Design and simulation of an integrated end-effector for picking kiwifruit by robot

Mu, Longtao; Cui, Gongpei; Liu, Yadong; Cui, Yongjie; Fu, Longsheng; Gejima, Yoshinori

doi:10.1016/j.inpa.2019.05.004

Cited by 75 publications

(52 citation statements)

References 21 publications

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“…Chen et al [42] analyzed the selected parameters of fruit contact with the fingers of human hands when sorting fruit. Mu et al [6] presented the effects of a two-finger gripper for harvesting kiwi, which uses pressure sensors to minimize damage. De Preter et al [43] presented the effects of a strawberry harvesting robot in which a finger gripper was installed.…”

Section: Related Work Connected With the End-effectormentioning

confidence: 99%

“…Zones and Directions 1 for Applying Suction Cups 0.0065 (1) 0.0062 (1) 0.0210 (4) 0.0118 (3) 0.0085 (1) (2) 0.0402 (6) 0.0095 (2) 0.0074 (1) (2) 0.0289 (5) (..) Homogenous groups.…”

Section: Stress (Mpa)mentioning

confidence: 99%

“…Mu et al [6] conducted tests on the collection of kiwi fruit, achieving over a 94% success rate, while the time of harvesting one fruit was 4 to 5 seconds. Birrell et al [7] demonstrated the results of field tests of a constructed vision system that uses two integrated neural networks.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the Pneumatic System Parameters of the Suction Cup Integrated with the Head for Harvesting Strawberry Fruit

Kurpaska

Sobol

Pedryc

et al. 2020

Sensors

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Fruit and vegetable harvest efficiency depends on the mechanization and automation of production. The available literature lacks the results of research on the applicability of pneumatic end effectors among grippers for the robotic harvesting of strawberries. To determine their practical applications, a series of tests was performed. They included the determination of the morphological indicators of the strawberry, fruit suction force, the real stress exerted by fruit suckers and the degree of fruit damage. The fruits’ morphological indicators included the relationships between the weight and geometrical dimensions of the tested fruit, the equivalent diameter, and the sphericity coefficient. The fruit suction force was determined on a stand equipped with a vacuum pump, and control and measurement instruments, as well as a MTS 2 testing machine. The necrosis caused by tissue damage to the fruits by suction cup adhesion was assessed by counting the necrosis surface areas using the LabView programme. The assessment of the necrosis was conducted immediately upon the test’s performance, after 24 and after 72h. The stress values were calculated by referring the values of the suction forces obtained to the surface of the suction cup face. The tests were carried out with three constructions of suction cups and three positions of suction cup faces on the fruits’ surface. The research shows that there is a possibility for using pneumatic suction cups in robotic picking heads. The experiments performed indicate that the types of suction cups constructions, and the zones and directions of the suction cups’ application to the fruit significantly affect the values of the suction forces and stresses affecting the fruit. The surface areas of the necrosis formed depend mainly on the time that elapses between the test and their assessment. The weight of strawberry fruit in the conducted experiment constituted from 13.6% to 23.1% of the average suction force.

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Section: Related Work Connected With the End-effectormentioning

confidence: 99%

“…Zones and Directions 1 for Applying Suction Cups 0.0065 (1) 0.0062 (1) 0.0210 (4) 0.0118 (3) 0.0085 (1) (2) 0.0402 (6) 0.0095 (2) 0.0074 (1) (2) 0.0289 (5) (..) Homogenous groups.…”

Section: Stress (Mpa)mentioning

confidence: 99%

See 1 more Smart Citation

Analysis of the Pneumatic System Parameters of the Suction Cup Integrated with the Head for Harvesting Strawberry Fruit

Kurpaska

Sobol

Pedryc

et al. 2020

Sensors

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“…To respond to the different functional requirements for the robot, the design of different structures are needed, and many scholars have carried out the research on the grasping mechanism of the robot, including the design and development of a mechanical arm for a transplanter to process paper can seedlings [7], and the design of a stable and reliable grabbing mechanism [8] for some agricultural product bags, such as tight packing, large deformation and easy damage. Concurrently, some researchers also designed and analyzed the end actuator [9,10]. So far, certain achievements have been made in the structural design of agricultural robots in grasping, moving and other motions.…”

Section: Introductionmentioning

confidence: 99%

Path Tracking Control of Field Information-Collecting Robot Based on Improved Convolutional Neural Network Algorithm

Zhang

et al. 2020

Sensors

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Due to the narrow row spacing of corn, the lack of light in the field caused by the blocking of branches, leaves and weeds in the middle and late stages of corn growth, it is generally difficult for machinery to move between rows and also impossible to observe the corn growth in real time. To solve the problem, a robot for corn interlines information collection thus is designed. First, the mathematical model of the robot is established using the designed control system. Second, an improved convolutional neural network model is proposed for training and learning, and the driving path is fitted by detecting and identifying corn rhizomes. Next, a multi-body dynamics simulation software, RecurDyn/track, is used to establish a dynamic model of the robot movement in soft soil conditions, and a control system is developed in MATLAB/SIMULINK for joint simulation experiments. Simulation results show that the method for controlling a sliding-mode variable structure can achieve better control results. Finally, experiments on the ground and in a simulated field environment show that the robot for field information collection based on the method developed runs stably and shows little deviation. The robot can be well applied for field plant protection, the control of corn diseases and insect pests, and the realization of human–machine separation.

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“…Computer vision systems in horticulture are used to count the number of fruits and berries, monitor the condition of plants, control pests and diseases, topographic analysis of plantings, control various parameters of the soil and the environment [3,4]. The most important tasks when performing automated harvesting of berry and fruit crops in the field is to accurately determine the boundaries of fruits and berries, as well as to assess the degree of ripeness using a computer vision camera under various lighting conditions [5][6][7]. The purpose of the study was to develop a 3D model of an automated manipulator and an algorithm for recognizing location coordinates based on size and ripeness factors using the example of wild strawberry berries under various lighting conditions.…”

Section: Introductionmentioning

confidence: 99%

Device for robotic picking of strawberries

et al. 2020

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The article presents a device for collecting strawberry berries, presents a 3D model of an automated manipulator with a computer vision system. The overall dimensions of the manipulator (WxDxH), mm - 200x700x600, the number of degrees of freedom - 6, load capacity - 1 kg, the maximum effort to detach fruits and berries - 10-80 N. An algorithm for the operation of a computer vision system for determining the boundaries of berries and their ripeness based on size factor. The results of field experiments, measuring the area of berries of wild strawberries at different times of the day are given. It was established that the lighting conditions have a significant effect on the detection of the boundaries and area of garden strawberries, and their ripeness.

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Design and simulation of an integrated end-effector for picking kiwifruit by robot

Cited by 75 publications

References 21 publications

Analysis of the Pneumatic System Parameters of the Suction Cup Integrated with the Head for Harvesting Strawberry Fruit

Analysis of the Pneumatic System Parameters of the Suction Cup Integrated with the Head for Harvesting Strawberry Fruit

Path Tracking Control of Field Information-Collecting Robot Based on Improved Convolutional Neural Network Algorithm

Device for robotic picking of strawberries

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