Teleoperation can assist people to complete various complex tasks in inaccessible or high-risk environments,in which a wearable hand exoskeleton is one of the key devices.Adequate adaptability would be available to en...Teleoperation can assist people to complete various complex tasks in inaccessible or high-risk environments,in which a wearable hand exoskeleton is one of the key devices.Adequate adaptability would be available to enable the master hand exoskeleton to capture the motion of human fingers and reproduce the contact force between the slave hand and its object.This paper presents a novel finger exoskeleton based on the cascading four-link closed-loop kinematic chain.Each finger has an independent closed-loop kinematic chain,and the angle sensors are used to obtain the finger motion including the flexion/extension and the adduction/abduction.The cable tension is changed by the servo motor to transmit the contact force to the fingers in real time.Based on the finger exoskeleton,an adaptive hand exoskeleton is consequently developed.In addition,the hand exoskeleton is tested in a master-slave system.The experiment results show that the adaptive hand exoskeleton can be worn without any mechanical constraints,and the slave hand can follow the motions of each human finger.The accuracy and the real-time capability of the force reproduction are validated.The proposed adaptive hand exoskeleton can be employed as the master hand to remotely control the humanoid five-fingered dexterous slave hand,thus,enabling the teleoperation system to complete complex dexterous manipulation tasks.展开更多
Actuator plays a significant role in soft robotics.This paper proposed an ultralong stretchable soft actuator(US2A)with a variable and sizeable maximum elongation.The US2A is composed of a silicone rubber tube and a b...Actuator plays a significant role in soft robotics.This paper proposed an ultralong stretchable soft actuator(US2A)with a variable and sizeable maximum elongation.The US2A is composed of a silicone rubber tube and a bellows woven sleeve.The maximal extension can be conveniently regulated by just adjusting the wrinkles’initial angle of the bellows woven sleeve.The kinematics of US2A could be obtained by geometrically analyzing the structure of the bellows woven sleeve when the silicone rubber tube is inflated.Based on the principle of virtual work,the actuating models have been established:the pressure-elongation model and the pressure-force model.These models reflect the influence of the silicone tube’s shell thickness and material properties on the pneumatic muscle’s performance,which facilitates the optimal design of US2A for various working conditions.The experimental results showed that the maximum elongation of the US2A prototype is 257%,and the effective elongation could be variably regulated in the range of 0 and 257%.The proposed models were also verified by pressure-elongation and pressure-force experiments,with an average error of 5%and 2.5%,respectively.Finally,based on the US2A,we designed a pneumatic rehabilitation glove,soft arm robot,and rigid-soft coupling continuous robot,which further verified the feasibility of US2A as a soft driving component.展开更多
The remarkable skill of changing its grasp status and relocating its fingers to perform continuous in-hand manipulation is essential for a multifingered anthropomorphic hand.A commonly utilized method of manipulation ...The remarkable skill of changing its grasp status and relocating its fingers to perform continuous in-hand manipulation is essential for a multifingered anthropomorphic hand.A commonly utilized method of manipulation involves a series of basic movements executed by a high-level controller.However,it remains unclear how these primitives evolve into sophisticated finger gaits during manipulation.Here,we propose an adaptive finger gait-based manipulation method that offers real-time regulation by dynamically changing the primitive interval to ensure the force/moment balance of the object.Successful manipulation relies on contact events that act as triggers for real-time online replanning of multifinger manipulation.We identify four basic motion primitives of finger gaits and create a heuristic finger gait that enables the continuous object rotation of a round cup.Our experimental results verify the effectiveness of the proposed method.Despite the constant breaking and reengaging of contact between the fingers and the object during manipulation,the robotic hand can reliably manipulate the object without failure.Even when the object is subjected to interfering forces,the proposed method demonstrates robustness in managing interference.This work has great potential for application to the dexterous operation of anthropomorphic multifingered hands.展开更多
