Kinematics analysis and experiment of a lily picking mechanical arm

Jiang, Ao; Xiang, Yang; Cheng, Ming; Zhou, Juan

doi:10.1049/joe.2018.8265

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…The kinematics and picking experiments of the robotic arm were carried out in the experimental field in the natural environment through the robotic physics machine platform. The results show that the position error of the robot arm from the end of the arm is less than 12 mm, and the picking success rate is 83.33% [4]. The related research did not pay attention to saving the data of the manipulator, and even did not simulate the trajectory of the manipulator.…”

Section: Related Workmentioning

confidence: 99%

“…For continuous distribution functions, the inverse cumulative distribution function can be used for sampling. 4 Wireless Communications and Mobile Computing Supposing the distribution function of the sample random variable X is FðXÞ, and the function FðXÞ is continuous. Its inverse cumulative distribution function is also its inverse function, denoted as F −1 ðzÞ, 0 ≤ z ≤ 1.…”

Section: Cauchy Distribution Function Data Sampling Andmentioning

confidence: 99%

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A Data Acquisition and Processing Method for Edge Computing Robotic Arm Behavior Recognition

Zhang

Xiao

Liu

et al. 2022

Wireless Communications and Mobile Computing

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Edge computing refers to the use of an open platform on the side close to the object or data source to integrate network, computing, storage, and core application functions to provide the latest nearby services. With the development of edge computing, the cost of data acquisition has been reduced, and the efficiency has been improved. However, at present, there is no in-depth research on edge computing for robot arm behavior recognition. This paper aims to study the data acquisition and processing methods of robotic arm behavior recognition through edge computing technology. A gesture recognition method based on Cauchy distribution and grey correlation threshold is proposed, which improves the efficiency of data processing and has great research significance. In edge computing, the use of Cauchy distribution processing is more impressive; compared with empirical distribution, the algorithm optimization can reach at least 10%. Experiments show that the static gesture recognition method used in this paper is simple and high in recognition and has good robustness and the accuracy rate can basically reach more than 90%. In the case of different threshold values, when the gray correlation threshold is 0.75, the MAE value reaches the minimum value, which means that the gap between the predicted score and the actual score is the smallest, which means that the predicted result is accurate, which can prove that the recommendation of the algorithm has relatively superior performance.

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Section: Related Workmentioning

confidence: 99%

Section: Cauchy Distribution Function Data Sampling Andmentioning

confidence: 99%

A Data Acquisition and Processing Method for Edge Computing Robotic Arm Behavior Recognition

Zhang

Xiao

Liu

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…For example, the 10-degree-of-freedom under-driven three-finger hand [21][22][23][24][25][26][27][28][29][30] developed by Gosselin's group at Laval University in Canada, which is the international leader in the under-driven multi-finger hand control, uses only two motors, one motor is responsible for the grasping opening and closing motion of three fingers, and the other motor completes the finger steering. Rodriguze et al, developed a 15-degree-of-freedom single-motor-driven multi-finger hand, which can achieve safe and reliable grasping without any sensors and feedback control [31].…”

Section: Introductionmentioning

confidence: 99%

End-Effectors Developed for Citrus and Other Spherical Crops

Wang

Jiang

2022

Applied Sciences

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Citrus harvesting is time-intensive and labor-intensive, relying mainly on manual harvesting. The automatic harvesting of fruit and vegetable crops can not only reduce the physical labor of fruit farmers in the harsh field environment but also greatly improve the harvesting efficiency. Based on the principle of manual citrus picking, an end-effector with three-finger grasping is designed in this study. First, the structure of the end-effector was designed to achieve the function of stable grasping and effective cutting of citrus fruits, and then the working process and key parameters of the end-effector were explained in detail. Finally, a picking test was conducted without considering robot vision. The test results show that the end-effector has a picking success rate of 95.23% for citrus with a diameter of 30–100 mm and an average picking time of 4.65 s for a single fruit. This end-effector can realize the picking function for citrus of different sizes and shapes and has the advantages of high adaptability, stable gripping and no damage to the fruit.

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Kinematics analysis and experiment of a lily picking mechanical arm

Cited by 2 publications

References 8 publications

A Data Acquisition and Processing Method for Edge Computing Robotic Arm Behavior Recognition

A Data Acquisition and Processing Method for Edge Computing Robotic Arm Behavior Recognition

End-Effectors Developed for Citrus and Other Spherical Crops

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