A vision methodology for harvesting robot to detect cutting points on peduncles of double overlapping grape clusters in a vineyard

Luo, Liang; Tang, Yunchao; Lu, Qinghua; Xiong, Chen; Zhang, Po; Zou, Xiangjun

doi:10.1016/j.compind.2018.03.017

Cited by 117 publications

(51 citation statements)

References 21 publications

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“…The 3D localization allows a cutting point and a grasp to be determined. Currently, some work has been done to detect the peduncle based on the color information using RGB cameras [6,7,29,30]; however, such cameras are not capable of discriminating between peduncles, leaves, and crops if they are in the same color [7]. The work in [31] proposed a dynamic thresholding to detect apples in viable lighting conditions, and they further used a dynamic adaptive thresholding algorithm [32] for fruit detection using a small set of training images.…”

Section: Perceptionmentioning

confidence: 99%

“…First, cutting at the peduncle leads to higher success rates for crop detachment, and detachment at the peduncle reduces the risk of damaging the flesh or other stems of the plant and maximizes the storage life. However, peduncle detection is still a challenging step due to varying lighting conditions and occlusion of leaves or other crops [5], as well as similar colors of peduncle and leaves [6,7]. Second, existing manipulation tools that realize both grasping and cutting functions usually require a method of additional detachment [8,9], which increases the cost of the entire system.…”

Section: Introductionmentioning

confidence: 99%

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An Autonomous Fruit and Vegetable Harvester with a Low-Cost Gripper Using a 3D Sensor

Zhang

Huang

You

et al. 2019

Sensors

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Reliable and robust systems to detect and harvest fruits and vegetables in unstructured environments are crucial for harvesting robots. In this paper, we propose an autonomous system that harvests most types of crops with peduncles. A geometric approach is first applied to obtain the cutting points of the peduncle based on the fruit bounding box, for which we have adapted the model of the state-of-the-art object detector named Mask Region-based Convolutional Neural Network (Mask R-CNN). We designed a novel gripper that simultaneously clamps and cuts the peduncles of crops without contacting the flesh. We have conducted experiments with a robotic manipulator to evaluate the effectiveness of the proposed harvesting system in being able to efficiently harvest most crops in real laboratory environments. Sensors 2020, 20, 93 2 of 15 the crop peduncle based on the Mask Region-based Convolutional Neural Network (Mask R-CNN) algorithm [10].The rest of this paper is organized as follows: In Section 2, we introduce related work. In Section 3, we describe the design of the gripper and provide a mechanical analysis. In Section 4, we present the approach of cutting-point detection. In Section 5, we describe an experimental robotic system to demonstrate the harvesting process for artificial and real fruits and vegetables. Conclusions and future work are given in Section 6. Related WorkDuring the last few decades, many systems have been developed for the autonomous harvesting of soft crops, ranging from cucumber [11] and tomato-harvesting robots [12] to sweet-pepper [2,13] and strawberry-picking apparatus [14,15]. The work in [16] demonstrated a sweet-pepper-harvesting robot that achieved a success rate of 6% in unstructured environments. This work highlighted the difficulty and complexity of the harvesting problem. The key research challenges include manipulation tools along with the harvesting process, and perception of target fruits and vegetables with the cutting points.

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Section: Perceptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Autonomous Fruit and Vegetable Harvester with a Low-Cost Gripper Using a 3D Sensor

Zhang

Huang

You

et al. 2019

Sensors

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“…A number of color-pattern recognition methods have emerged in the field of strawberry field-image processing, for example, the K-Nearest Neighbor algorithm, Principle Component Analysis, Linear Discriminant Analysis, and Non-Negative Matrix Factorization (Wu et al, 2017). Luo et al (2018) designed a vision system to detect cutting points on the peduncles of double-overlapping grape clusters in a vineyard; they used three main steps to detect the cutting pointnamely, K-means clustering, edge detection, and geometric information decision-making-and demonstrated the effective practical performance of the system. Wang et al (2017) combined supervised classification technology with a geometric centerbased matching method and built a recognition and matching system for mature litchi fruits.…”

Section: Introductionmentioning

confidence: 99%

A Novel Greenhouse-Based System for the Detection and Plumpness Assessment of Strawberry Using an Improved Deep Learning Technique

Zhou

et al. 2020

Front. Plant Sci.

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“…In recent years, robot mechanisms have been used in almost every field in the industry, such as automotive technology, space exploration, rescue operations etc. Besides the industry, in the literature, it is seen that robotic mechanisms are also designed for using in special applications such as medical [1]- [2], agricultural [3] or mine rescue [4] applications. Serial robotic mechanisms consisting of rigid bodies that connected by revolute or prismatic joints are the most well-known robot mechanisms.…”

Section: Introductionmentioning

confidence: 99%

5-DOF serial robot manipulator design, application and inverse kinematic solution through analytical method and simple search technique

Uzuner¹,

Akkuş²,

Toz³

2020

Pamukkale J Eng Sci

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ÖzIn this study a five Degrees of Freedom (DOF) serial robot manipulator was designed and implemented. The inverse kinematics problem, which has not exact analytical solution (only one inverse kinematic solution for a predefined end effector position in three dimensional space), of the robot mechanism was solved by using the combination of the analytical method and a simple search method. In order to use the proposed method in real-time applications, the method is designed so that it can be used to solve the inverse kinematics problem for the next point of the end-effector while the mechanism is working. Moreover, so that to control the implemented mechanism, a user interface program was written by using Visual Basic programming language. Finally, the proposed inverse kinematic solution method was tested on two different trajectories, an arc shaped trajectory that composed of 58 points and a linear trajectory divided into 29 points. The obtained results revealed that the proposed method can be used successfully in solving the inverse kinematic problem of the designed mechanism.Bu çalışmada, beş serbestlik derecesine (SD) sahip bir seri robot manipülatörü tasarlanmış ve test edilmiştir. Robot mekanizmasının kesin analitik çözümü (üç boyutlu uzayda önceden tanımlanmış bir uç işlevcisi konumu için yalnızca bir ters kinematik çözümü) olmayan ters kinematik problemi, analitik yöntem ve bir basit arama metodu kombinasyonu kullanılarak çözülmüştür. Önerilen yöntem, gerçek zamanlı kullanılabilmesi için mekanizma çalışırken uç işlevcinin gideceği bir sonraki nokta için çözüm yapılabilecek şekilde tasarlanmıştır. Gerçekleştirilen mekanizmayı kontrol etmek için Visual Basic programlama dili kullanılarak bir kullanıcı arayüzü programı yazılmıştır. Son olarak, önerilen ters kinematik çözüm yöntemi 58 noktadan oluşan yay şeklinde bir yörünge ve 29 noktaya bölünmüş doğrusal bir yörünge olmak üzere iki farklı yörüngede test edilmiştir. Elde edilen sonuçlar, önerilen yöntemin tasarlanan mekanizmanın ters kinematik problemini çözmede başarıyla kullanılabileceğini ortaya koymuştur.

show abstract

A vision methodology for harvesting robot to detect cutting points on peduncles of double overlapping grape clusters in a vineyard

Cited by 117 publications

References 21 publications

An Autonomous Fruit and Vegetable Harvester with a Low-Cost Gripper Using a 3D Sensor

An Autonomous Fruit and Vegetable Harvester with a Low-Cost Gripper Using a 3D Sensor

A Novel Greenhouse-Based System for the Detection and Plumpness Assessment of Strawberry Using an Improved Deep Learning Technique

5-DOF serial robot manipulator design, application and inverse kinematic solution through analytical method and simple search technique

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