The versatile motion capability of snake robots offers themselves robust adaptability in varieties of challenging environments where traditional robots may be incapacitated.This study reports a novel flexible snake ro...The versatile motion capability of snake robots offers themselves robust adaptability in varieties of challenging environments where traditional robots may be incapacitated.This study reports a novel flexible snake robot featuring a rigid-flexible coupling structure and multiple motion gaits.To better understand the robot's behavior,a bending model for the soft actuator is established.Furthermore,a dynamic model is developed to map the relationship between the input air pressure and joint torque,which is the model base for controlling the robot effectively.Based on the wave motion generated by the joint coupling direction function in different planes,multiple motion gait planning methods of the snake-like robot are proposed.In order to evaluate the adaptability and maneuverability of the developed snake robot,extensive experiments were conducted in complex environments.The results demonstrate the robot's effectiveness in navigating through intricate settings,underscoring its potential for applications in various fields.展开更多
To realize the robotic harvesting of Hangzhou White Chrysanthemums,the quick recognition and 3D vision localization system for target Chrysanthemums was investigated in this study.The system was comprised of three mai...To realize the robotic harvesting of Hangzhou White Chrysanthemums,the quick recognition and 3D vision localization system for target Chrysanthemums was investigated in this study.The system was comprised of three main stages.Firstly,an end-effector and a simple freedom manipulator with three degrees were designed to meet the quality requirements of harvesting Hangzhou White Chrysanthemums.Secondly,a segmentation based on HSV color space was performed.A fast Fuzzy C-means(FCM)algorithm based on S component was proposed to extract the target image from irrelevant background.Thirdly,binocular stereo vision was used to acquire the target spatial information.According to the shape of Hangzhou White Chrysanthemums,the centroids of stamens were selected as feature points to match in the right and left images.The experimental results showed that the proposed method was able to recognize Hangzhou White Chrysanthemums with the accuracy of 85%.When the distance between target and baseline was 150-450 mm,the errors between the calculated and measured distance were less than 14 mm,which could meet the requirements of the localization accuracy of the harvesting robot.展开更多
In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment,a color image segmentation method for Hangzhou white chrysanthemum based on least squares support ve...In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment,a color image segmentation method for Hangzhou white chrysanthemum based on least squares support vector machine(LS-SVM)was proposed.Firstly,bilateral filter was used to filter the RGB channels image respectively to eliminate noise.Then the pixel-level color feature and texture feature of the image,which was used as input of LS-SVM model(classifier)and SVM model(classifier),were extracted via RGB value of image and gray level co-occurrence matrix.Finally,the color image was segmented with the trained LS-SVM model(classifier)and SVM model(classifier)separately.The experimental results showed that the trained LS-SVM model and SVM model could effectively segment the images of the Hangzhou white chrysanthemums from complicated background taken under three illumination conditions such as front-lighting,back-lighting and overshadow,with the accuracy of above 90%.When segmenting an image,the SVM algorithm required 1.3 s,while the LS-SVM algorithm proposed in this paper just needed 0.7 s,which was better than the SVM algorithm obviously.The picking experiment was carried out and the results showed that the implementation of the proposed segmentation algorithm on the picking robot could achieve 81%picking success rate.展开更多
Apple fruits on trees tend to swing because of wind or other natural causes,therefore reducing the accuracy of apple picking by robots.To increase the accuracy and to speed up the apple tracking and identifying proces...Apple fruits on trees tend to swing because of wind or other natural causes,therefore reducing the accuracy of apple picking by robots.To increase the accuracy and to speed up the apple tracking and identifying process,tracking and recognition method combined with an affine transformation was proposed.The method can be divided into three steps.First,the initial image was segmented by Otsu’s thresholding method based on the two times Red minus Green minus Blue(2R-G-B)color feature;after improving the binary image,the apples were recognized with a local parameter adaptive Hough circle transformation method,thus improving the accuracy of recognition and avoiding the long,time-consuming process and excessive fitted circles in traditional Hough circle transformation.The process and results were verified experimentally.Second,the Shi-Tomasi corners detected and extracted from the first frame image were tracked,and the corners with large positive and negative optical flow errors were removed.The affine transformation matrix between the two frames was calculated