Design of End-effector for Kiwifruit Harvesting Robot Experiment

Mu, Longtao; Liu, Yadong; Cui, Yongjie; Liu, Haibin; Chen, Lipeng; Fu, Longsheng; Gejima, Yoshinorib

doi:10.13031/aim.201700666

Cited by 15 publications

(4 citation statements)

References 5 publications

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“…An arbitrary ant takes the 3D coordinates of a random citrus as its initial position and walks towards the next target according to the transfer probability until all the coordinates are traversed, as shown in Equation (6).…”

Section: Planning the Picking Sequence For Citrus Based On An Improve...mentioning

confidence: 99%

“…In terms of accurate fruit identification and localization, traditional machine vision algorithms have poor robustness in complex scenes, and it is difficult to meet the needs of practical work. In recent years, researchers across the world have conducted numerous studies on robotic picking of fruits and vegetables, including grapes (Luo et al, 2016) [4], apples (Sarabu et al, 2019) [5],kiwifruits (Mu et al, 2017) [6], citrus fruits (Xiong et al, 2018) [7], and mushrooms (Huang et al, 2021) [8]. More of them use the convolution neural network algorithm.…”

Section: Related Workmentioning

confidence: 99%

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Recognition of citrus fruit and planning the robotic picking sequence in orchards

Liu

Fang

et al. 2023

Preprint

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To improve the operational efficiency of and to prevent possible collision damage in the picking of citruses by robots in densely planted complex orchards, this study proposes an algorithm based on YOLOv5 for recognizing citruses and planning a picking sequence. First, the convolutional block attention module (CBAM) is embedded into YOLOv5 to improve the feature extraction capability of the network and mitigate missed detection of occluded targets and small targets. Simultaneously, the bounding loss function is optimized to improve the positioning accuracy of the bounding box. This combined model is used to recognize and localize citruses. Then, a three-dimensional model of citrus fruit was established, and an adaptive pheromone concentration updating mechanism was introduced on the basis of the ant colony algorithm to dynamically judge the picking order of citruses and determine the optimal picking sequence. We show the quantitative and qualitative results of our method in comparison to previous methods. In tests, the F1-score of this method for citrus in a densely planted environment was 92.41%, which is a 2.81% improvement compared to YOLOv5. Compared to stochastic planning, the proposed method can plan the picking sequence of citruses in the field of view in advance and shorten the picking path. In addition, extensive sequence planning experiments on other fruits validate the superior of the proposed method. Therefore, the method in this paper may provide new solutions for citrus anti-collision picking and orchard yield forecasting and new ideas for the intelligent fruit industry.

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Section: Planning the Picking Sequence For Citrus Based On An Improve...mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Recognition of citrus fruit and planning the robotic picking sequence in orchards

Liu

Fang

et al. 2023

Preprint

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show abstract

“…Li [31] of Nanjing Agricultural University introduced a fruit catcher cut off by a tooth-like structure made by a blade, and the appearance of the whole fruit catcher looks like a tubular structure, as shown in Figure 2. As soon as the toothed fruit catcher is rotated, the fruit stalk will be cut off and subsequently, the apple will be entered into the fruit catcher, which can be seen that such an actuator has good versatility and high picking efficiency, and the damage rate is also low.…”

Section: Rigid Separation Typementioning

confidence: 99%

Review of rigid fruit and vegetable picking robots

Zhao,

Wan,

Duo

2023

International Journal of Agricultural and Biological Engineering

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The important indicators to measure the goodness of rigid fruit and vegetable picking robot have two aspects, the first is the reasonable structural design of the end-effector, and the second is having a high precision positioning recognition method. Many researchers have done a lot of work in these two aspects, and a variety of end-effector structures and advanced position recognition methods are constantly appearing in people's view. The working principle, structural characteristics, advantages and disadvantages of each end-effector are summarized to help us design better fruit and vegetable picking robot. The authors start from the rigid fruit and vegetable picking robot grasping methods, separation methods, and position recognition methods, firstly introduce three different grasping methods and the characteristics of the two separation methods, then introduce the under-driven picking robot developed on the basis of the traditional rigid picking robot, then explain the single special, multi-feature, and deep learning location position recognition methods currently used, and finally make a summary and outlook on the rigid fruit and vegetable picking robot from the structural development and position recognition methods.

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“…The end effector would approach a kiwifruit, the fiber sensor would detect the fruit between the fingers, and then the motor would initiate a downward movement to detach it. The design of the proposed end effector was previously analyzed in [71]. Performance evaluation revealed that the combination of fiber sensors, pressure sensors and adjusting spring for fruit size and shape tolerance could ensure a nondestructive accurate end effector harvesting system.…”

Section: Sweet Peppersmentioning

confidence: 99%

An Overview of End Effectors in Agricultural Robotic Harvesting Systems

et al. 2022

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In recent years, the agricultural sector has turned to robotic automation to deal with the growing demand for food. Harvesting fruits and vegetables is the most labor-intensive and time-consuming among the main agricultural tasks. However, seasonal labor shortage of experienced workers results in low efficiency of harvesting, food losses, and quality deterioration. Therefore, research efforts focus on the automation of manual harvesting operations. Robotic manipulation of delicate products in unstructured environments is challenging. The development of suitable end effectors that meet manipulation requirements is necessary. To that end, this work reviews the state-of-the-art robotic end effectors for harvesting applications. Detachment methods, types of end effectors, and additional sensors are discussed. Performance measures are included to evaluate technologies and determine optimal end effectors for specific crops. Challenges and potential future trends of end effectors in agricultural robotic systems are reported. Research has shown that contact-grasping grippers for fruit holding are the most common type of end effectors. Furthermore, most research is concerned with tomato, apple, and sweet pepper harvesting applications. This work can be used as a guide for up-to-date technology for the selection of suitable end effectors for harvesting robots.

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Design of End-effector for Kiwifruit Harvesting Robot Experiment

Cited by 15 publications

References 5 publications

Recognition of citrus fruit and planning the robotic picking sequence in orchards

Recognition of citrus fruit and planning the robotic picking sequence in orchards

Review of rigid fruit and vegetable picking robots

An Overview of End Effectors in Agricultural Robotic Harvesting Systems

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