We proved a new necessary and sufficient condition for 2D three finger equilibrium grasps and implemented an geometrical algorithm for computing force closure grasps of arbitrary 2D objects in this article. The algori...We proved a new necessary and sufficient condition for 2D three finger equilibrium grasps and implemented an geometrical algorithm for computing force closure grasps of arbitrary 2D objects in this article. The algorithm is quite simple and only needs some algebraic calculations. Finally, we implemented the algorithm and confirmed its usefulness by an example.展开更多
Stability is a significant property for a robot hand grasp to perform complextasks similar to human hands. The common method to investigate the stability of roboticmulti-fingered grasp system is Lyapunov direct method...Stability is a significant property for a robot hand grasp to perform complextasks similar to human hands. The common method to investigate the stability of roboticmulti-fingered grasp system is Lyapunov direct method, but usually it is rather difficult toconstruct a proper Lyapunov function. Avoiding the hard work of constructing a Lyapunov function, wepropose the sufficient conditions for stability of the robotic grasp system.展开更多
This research characterizes grasping by multifingered robot hands through investiga- tion of the space of contact forces into four subspaces , a method is developed to determine the di- mensions of the subspaces with ...This research characterizes grasping by multifingered robot hands through investiga- tion of the space of contact forces into four subspaces , a method is developed to determine the di- mensions of the subspaces with respect to the connectivity of the object. The relationship reveals the differences between three types of grasps classified and indicates how the contact force can be decomposed corresponding to each type of grasp. The subspaces and the determination of their di- mensions are illlustrated by examples.展开更多
This paper deals with the problem of force-closure analysis for soft multi-fingered grasps. The first step is the study of the relationship between the external wrench space and the manipulation force space at any con...This paper deals with the problem of force-closure analysis for soft multi-fingered grasps. The first step is the study of the relationship between the external wrench space and the manipulation force space at any contact. Constraint force set, strictly constraint force set and normal force set are defined in the contact force space, followed by an investigation of their relationships. Based on the convexity of the friction constraints for soft finger contact, the necessary and sufficient conditions for force-closure grasps are derived. Accordingly an efficient algorithm for testing force-closure is presented. Some illustrative examples are given.展开更多
With the rapid development of robotics,grasp prediction has become fundamental to achieving intelligent physical interactions.To enhance grasp detection accuracy in unstructured environments,we propose a novel Cross-M...With the rapid development of robotics,grasp prediction has become fundamental to achieving intelligent physical interactions.To enhance grasp detection accuracy in unstructured environments,we propose a novel Cross-Multiscale Adaptive Collaborative and Fusion Grasp Detection Network(CMACF-Net).Addressing the limitations of conventional methods in capturing multi-scale spatial features,CMACF-Net introduces the Quantized Multi-scale Global Attention Module(QMGAM),which enables precise multi-scale spatial calibration and adaptive spatial-channel interaction,ultimately yielding a more robust and discriminative feature representation.To reduce the degradation of local features and the loss of high-frequency information,the Cross-scale Context Integration Module(CCI)is employed to facilitate the effective fusion and alignment of global context and local details.Furthermore,an Efficient Up-Convolution Block(EUCB)is integrated into a U-Net architecture to effectively restore spatial details lost during the downsampling process,while simultaneously preserving computational efficiency.Extensive evaluations demonstrate that CMACF-Net achieves state-of-the-art detection accuracies of 98.9% and 95.9% on the Cornell and Jacquard datasets,respectively.Additionally,real-time grasping experiments on the RM65-B robotic platform validate the framework’s robustness and generalization capability,underscoring its applicability to real-world robotic manipulation scenarios.展开更多