based on the Random Sampling Consistency algorithm(RANSAC)to correct the scale of the template image and predict the apple positions.Third,the best positions of the target apples within 1.2 times of the prediction area were searched with a de-mean normalized cross-correlation template matching algorithm.The test results showed that the running time of each frame was 25 ms and 130 ms and the tracking error was more than 8%and 20%in the absence of template correction and apple position prediction,respectively.In comparison,the running time of our algorithm was 25 ms,and the tracking error was less than 4%.Therefore,test results indicate that speed and efficiency can be greatly improved by using our method,and this strategy can also provide a reference for tracking and recognizing other oscillatory fruits.展开更多
Tactile sensing is essentially required for dexterous manipulation in robotic applications.Mimicking human perception of softness identification in a non-invasive fashion,thus achieving satisfactory interaction with f...Tactile sensing is essentially required for dexterous manipulation in robotic applications.Mimicking human perception of softness identification in a non-invasive fashion,thus achieving satisfactory interaction with fragile objects remains a grand challenge.Here,a scatheless measuring methodology based on the multisensory electronic skins to quantify the elastic coefficient of soft materials is reported.This recognition approach lies in the preliminary classification of softness by piezoelectric signals with a modified machine learning algorithm,contributing to an appropriate contact force assignment for subsequent quantitative measurements via strain sensing feedback.The integration of multifunctional sensing system allows the manipulator to hold capabilities of selfsensing and adaptive grasping motility in response to objects with the various softness(i.e.,kPa-MPa).As a proof-of-concept demonstration,the biomimetic manipulator cooperates with the robotic arm to realize the intelligent sorting of oranges varying in freshness,paving the way for the development of microsurgery robots,human-machine interfacing,and advanced prosthetics.展开更多
With the decrease of agricultural labors and the increase in production costs,harvesting robots have become a research hotspot in recent years.To guide harvesting robots to pick mature citrus more precisely under vari...With the decrease of agricultural labors and the increase in production costs,harvesting robots have become a research hotspot in recent years.To guide harvesting robots to pick mature citrus more precisely under variable illumination conditions,an image segmentation algorithm based on superpixel was proposed.Efficient simple linear iterative clustering(SLIC)algorithm which takes similarity of adjacent pixels into account was adopted to segment the images captured under variable illumination conditions into superpixels.The color and texture features of these superpixels were extracted and fused into feature vectors as descriptors to train backpropagation neural networks(BPNN)classifier in the next step.The adjacency information of superpixels was considered by calculating the global-local binary pattern(LBP)in R component images when extracting texture features.To accelerate the classification process,the mean of Cr-Cb image was utilized to find superpixels of interest which were regarded as candidates of citrus superpixels.These candidates were then classified by a pre-trained BPNN model with superpixel-level accuracy of 98.77%and pixel-level accuracy of 94.96%,while the average time to segment one image was 0.4778 s.Therefore,the results indicated that a superpixel-based segmentation algorithm toward citrus images had decent light robustness as well as high accuracy that could guide harvesting robot to pick mature citrus efficiently.展开更多
The quality of egg is mainly influenced by the dirt adhering to its shell.Even with good farm-management practices and careful handling,a small percentage of dirty eggs will be produced.The purpose of this research wa...The quality of egg is mainly influenced by the dirt adhering to its shell.Even with good farm-management practices and careful handling,a small percentage of dirty eggs will be produced.The purpose of this research was to detect the egg stains by using image processing technique.Compared to the color values,the local texture was found to be much more adept at accurately segmenting of the complex and miscellaneous dirt stains on the egg shell.Firstly,the global threshold of the image was obtained by two-peak method.The irrelevant background was removed by using the global threshold and the interested region was acquired.The local texture information extracted from the interested region was taken as the input of fuzzy C-means clustering for segmentation of the dirt stains.According to the principle of projection,the area of dirt stains on the curved egg surface was accurately calculated.The validation experimental results showed that the proposed method for classifying eggs in terms of stain has the specificity of 91.4%for white eggs and 89.5%for brown eggs.展开更多
