• Journal of Biosystems Engineering
• /
• 제23권6호
• /
• pp.629-634
• /
• 1998

Factory-style plant production system aims to produce the standardized horticultural products with high quality and cleanness. In Korea, researches for year-round leaf vegetables production system are in progress and the most of them are focused on environment control. Automating technologies for harvesting, transporting and grading need to be developed. A lettuce harvesting system applicable to the plant factory was studied. It was composed of an articulated robot with a cutter and a gripper, lettuce feeding conveyor and air blower. Success rate of the developed system was 94.7 %. The system carried out harvesting a lettuce smoothly and the harvesting time was about 6 seconds per lettuce. The results showed a feasibility of robotic lettuce harvesting.

• 로봇학회논문지
• /
• 제16권1호
• /
• pp.49-55
• /
• 2021

Recently, a smart farm technology is drawing attention as an alternative to the decline of farm labor population problems due to the aging society. Especially, there is an increasing demand for automatic harvesting system that can be commercialized in the market. Pre-harvest crop detection is the most important issue for the harvesting robot system in a real-world environment. In this paper, we proposed a real-time tomato instance tracking algorithm by using deep learning and probability models. In general, It is hard to keep track of the same tomato instance between successive frames, because the tomato growing environment is disturbed by the change of lighting condition and a background clutter without a stochastic approach. Therefore, this work suggests that individual tomato object detection for each frame is conducted by YOLOv3 model, and the continuous instance tracking between frames is performed by Kalman filter and probability model. We have verified the performance of the proposed method, an experiment was shown a good result in real-world test data.

• 스마트미디어저널
• /
• 제12권10호
• /
• pp.19-28
• /
• 2023

과실 수확 분야에서 다양한 계절성과 수확 비용 상승 등으로 자동 로봇 수확에 대한 관심이 증가하고 있다. 빛의 변화, 바람에 의한 진동, 나뭇잎 및 가지 겹침 등 복잡한 과수원 환경에서 정확한 사과 검출은 어려운 문제이다. 본 논문에서는 로봇 자동 사과 수확에 유리한 데이터셋과 적응형 히트맵 회귀 모델을 소개한다. 사과 데이터셋은 사과 위치뿐만 아니라 가시성을 같이 레이블링하였다. 가시성에 따라 가우시안 모양을 조절하는 적응형 히트맵 회귀 모델을 사용하여 사과 중심점을 검출하는 방법을 제안한다. 실험 결과 MAP@K가 K=5와 K=10일 때 0.9809, 0.9801로 사과 수확 로봇에 응용 가능한 성능을 나타내었다.

• 드라이브 ㆍ 컨트롤
• /
• 제21권2호
• /
• pp.65-71
• /
• 2024

In this study, we suggested a key-point detection method for robot harvesting of oriental melon. Our suggested method could be used to detect the detachment part and major composition of oriental melon. We defined four points (harvesting point, calyx, center, bottom) based on tomato with characteristics similar to those of oriental melon. The evaluation of estimated key-points was conducted by pixel error and PDK (percentage of detected key-point) index. Results showed that the average pixel error was 18.26 ± 16.62 for the x coordinate and 17.74 ± 18.07 for the y coordinate. Considering the resolution of raw images, these pixel errors were not expected to have a serious impact. The PDK score was found to be 89.5% PDK@0.5 on average. It was possible to estimate oriental melon specific key-point. As a result of this research, we believe that the proposed method can contribute to the application of harvesting robot system.

• 한국농업기계학회:학술대회논문집
• /
• 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
• /
• pp.820-829
• /
• 1993

We have developed an agricultural hydraulic robot to operate in the agricultural field. Using the robot, automatic harvesting experiments of watermelon were done. The results are as follows. First, the gripper should be modified its finger. Second, the manipulator and the gripper should be known precisely about dynamic characteristics of them in order to control adequately. Therefore, a new gripper was manufactured on trial by modifying its finger, and in order to known dynamic characteristics of the manipulator and the new gripper, the system identification was carried out with experiments.

• 한국전자통신학회논문지
• /
• 제16권4호
• /
• pp.749-754
• /
• 2021

농업인구의 감소, 고령화 등의 문제를 해결하기 위해 다양한 농작업의 자동화를 목적으로 농업용 로봇의 연구가 활발히 진행 중이다. 농가 작업 중 가장 노동력이 많이 투입되는 과정은 수확 과정으로 타 과정 대비 약 2~3배 소모된다. 농가의 수확물 이송 작업은 인건비가 가장 많이 들고 작업 중 부상의 위험성도 있기 때문에 이송 작업을 농업용 로봇을 통해 자동화시키면 안전성 향상과 더불어 생산성을 대폭 향상할 수 있다. 따라서 본 논문은 농가 작업 현장에 최적화되고 자율주행이 가능한 농업용 로봇을 제안한다.

• Journal of Biosystems Engineering
• /
• 제39권2호
• /
• pp.134-141
• /
• 2014

Purposes: This study was to validate the Robotic Smart Work System that can provides better working conditions and high productivity in unstructured environments like bio-industry, based on a tele-operation system for fruit harvesting with low cost 3-D positioning system on the laboratory level. Methods: For the Robotic Smart Work System for fruit harvesting and cultivation management in agriculture, a vision based tele-operating system and 3-D position information are key elements. This study proposed Robotic Smart Farming, an agricultural version of Robotic Smart Work System, and validated a 3-D position information system with a low cost omni camera and a laser marker system in the lab environment in order to get a vision based tele-operating system and 3-D position information. Results: The tasks like harvesting of the fixed target and cultivation management were accomplished even if there was a short time delay (30 ms ~ 100 ms). Although automatic conveyor works requiring accurate timing and positioning yield high productivity, the tele-operation with user's intuition will be more efficient in unstructured environments which require target selection and judgment. Conclusions: This system increased work efficiency and stability by considering ancillary intelligence as well as user's experience and knowhow. In addition, senior and female workers will operate the system easily because it can reduce labor and minimized user fatigue.