The word“spatial”fundamentally relates to human existence,evolution,and activity in terrestrial and even celestial spaces.After reviewing the spatial features of many areas,the paper describes basics of high level m...The word“spatial”fundamentally relates to human existence,evolution,and activity in terrestrial and even celestial spaces.After reviewing the spatial features of many areas,the paper describes basics of high level model and technology called Spatial Grasp for dealing with large distributed systems,which can provide spatial vision,awareness,management,control,and even consciousness.The technology description includes its key Spatial Grasp Language(SGL),self-evolution of recursive SGL scenarios,and implementation of SGL interpreter converting distributed networked systems into powerful spatial engines.Examples of typical spatial scenarios in SGL include finding shortest path tree and shortest path between network nodes,collecting proper information throughout the whole world,elimination of multiple targets by intelligent teams of chasers,and withstanding cyber attacks in distributed networked systems.Also this paper compares Spatial Grasp model with traditional algorithms,confirming universality of the former for any spatial systems,while the latter just tools for concrete applications.展开更多
“Multidimensional international world”refers to understanding the world through multiple dimensions beyond traditional economic or political measures,fostering cross-cultural collaboration,and creating systems that ...“Multidimensional international world”refers to understanding the world through multiple dimensions beyond traditional economic or political measures,fostering cross-cultural collaboration,and creating systems that balance global integration with local needs.This also includes management of global business operations across diverse cultures in a multipolar international landscape.The paper briefs the developed and already tested in numerous applications high-level Spatial Grasp Model and Technology(SGT),which can help investigate and manage complex systems with a holistic spatial approach effectively covering various physical and virtual dimensions,their interrelations,and integration as a whole.Different areas will be investigated with examples of practical solutions in them and their combinations in a high-level Spatial Grasp Language(SGL),the key element of SGT.This allows for the creation and distributed management of very large spatial networks with different orientation which can be self-spreading,self-analyzing,self-modifying,and self-recovering in complex terrestrial and celestial environments,and also organize dynamic multi-networking solutions supporting global evolution and integrity.展开更多
Currently,numerous biomimetic robots inspired by natural biological systems have been developed.However,creating soft robots with versatile locomotion modes remains a significant challenge.Snakes,as invertebrate repti...Currently,numerous biomimetic robots inspired by natural biological systems have been developed.However,creating soft robots with versatile locomotion modes remains a significant challenge.Snakes,as invertebrate reptiles,exhibit diverse and powerful locomotion abilities,including prey constriction,sidewinding,accordion locomotion,and winding climbing,making them a focus of robotics research.In this study,we present a snake-inspired soft robot with an initial coiling structure,fabricated using MXene-cellulose nanofiber ink printed on pre-expanded polyethylene film through direct ink writing technology.The controllable fabrication of initial coiling structure soft robot(ICSBot)has been achieved through theoretical calculations and finite element analysis to predict and analyze the initial structure of ICSBot,and programmable ICSBot has been designed and fabricated.This robot functions as a coiling gripper capable of grasping objects with complex shapes under near infrared light stimulation.Additionally,it demonstrates multi-modal crawling locomotion in various environments,including confined spaces,unstructured terrains,and both inside and outside tubes.These results offer a novel strategy for designing and fabricating coiling-structured soft robots and highlight their potential applications in smart and multifunctional robotics.展开更多
Background:Q uantifying the rich home-c age activities of tree shrews provides a reliable basis for understanding their daily routines and building disease models.However,due to the lack of effective behavioral method...Background:Q uantifying the rich home-c age activities of tree shrews provides a reliable basis for understanding their daily routines and building disease models.However,due to the lack of effective behavioral methods,most efforts on tree shrew behavior are limited to simple measures,resulting in the loss of much behavioral information.Methods:T o address this issue,we present a deep learning(DL)approach to achieve markerless pose estimation and recognize multiple spontaneous behaviors of tree shrews,including drinking,eating,resting,and staying in the dark house,etc.Results:T his high-t hroughput approach can monitor the home-cage activities of 16 tree shrews simultaneously over an extended period.Additionally,we demonstrated an innovative system with reliable apparatus,paradigms,and analysis methods for investigating food grasping behavior.The median duration for each bout of grasping was 0.20 s.Conclusion:T his study provides an efficient tool for quantifying and understand tree shrews'natural behaviors.展开更多