Grasping unstructured and fragile objects such as food and fruits is a great challenge for robots.Being naturally different from the traditional rigid robot,soft robotics provide highly promising choices with their in...Grasping unstructured and fragile objects such as food and fruits is a great challenge for robots.Being naturally different from the traditional rigid robot,soft robotics provide highly promising choices with their intrinsic flexibility and compliance to objects.Inspired by duck foot and octopus tentacle,a pneumatic webbed soft gripper was proposed,which is consisted of four multi-chambered fingers and four webs.Due to its silicone body and soft web structure,the developed soft gripper can naturally adapt,grasp and hold delicate and unstructured objects.Compressed air inflated into the three chambers of the finger actuates the silicone body and performs inflection and extension.The silicone web follows the motion of four fingers,forming a semi-closed grasping configuration.The fingers were fabricated with silicone rubber and constraint spring by casting process.The web was cast around the fingers.The inflecting motion was modeled via the pneumatic principle and geometrical analysis.The dynamic properties of the finger were tested by step and sinusoidal signals.And the grasping performances for different objects,such as egg,strawberry,candy,and knife,were also demonstrated by experiments.The proposed soft gripper performed stably in response to a 0.4 Hz reference sinusoidal signal.The bionic structure greatly improves the stability and reliability of grasping,particularly for unstructured and fragile objects.Moreover,the webs ensure the grasping for multiple objects in one snatch,especially suitable for agricultural products and food processing.展开更多
基金Supported by National Key Research and Development Program of China(Grant No.2018YFE0125600)Zhejiang Provincial Key Research,Develop-ment Program(Grant No.2021C04015)Natural Science Foundation of Zhejiang(Grant No.LZ23E050005).
文摘Teleoperation can assist people to complete various complex tasks in inaccessible or high-risk environments,in which a wearable hand exoskeleton is one of the key devices.Adequate adaptability would be available to enable the master hand exoskeleton to capture the motion of human fingers and reproduce the contact force between the slave hand and its object.This paper presents a novel finger exoskeleton based on the cascading four-link closed-loop kinematic chain.Each finger has an independent closed-loop kinematic chain,and the angle sensors are used to obtain the finger motion including the flexion/extension and the adduction/abduction.The cable tension is changed by the servo motor to transmit the contact force to the fingers in real time.Based on the finger exoskeleton,an adaptive hand exoskeleton is consequently developed.In addition,the hand exoskeleton is tested in a master-slave system.The experiment results show that the adaptive hand exoskeleton can be worn without any mechanical constraints,and the slave hand can follow the motions of each human finger.The accuracy and the real-time capability of the force reproduction are validated.The proposed adaptive hand exoskeleton can be employed as the master hand to remotely control the humanoid five-fingered dexterous slave hand,thus,enabling the teleoperation system to complete complex dexterous manipulation tasks.
基金National Natural Science Foundation of China(Grant No.U2013212)Key Research and Development Program of Zhejiang(Grant No.2021C04015)Fundamental Research Funds for the Provincial Universities of Zhejiang(Grant No.RF-C2019004)。
文摘Actuator plays a significant role in soft robotics.This paper proposed an ultralong stretchable soft actuator(US2A)with a variable and sizeable maximum elongation.The US2A is composed of a silicone rubber tube and a bellows woven sleeve.The maximal extension can be conveniently regulated by just adjusting the wrinkles’initial angle of the bellows woven sleeve.The kinematics of US2A could be obtained by geometrically analyzing the structure of the bellows woven sleeve when the silicone rubber tube is inflated.Based on the principle of virtual work,the actuating models have been established:the pressure-elongation model and the pressure-force model.These models reflect the influence of the silicone tube’s shell thickness and material properties on the pneumatic muscle’s performance,which facilitates the optimal design of US2A for various working conditions.The experimental results showed that the maximum elongation of the US2A prototype is 257%,and the effective elongation could be variably regulated in the range of 0 and 257%.The proposed models were also verified by pressure-elongation and pressure-force experiments,with an average error of 5%and 2.5%,respectively.Finally,based on the US2A,we designed a pneumatic rehabilitation glove,soft arm robot,and rigid-soft coupling continuous robot,which further verified the feasibility of US2A as a soft driving component.
