• 제목/요약/키워드: Farm vehicle navigation

검색결과 5건 처리시간 0.017초

RESEARCH ON AUTONOMOUS LAND VEHICLE FOR AGRICULTURE

  • Matsuo, Yosuke;Yukumoto, Isamu
    • 한국농업기계학회:학술대회논문집
    • /
    • 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
    • /
    • pp.810-819
    • /
    • 1993
  • An autonomous lan vehicle for agriculture(ALVA-II) was developed. A prototype vehicle was made by modifying a commercial tractor. A Navigation sensor system with a geo-magnetic sensor performed the autonomous operations of ALVA-II, such as rotary tilling with headland turnings. A navigation sensor system with a machine vision system was also investigated to control ALVA-II following a work boudnary.

  • PDF

자동 자기 왜곡보정 방위센서 개발 (Development of Auto-Tuning Geomagnetic Compass)

  • 김상철;이용범;한길수;임동혁;최홍기;박우풍;이운용
    • Journal of Biosystems Engineering
    • /
    • 제33권1호
    • /
    • pp.58-62
    • /
    • 2008
  • The need for position information in agriculture is gradually increasing for precise control farm vehicle and effective manage farm land. Though geomagnetic sensor has a lot of merits in estimating heading angle of vehicle because of low costs and sensing ability of magnetic north, it is easy that sensor outputs are distorted in electro magnetic field environment. This study was conducted to develop geomagnetic compass which could be available in measuring relative position from reference point correcting output distorted by external electro magnetic field in a small scale field. Magnetic inducing sensor (PNI's Vector2X) which wound enamel coated copper coil on ferrite core in order to measure and correct earth magnetic field. Magnetic azimuth was corrected using the algorithm which estimated amount of magnetic distortion from the difference between each outputs of magnetic sensors that located on the cross shaped base. Developed auto-tuning magnetic sensor was showed less then 5% as bearing accuracy in the strong magnetic field.

무인비행체를 이용한 방목형 목장관리 시스템 (A Farm management System Using Drone)

  • 정념;김상훈
    • 디지털콘텐츠학회 논문지
    • /
    • 제18권5호
    • /
    • pp.889-894
    • /
    • 2017
  • 본 논문은 방목형 축산관리의 효율성을 극대화하기 위해 무인비행체 기반의 자동항법과 근거리무선통신망 기술, 자동이착륙 시스템을 및 개체관리 운영 앱을 통하여 스마트팜 구현에 목적이 있다. 산지생태축산 활성화를 위하여 축산 ICT 융합 기술을 접목한 방목 목장 관리 시스템은 방목 가축의 생산성 향상과 우수품질을 생산하는 인프라 조성과 FTA에 대응하는 축산 경쟁력 확보에 기여할 것으로 예상된다. 축산업에 종사하는 부족한 인력을 대체하는 기술로 농가에 보급을 통해 경쟁력 향상에 기여할 것이다.

다중 GPS 수신기에 의한 농업용 차량의 정밀 위치 계측(I) - 오차추정 시뮬레이션 및 고정위치계측 - (Precise Positioning of Farm Vehicle Using Plural GPS Receivers - Error Estimation Simulation and Positioning Fixed Point -)

  • 김상철;조성인;이승기;이운용;홍영기;김국환;조희제;강지원
    • Journal of Biosystems Engineering
    • /
    • 제36권2호
    • /
    • pp.116-121
    • /
    • 2011
  • This study was conducted to develop a robust navigator which could be in positioning for precision farming through developing a plural GPS receiver with 4 sets of GPS antenna. In order to improve positioning accuracy by integrating GPS signals received simultaneously, the algorithm for processing plural GPS signal effectively was designed. Performance of the algorithm was tested using a simulation program and a fixed point on WGS 84 coordinates. Results of this study are aummarized as followings. 1. 4 sets of lower grade GPS receiver and signals were integrated by kalman filter algorithm and geometric algorithm to increase positioning accuracy of the data. 2. Prototype was composed of 4 sets of GPS receiver and INS components. All Star which manufactured by CMC, gyro compass made by KVH, ground speed sensor and integration S/W based on RTOS(Real Time Operating System)were used. 3. Integration algorithm was simulated by developed program which could generate random position error less then 10 m and tested with the prototype at a fixed position. 4. When navigation data was integrated by geometrical correction and kalman filter algorithm, estimated positioning erros were less then 0.6 m and 1.0 m respectively in simulation and fixed position tests.

특징 융합을 이용한 농작물 다중 분광 이미지의 의미론적 분할 (Semantic Segmentation of Agricultural Crop Multispectral Image Using Feature Fusion)

  • 문준렬;박성준;백중환
    • 한국항행학회논문지
    • /
    • 제28권2호
    • /
    • pp.238-245
    • /
    • 2024
  • 본 논문에서는 농작물 다중 분광 이미지에 대해 특징 융합 기법을 이용하여 의미론적 분할 성능을 향상시키기 위한 프레임워크를 제안한다. 스마트팜 분야에서 연구 중인 딥러닝 기술 중 의미론적 분할 모델 대부분은 RGB(red-green-blue)로 학습을 진행하고 있고 성능을 높이기 위해 모델의 깊이와 복잡성을 증가시키는 데에 집중하고 있다. 본 연구는 기존 방식과 달리 다중 분광과 어텐션 메커니즘을 통해 모델을 최적화하여 설계한다. 제안하는 방식은 RGB 단일 이미지와 함께 UAV (unmanned aerial vehicle)에서 수집된 여러 채널의 특징을 융합하여 특징 추출 성능을 높이고 상호보완적인 특징을 인식하여 학습 효과를 증대시킨다. 특징 융합에 집중할 수 있도록 모델 구조를 개선하고, 작물 이미지에 유리한 채널 및 조합을 실험하여 다른 모델과의 성능을 비교한다. 실험 결과 RGB와 NDVI (normalized difference vegetation index)가 융합된 모델이 다른 채널과의 조합보다 성능이 우수함을 보였다.