Apple viscoelastic complex model for bruise damage analysis in constant velocity grasping by gripper

Ji, Wei; Qian, Zhijie; Xu, Bo; Chen, Guangyu; Zhao, Debin

doi:10.1016/j.compag.2019.05.022

Cited by 36 publications

(15 citation statements)

References 13 publications

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“…On the basis of our previous studies, the law of fruit deformation with time and the law of contact force with time under six grasping velocities were obtained [16]. us, the relationship between contact force and deformation of apple grasping in three stages is shown in Figure 3.…”

Section: Analysis Of Mechanical Characteristics Of Applementioning

confidence: 99%

“…e traditional impedance control can make the force and position achieve a good dynamic relationship [23]. According to the analysis in reference [16], when the grasping velocity is greater than 3 mm•s −1 , the apple skin will undergo plastic deformation. Considering the real-time performance of the robot grasping fruits, the grasping velocity of 3 mm•s −1 is selected.…”

Section: Complexitymentioning

confidence: 99%

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Adaptive Variable Parameter Impedance Control for Apple Harvesting Robot Compliant Picking

Ding

et al. 2020

Complexity

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In order to reduce the damage of apple harvesting robot to fruits and achieve compliant picking, an adaptive variable parameter impedance control method for apple harvesting robot compliant picking is proposed in this paper. Firstly, the Burgers viscoelastic model is used to characterize the rheological properties of apples and study the variation of mechanical properties of apple grasping at different speeds. Then, a force-based impedance control system is designed. On this basis, aiming at the influence of impedance controller parameters on contact force, three impedance parameters self-tuning functions are constructed to complete the design of an improved force-based impedance control system based on the hyperbolic secant function. The simulation and experimental results show that the proposed control makes the desired force smoother, and its overshoot is about 2.3%. The response speed is faster, and the adjustment time of contact force is shorter of about 0.48 s. The contact force overshoot is about 2%, which is 37.5% less than that of the traditional force-based impedance control. This research improves the control performance for apple harvesting robot compliant picking.

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Section: Analysis Of Mechanical Characteristics Of Applementioning

confidence: 99%

Section: Complexitymentioning

confidence: 99%

Adaptive Variable Parameter Impedance Control for Apple Harvesting Robot Compliant Picking

Ding

et al. 2020

Complexity

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show abstract

“…Moallem et al combines a variety of machine learning methods to classify apple fruits [23]. Ji et al focuses on establishing apple viscoelastic finite element complex model to estimate apple stress variation during grasping with its own characteristics of constant velocity and continuous energy input [24], [25].…”

Section: Related Owrks and Data Acquisition A Related Workmentioning

confidence: 99%

Fast Recognition and Location of Target Fruit Based on Depth Information

Duan

Luo

et al. 2019

IEEE Access

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In order to realize the recognition and localization of the target fruit more accurately and efficiently, an optimized graph-based recognition algorithm based on image depth information is proposed. Firstly, the gradient information of the depth image is obtained from the acquired depth apple image, the gradient vector is reflected from the 3D space to the 2D space, and then rotated 90 degrees clockwise, and the regular region forms the center of the vortex, which is the target fruit center to determine target fruit location. In the RGB space, the graph-based segmentation algorithm is introduced. By constructing the adaptive threshold, the mapping relationship between the number of centers and the parameters of k and min is established to find the target fruit region. Finally, scan the maximum radius of the super pixel area where the center of the circle is located, and fit the target fruit to realize the recognition and localization of the apple. The experimental results show that the target recognition rate is 96.61%, and the recognition time has been reduced significantly.

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“…Finally, the detection accuracy and location accuracy of improved Mask RCNN is evaluated by comparing with original Mask RCNN, Faster RCNN, YOLO V2 and YOLO V3. Compared with our previous works [26]- [28], this work focuses on the detection of cucumber fruits and is more challenging.…”

Section: Introductionmentioning

confidence: 99%

Cucumber Fruits Detection in Greenhouses Based on Instance Segmentation

et al. 2019

Self Cite

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The cucumber fruits have the same color with leaves and their shapes are all long and narrow, which is different from other common fruits, such as apples, tomatoes, and strawberries, etc. Therefore, cucumber fruits are more difficult to be detected by machine vision in greenhouses for special color and shape. A pixel-wise instance segmentation method, mask region-based convolutional neural network (Mask RCNN) of an improved version, is proposed to detect cucumber fruits. Resnet-101 is selected as the backbone of Mask RCNN with feature pyramid network (FPN). To improve the detection precision, region proposal network (RPN) in original Mask RCNN is improved. Logical green (LG) operator is designed to filter nongreen background and limit the range of anchor boxes. Besides, the scales and aspect ratios of anchor boxes are also adjusted to fit the size and shape of fruits. Improved Mask RCNN has a better performance on test images. The test results are compared with that of original Mask RCNN, Faster RCNN, you only look once (YOLO) V2 and YOLO V3. The F 1 score of improved Mask RCNN in test results reaches 89.47%, which is higher than the other methods. The average elapsed time of improved Mask RCNN is 0.3461 s, which is only lower than the original Mask RCNN. Meanwhile, the mean value and standard deviation of location deviation in improved Mask RCNN are 2.10 pixels and 1.73 pixels respectively, which are lower than the other methods.

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Apple viscoelastic complex model for bruise damage analysis in constant velocity grasping by gripper

Cited by 36 publications

References 13 publications

Adaptive Variable Parameter Impedance Control for Apple Harvesting Robot Compliant Picking

Adaptive Variable Parameter Impedance Control for Apple Harvesting Robot Compliant Picking

Fast Recognition and Location of Target Fruit Based on Depth Information

Cucumber Fruits Detection in Greenhouses Based on Instance Segmentation

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