基金This work was supported by the National Natural Science Foundation of China(U2013212)the Key Research and Development Program of Zhejiang,China(2021C04015)the Fundamental Research Funds for the Provincial Universities of Zhejiang,China(RF-C2019004).
文摘The remarkable skill of changing its grasp status and relocating its fingers to perform continuous in-hand manipulation is essential for a multifingered anthropomorphic hand.A commonly utilized method of manipulation involves a series of basic movements executed by a high-level controller.However,it remains unclear how these primitives evolve into sophisticated finger gaits during manipulation.Here,we propose an adaptive finger gait-based manipulation method that offers real-time regulation by dynamically changing the primitive interval to ensure the force/moment balance of the object.Successful manipulation relies on contact events that act as triggers for real-time online replanning of multifinger manipulation.We identify four basic motion primitives of finger gaits and create a heuristic finger gait that enables the continuous object rotation of a round cup.Our experimental results verify the effectiveness of the proposed method.Despite the constant breaking and reengaging of contact between the fingers and the object during manipulation,the robotic hand can reliably manipulate the object without failure.Even when the object is subjected to interfering forces,the proposed method demonstrates robustness in managing interference.This work has great potential for application to the dexterous operation of anthropomorphic multifingered hands.
基金financially supported by the Joint Fund of National Natural Science Foundation of China with Shenzhen City(U2013212)the National Key R&D Program of China(2020YFB1313001).
文摘The versatile motion capability of snake robots offers themselves robust adaptability in varieties of challenging environments where traditional robots may be incapacitated.This study reports a novel flexible snake robot featuring a rigid-flexible coupling structure and multiple motion gaits.To better understand the robot's behavior,a bending model for the soft actuator is established.Furthermore,a dynamic model is developed to map the relationship between the input air pressure and joint torque,which is the model base for controlling the robot effectively.Based on the wave motion generated by the joint coupling direction function in different planes,multiple motion gait planning methods of the snake-like robot are proposed.In order to evaluate the adaptability and maneuverability of the developed snake robot,extensive experiments were conducted in complex environments.The results demonstrate the robot's effectiveness in navigating through intricate settings,underscoring its potential for applications in various fields.
基金This work was financially supported by the project of National Science and Technology Supporting Plan(2015BAF01B02)the Open Foundation of Intelligent Robots and Systems at the University of Beijing Institute of Technology,High-tech Innovation Center(2016IRS03).
文摘To realize the robotic harvesting of Hangzhou White Chrysanthemums,the quick recognition and 3D vision localization system for target Chrysanthemums was investigated in this study.The system was comprised of three main stages.Firstly,an end-effector and a simple freedom manipulator with three degrees were designed to meet the quality requirements of harvesting Hangzhou White Chrysanthemums.Secondly,a segmentation based on HSV color space was performed.A fast Fuzzy C-means(FCM)algorithm based on S component was proposed to extract the target image from irrelevant background.Thirdly,binocular stereo vision was used to acquire the target spatial information.According to the shape of Hangzhou White Chrysanthemums,the centroids of stamens were selected as feature points to match in the right and left images.The experimental results showed that the proposed method was able to recognize Hangzhou White Chrysanthemums with the accuracy of 85%.When the distance between target and baseline was 150-450 mm,the errors between the calculated and measured distance were less than 14 mm,which could meet the requirements of the localization accuracy of the harvesting robot.
