Development of a tomato harvesting robot used in greenhouse

A tomato harvesting robot was developed in this study, which consisted of a four-wheel independent steering system, a 5-DOF harvesting system, a navigation system, and a binocular stereo vision system. The four-wheel independent steering system was capable of providing a low-speed steering control o...

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Vydané v:International journal of agricultural and biological engineering Ročník 10; číslo 4; s. 140 - 149
Hlavní autori: Lili, Wang, Bo, Zhao, Jinwei, Fan, Xiaoan, Hu, Shu, Wei, Yashuo, Li, Qiangbing, Zhou, Chongfeng, Wei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Beijing International Journal of Agricultural and Biological Engineering (IJABE) 2017
Predmet:
Agriculture
Algorithms
Bearing capacity
Binocular vision
Computers
Control systems
Electronics
Fruits
Greenhouses
Harvest
Harvesting
International conferences
Laser guidance
Mechanical engineering
Navigation systems
Neural networks
Obstacle avoidance
Proportional integral derivative
Recognition
Robotics
Robots
Steering
Success
Tomatoes
Vegetables
Vision systems
China
ISSN:1934-6344, 1934-6352
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Shrnutí:A tomato harvesting robot was developed in this study, which consisted of a four-wheel independent steering system, a 5-DOF harvesting system, a navigation system, and a binocular stereo vision system. The four-wheel independent steering system was capable of providing a low-speed steering control of the robot based on Ackerman steering geometry. The proportional-integral-derivative (PID) algorithm was used in the laser navigation control system. The Otsu algorithm and the elliptic template method were used for the automatic recognition of ripe tomatoes, and obstacle avoidance strategies were proposed based on the C-space method. The maximum average absolute error between the set angle and the actual angle was about 0.14°, and the maximum standard deviation was about 0.04°. The laser navigation system was able to rapidly and accurately track the path, with the deviation being less than 8 cm. The load bearing capacity of the mechanical arm was about 1.5 kg. The success rate of the binocular vision system in the recognition of ripe tomatoes was 99.3%. When the distance was less than 600 mm, the positioning error was less than 10 mm. The time needed for recognition of ripe tomatoes and pitching was about 15 s per tomato, with a success rate of about 86%. This study provides some insights into the development and application of tomato harvesting robot used in the greenhouse.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1934-6344
1934-6352
DOI:10.25165/j.ijabe.20171004.3204