Design and control of an apple harvesting robot

Zhao, Debin; Lv, Jidong; Ji, Wei; Zhang, Ying; Chen, Yu

doi:10.1016/j.biosystemseng.2011.07.005

Cited by 314 publications

(108 citation statements)

References 15 publications

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“…Three-finger gripper with video camera for citrus harvesting [13] Vacuum gripper with a video camera for Harvesting tomatoes [15] Six-finger pneumatic gripper with video camera to harvesting eggplants [16] Two-finger grip with pressure and collision sensors for harvesting apple [17] Two-fingered permanent gripper with a cutting knife and a video camera for removing tomato leaves [18] …”

Section: Resultsmentioning

confidence: 99%

Hierarchical classification of robotic grippers applied for agricultural object manipulations

Kuzov

Ronzhin

2018

MATEC Web Conf.

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An overview of agricultural grippers used to control weeds and harvesting is presented. The classification of the grippers, which are installed on robotic agricultural tools for manipulation of fruits, weeds and other objects, is compiled. There are 22 types of grippers depending on 6 selected criteria: drive type, the presence of the drive in the grip, the number of fingers, the type of gripper movement, the type of mechanism, the type of sensors. In this classification, we mainly consider the characteristics of the gripper, which is installed at the end of the manipulator and is responsible for physical contact with the object. Therefore, the main attention is paid to problems requiring direct capture of objects by the agrobots. The issue of joint interaction of a group of heterogeneous terrestrial and airborne robots in the performance of the target agrarian task in an autonomous mission will also be investigated.

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Section: Resultsmentioning

confidence: 99%

Hierarchical classification of robotic grippers applied for agricultural object manipulations

Kuzov

Ronzhin

2018

MATEC Web Conf.

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“…identified apple targets under different circumstances such as low light, weak light, bright light and strong light. The fruit recognition algorithm was developed by Zhao et al (2011) to detect and locate apples in trees automatically by using a support vector machine with radial basis function. Ji et al (2012) proposed an automatic recognition vision system guided for an apple harvesting robot.…”

Section: Methodsmentioning

confidence: 99%

Segmentation of foreground apple targets by fusing visual attention mechanism and growth rules of seed points

Shang

Shao

et al. 2015

Span. j. agric. res.

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Accurate segmentation of apple targets is one of the most important problems to be solved in the vision system of apple picking robots. This work aimed to solve the difficulties that background targets often bring to foreground targets segmentation, by fusing the visual attention mechanism and the growth rule of seed points. Background targets could be eliminated by extracting the ROI (region of interest) of apple targets; the ROI was roughly segmented on the HSV color space, and then each of the pixels was used as a seed growing point. The growth rule of the seed points was adopted to obtain the whole area of apple targets from seed growing points. The proposed method was tested with 20 images captured in a natural scene, including 54 foreground apple targets and approximately 84 background apple targets. Experimental results showed that the proposed method can remove background targets and focus on foreground targets, while the k-means algorithm and the chromatic aberration algorithm cannot. Additionally, its average segmentation error rate was 13.23%, which is 2.71% higher than that of the k-means algorithm and 2.95% lower than that of the chromatic aberration algorithm. In conclusion, the proposed method contributes to the vision system of apple-picking robots to locate foreground apple targets quickly and accurately under a natural scene.Additional key words:picking robots; Malus domestica; ROI; Itti model.

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“…The workspace and a quick access to a certain point in all robotic arms are strongly dependent on linkages properties, joint properties (length, angles, angular velocity 24 (2018) [91][92][93][94][95][96][97][98][99][100][101][102][103][104] …”

Section: Description Of the Ravebots-1 Workpacementioning

confidence: 99%

“…Agricultural robotics can help address and solve these issues that farming communities encounter on a regular basis (Cassinis & Tampalini 2007). Some example include a multi-arm robotic harvester (Zion et al 2014), a strawberry-harvesting robot (Hayashi et al 2010), an apple harvesting robot (De-An et al 2011), an autonomous robot for white asparagus harvesting (Barawid Jr et al 2007), a cherry-harvesting robot (Tanigaki et al 2008), robots for tomato, petty-tomato, cucumber and grape harvesting (Kondo et al 1996) and Stationary robots that are used for sheep herding (Tanner et al 2001).…”

Section: Introductionmentioning

confidence: 99%

Kinematics Analysis and Simulation of A 5DOF Articulated Robotic Arm Applied to Heavy Products Harvesting

Roshanianfard

Noguchi

2018

Tarım Bilimleri Dergisi

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Robotics can play a significant role to increase efficiency and lighten the farmer's load. Despite challenges in the agricultural robotic designs, robots are capable of performing various tasks and changing themselves accordingly, based on specific conditions. To address modern problems in the agricultural field, an agricultural robot is one of the key technologies. Although agricultural robotic is still in the development stage, robots have a bright future ahead. This paper proposes a new 5DOF articulated robotic arm design that would become a solution for heavy crop harvestings like pumpkin and cabbage. After the development stage, this robotic arm will be mounted on a robot tractor for real experimentation.The main design process of this robotic arm was conceived using 6 stages of Shigley design process. All components were designed, assembled and analyzed by using Solidworks 2014 in compliance with Japanese Industrial Standards (JIS) standards. The parts of the system that had dynamic nature were analyzed manually using standard mechanical formulas. Calculations of the workspace required joint torque, and coordination of mass center position was done by using standard machine design methods. Denavit-Hartenberg method was used to calculate forward and inverse kinematics. To resolve the torque reduction, components were designed using different materials and mass centers and comparing their performance.Results showed that total torque in Joints number 1, 2, 3, 4 and 5 were 6.15, 257.35, 103.4, 20.2 and 0.1 respectively with a rotational speed range of 15 ~ 60 rpm. Changes in the linkage material and servo motor location improved 29.7% ~ 47.7% and 29.7% ~ 68.9% of the total required torque for each joint. The maximum distance covered by the arm was 1421 mm from the and 2026 mm from the attachment point. According to the feedback received from a inverse kinematics equation algorithm, the fundamental operation of the robot arm had an optimal performance.

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Design and control of an apple harvesting robot

Cited by 314 publications

References 15 publications

Hierarchical classification of robotic grippers applied for agricultural object manipulations

Hierarchical classification of robotic grippers applied for agricultural object manipulations

Segmentation of foreground apple targets by fusing visual attention mechanism and growth rules of seed points

Kinematics Analysis and Simulation of A 5DOF Articulated Robotic Arm Applied to Heavy Products Harvesting

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