is under development. Herein, we proposed a method to recognize aquatic plants via real-time image processing to enable the automation of a robot boat. We adopted a segmentation method using deep learning for image processing and conducted deep learning and testing on our own dataset. An NVIDIA Jetson TX 2 embedded AI computing device achieved an execution time of 1.31 fps (image size: 576 × 324 px). The traveling speed of the robot boat was considerably slow at 0.3 m s<sup>-1</sup>; hence, the boat can be implemented as a real-time system even at a processing speed of 1.31 fps.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