Humans can quickly perform adaptive grasping of soft objects by using visual perception and judgment of the grasping angle,which helps prevent the objects from sliding or deforming excessively.However,this easy task r...Humans can quickly perform adaptive grasping of soft objects by using visual perception and judgment of the grasping angle,which helps prevent the objects from sliding or deforming excessively.However,this easy task remains a challenge for robots.The grasping states of soft objects can be categorized into four types:sliding,appropriate,excessive and extreme.Effective recognition of different states is crucial for achieving adaptive grasping of soft objects.To address this problem,a novel visual-curvature fusion network based on YOLOv8(VCFN-YOLOv8)is proposed to evaluate the grasping state of various soft objects.In this framework,the robotic arm equipped with the wrist camera and the curvature sensor is established to perform generalization grasping and lifting experiments on 11 different objects.Meanwhile,the dataset is built for training and testing the proposed method.The results show a classification accuracy of 99.51% on four different grasping states.A series of grasping evaluation experiments is conducted based on the proposed framework,along with tests for the model's generality.The experiment results demonstrate that VCFN-YOLOv8 is accurate and efficient in evaluating the grasping state of soft objects and shows a certain degree of generalization for non-soft objects.It can be widely applied in fields such as automatic control,adaptive grasping and surgical robot.展开更多
The“visual perception+hand-eye transformation+motion planning”paradigm of robotic coordination grasping has demonstrated feasibility in unstructured scenes such as logistics.However,further developments in handling ...The“visual perception+hand-eye transformation+motion planning”paradigm of robotic coordination grasping has demonstrated feasibility in unstructured scenes such as logistics.However,further developments in handling complex and dynamic environments remain challenging.To address the issue of unknown targets requiring immediate deployment for grasping tasks,this paper proposes a novel hand-eye coordinated method for progressive grasping guided by the texture keypoints of the target.First,we develop an efficient system for acquiring texture-matching templates and an estimation algorithm for the feature region that filters the precisely registered texture feature points of the target.Then,we integrate optical flow estimation to update and track the robust texture region in real time,and design a feature-based servo grasping controller to map the optical flow points of the high-registration texture region to the robot joint velocities for precise tracking.Finally,we impose spatiotemporal constraints on the planned trajectory and decouple the target motion,to achieve progressive approach and rotationally invariant grasping for both dynamic and static targets.Comprehensive experiments demonstrate that this tracking grasping method exhibits a low latency,a high precision,and robustness in complex scenarios and dynamic disturbances,with an average position accuracy of approximately 5 mm,a rotation accuracy of approximately 0.02,and an overall grasping success rate of approximately 90%.展开更多
Robot grasp detection is a fundamental vision task for robots.Deep learning-based methods have shown excellent results in enhancing the grasp detection capabilities for model-free objects in unstructured scenes.Most p...Robot grasp detection is a fundamental vision task for robots.Deep learning-based methods have shown excellent results in enhancing the grasp detection capabilities for model-free objects in unstructured scenes.Most popular approaches explore deep network models and exploit RGB-D images combining colour and depth data to acquire enriched feature expressions.However,current work struggles to achieve a satisfactory balance between the accuracy and real-time performance;the variability of RGB and depth feature distributions receives inadequate attention.The treatment of predicted failure cases is also lacking.We propose an efficient fully convolutional network to predict the pixel-level antipodal grasp parameters in RGB-D images.A structure with hierarchical feature fusion is established using multiple lightweight feature extraction blocks.The feature fusion module with 3D global attention is used to select the complementary information in RGB and depth images suficiently.Additionally,a grasp configuration optimization method based on local grasp path is proposed to cope with the possible failures predicted by the model.Extensive experiments on two public grasping datasets,Cornell and Jacquard,demonstrate that the approach can improve the performance of grasping unknown objects.展开更多
文摘We proved a new necessary and sufficient condition for 2D three finger equilibrium grasps and implemented an geometrical algorithm for computing force closure grasps of arbitrary 2D objects in this article. The algorithm is quite simple and only needs some algebraic calculations. Finally, we implemented the algorithm and confirmed its usefulness by an example.