基金This work was financially supported by the project of National Science and Technology Supporting Plan(2015BAF01B02)the Open Foundation of Intelligent Robots and Systems at the University of Beijing Institute of Technology,High-tech Innovation Center(2016IRS03).
文摘In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment,a color image segmentation method for Hangzhou white chrysanthemum based on least squares support vector machine(LS-SVM)was proposed.Firstly,bilateral filter was used to filter the RGB channels image respectively to eliminate noise.Then the pixel-level color feature and texture feature of the image,which was used as input of LS-SVM model(classifier)and SVM model(classifier),were extracted via RGB value of image and gray level co-occurrence matrix.Finally,the color image was segmented with the trained LS-SVM model(classifier)and SVM model(classifier)separately.The experimental results showed that the trained LS-SVM model and SVM model could effectively segment the images of the Hangzhou white chrysanthemums from complicated background taken under three illumination conditions such as front-lighting,back-lighting and overshadow,with the accuracy of above 90%.When segmenting an image,the SVM algorithm required 1.3 s,while the LS-SVM algorithm proposed in this paper just needed 0.7 s,which was better than the SVM algorithm obviously.The picking experiment was carried out and the results showed that the implementation of the proposed segmentation algorithm on the picking robot could achieve 81%picking success rate.
基金This work was financially supported by Basic Public Welfare Research Project of Zhejiang Province(Grant No.LGN20E050007).
文摘Apple fruits on trees tend to swing because of wind or other natural causes,therefore reducing the accuracy of apple picking by robots.To increase the accuracy and to speed up the apple tracking and identifying process,tracking and recognition method combined with an affine transformation was proposed.The method can be divided into three steps.First,the initial image was segmented by Otsu’s thresholding method based on the two times Red minus Green minus Blue(2R-G-B)color feature;after improving the binary image,the apples were recognized with a local parameter adaptive Hough circle transformation method,thus improving the accuracy of recognition and avoiding the long,time-consuming process and excessive fitted circles in traditional Hough circle transformation.The process and results were verified experimentally.Second,the Shi-Tomasi corners detected and extracted from the first frame image were tracked,and the corners with large positive and negative optical flow errors were removed.The affine transformation matrix between the two frames was calculated based on the Random Sampling Consistency algorithm(RANSAC)to correct the scale of the template image and predict the apple positions.Third,the best positions of the target apples within 1.2 times of the prediction area were searched with a de-mean normalized cross-correlation template matching algorithm.The test results showed that the running time of each frame was 25 ms and 130 ms and the tracking error was more than 8%and 20%in the absence of template correction and apple position prediction,respectively.In comparison,the running time of our algorithm was 25 ms,and the tracking error was less than 4%.Therefore,test results indicate that speed and efficiency can be greatly improved by using our method,and this strategy can also provide a reference for tracking and recognizing other oscillatory fruits.
基金supported by National Natural Science Foundation of China (Grant no.11672269,11972323,and 12002308)Zhejiang Provincial Natural Science Foundation of China (Grant no.LQ22A020009,LR20A020002,LR18E050002,LR19E020004,LZY21E030002,D21F030003,and LSY19H180004)+2 种基金Fundamental Research Funds for the Provincial Universities of Zhejiang (RF-B2019004)111 Project (No.:D16004)Department of Education of Zhejiang Province (Y202043208).
文摘Tactile sensing is essentially required for dexterous manipulation in robotic applications.Mimicking human perception of softness identification in a non-invasive fashion,thus achieving satisfactory interaction with fragile objects remains a grand challenge.Here,a scatheless measuring methodology based on the multisensory electronic skins to quantify the elastic coefficient of soft materials is reported.This recognition approach lies in the preliminary classification of softness by piezoelectric signals with a modified machine learning algorithm,contributing to an appropriate contact force assignment for subsequent quantitative measurements via strain sensing feedback.The integration of multifunctional sensing system allows the manipulator to hold capabilities of selfsensing and adaptive grasping motility in response to objects with the various softness(i.e.,kPa-MPa).As a proof-of-concept demonstration,the biomimetic manipulator cooperates with the robotic arm to realize the intelligent sorting of oranges varying in freshness,paving the way for the development of microsurgery robots,human-machine interfacing,and advanced prosthetics.
基金This work was financially supported by Huzhou Har-bot Intelligent Technology Co.,Ltd.
文摘With the decrease of agricultural labors and the increase in production costs,harvesting robots have become a research hotspot in recent years.To guide harvesting robots to pick mature citrus more precisely under variable illumination conditions,an image segmentation algorithm based on superpixel was proposed.Efficient simple linear iterative clustering(SLIC)algorithm which takes similarity of adjacent pixels into account was adopted to segment the images captured under variable illumination conditions into superpixels.The color and texture features of these superpixels were extracted and fused into feature vectors as descriptors to train backpropagation neural networks(BPNN)classifier in the next step.The adjacency information of superpixels was considered by calculating the global-local binary pattern(LBP)in R component images when extracting texture features.To accelerate the classification process,the mean of Cr-Cb image was utilized to find superpixels of interest which were regarded as candidates of citrus superpixels.These candidates were then classified by a pre-trained BPNN model with superpixel-level accuracy of 98.77%and pixel-level accuracy of 94.96%,while the average time to segment one image was 0.4778 s.Therefore,the results indicated that a superpixel-based segmentation algorithm toward citrus images had decent light robustness as well as high accuracy that could guide harvesting robot to pick mature citrus efficiently.
基金The authors gratefully acknowledge the financial support of the National Science&Technology Pillar Program(2015BAD19B05).
文摘The quality of egg is mainly influenced by the dirt adhering to its shell.Even with good farm-management practices and careful handling,a small percentage of dirty eggs will be produced.The purpose of this research was to detect the egg stains by using image processing technique.Compared to the color values,the local texture was found to be much more adept at accurately segmenting of the complex and miscellaneous dirt stains on the egg shell.Firstly,the global threshold of the image was obtained by two-peak method.The irrelevant background was removed by using the global threshold and the interested region was acquired.The local texture information extracted from the interested region was taken as the input of fuzzy C-means clustering for segmentation of the dirt stains.According to the principle of projection,the area of dirt stains on the curved egg surface was accurately calculated.The validation experimental results showed that the proposed method for classifying eggs in terms of stain has the specificity of 91.4%for white eggs and 89.5%for brown eggs.
基金This research was financially supported by the National Natural Science Foundation of China(Grant No.51775499)the Key Research and Development Program of Zhejiang(Grand No.2021C04015)the Fundamental Research Funds for the Provincial Universities of Zhejiang(Grant No.RF-C2019004).
文摘Grasping unstructured and fragile objects such as food and fruits is a great challenge for robots.Being naturally different from the traditional rigid robot,soft robotics provide highly promising choices with their intrinsic flexibility and compliance to objects.Inspired by duck foot and octopus tentacle,a pneumatic webbed soft gripper was proposed,which is consisted of four multi-chambered fingers and four webs.Due to its silicone body and soft web structure,the developed soft gripper can naturally adapt,grasp and hold delicate and unstructured objects.Compressed air inflated into the three chambers of the finger actuates the silicone body and performs inflection and extension.The silicone web follows the motion of four fingers,forming a semi-closed grasping configuration.The fingers were fabricated with silicone rubber and constraint spring by casting process.The web was cast around the fingers.The inflecting motion was modeled via the pneumatic principle and geometrical analysis.The dynamic properties of the finger were tested by step and sinusoidal signals.And the grasping performances for different objects,such as egg,strawberry,candy,and knife,were also demonstrated by experiments.The proposed soft gripper performed stably in response to a 0.4 Hz reference sinusoidal signal.The bionic structure greatly improves the stability and reliability of grasping,particularly for unstructured and fragile objects.Moreover,the webs ensure the grasping for multiple objects in one snatch,especially suitable for agricultural products and food processing.