文摘Stability is a significant property for a robot hand grasp to perform complextasks similar to human hands. The common method to investigate the stability of roboticmulti-fingered grasp system is Lyapunov direct method, but usually it is rather difficult toconstruct a proper Lyapunov function. Avoiding the hard work of constructing a Lyapunov function, wepropose the sufficient conditions for stability of the robotic grasp system.
文摘This research characterizes grasping by multifingered robot hands through investiga- tion of the space of contact forces into four subspaces , a method is developed to determine the di- mensions of the subspaces with respect to the connectivity of the object. The relationship reveals the differences between three types of grasps classified and indicates how the contact force can be decomposed corresponding to each type of grasp. The subspaces and the determination of their di- mensions are illlustrated by examples.
文摘This paper deals with the problem of force-closure analysis for soft multi-fingered grasps. The first step is the study of the relationship between the external wrench space and the manipulation force space at any contact. Constraint force set, strictly constraint force set and normal force set are defined in the contact force space, followed by an investigation of their relationships. Based on the convexity of the friction constraints for soft finger contact, the necessary and sufficient conditions for force-closure grasps are derived. Accordingly an efficient algorithm for testing force-closure is presented. Some illustrative examples are given.
基金supported by the Jiangxi Provincial Natural Science Foundation(No.20232BAB202027)the National Natural Science Foundation of China(No.62367006)the Natural Science Foundation of Hubei Province of China(No.2022CFB536).
文摘With the rapid development of robotics,grasp prediction has become fundamental to achieving intelligent physical interactions.To enhance grasp detection accuracy in unstructured environments,we propose a novel Cross-Multiscale Adaptive Collaborative and Fusion Grasp Detection Network(CMACF-Net).Addressing the limitations of conventional methods in capturing multi-scale spatial features,CMACF-Net introduces the Quantized Multi-scale Global Attention Module(QMGAM),which enables precise multi-scale spatial calibration and adaptive spatial-channel interaction,ultimately yielding a more robust and discriminative feature representation.To reduce the degradation of local features and the loss of high-frequency information,the Cross-scale Context Integration Module(CCI)is employed to facilitate the effective fusion and alignment of global context and local details.Furthermore,an Efficient Up-Convolution Block(EUCB)is integrated into a U-Net architecture to effectively restore spatial details lost during the downsampling process,while simultaneously preserving computational efficiency.Extensive evaluations demonstrate that CMACF-Net achieves state-of-the-art detection accuracies of 98.9% and 95.9% on the Cornell and Jacquard datasets,respectively.Additionally,real-time grasping experiments on the RM65-B robotic platform validate the framework’s robustness and generalization capability,underscoring its applicability to real-world robotic manipulation scenarios.
文摘The word“spatial”fundamentally relates to human existence,evolution,and activity in terrestrial and even celestial spaces.After reviewing the spatial features of many areas,the paper describes basics of high level model and technology called Spatial Grasp for dealing with large distributed systems,which can provide spatial vision,awareness,management,control,and even consciousness.The technology description includes its key Spatial Grasp Language(SGL),self-evolution of recursive SGL scenarios,and implementation of SGL interpreter converting distributed networked systems into powerful spatial engines.Examples of typical spatial scenarios in SGL include finding shortest path tree and shortest path between network nodes,collecting proper information throughout the whole world,elimination of multiple targets by intelligent teams of chasers,and withstanding cyber attacks in distributed networked systems.Also this paper compares Spatial Grasp model with traditional algorithms,confirming universality of the former for any spatial systems,while the latter just tools for concrete applications.
文摘“Multidimensional international world”refers to understanding the world through multiple dimensions beyond traditional economic or political measures,fostering cross-cultural collaboration,and creating systems that balance global integration with local needs.This also includes management of global business operations across diverse cultures in a multipolar international landscape.The paper briefs the developed and already tested in numerous applications high-level Spatial Grasp Model and Technology(SGT),which can help investigate and manage complex systems with a holistic spatial approach effectively covering various physical and virtual dimensions,their interrelations,and integration as a whole.Different areas will be investigated with examples of practical solutions in them and their combinations in a high-level Spatial Grasp Language(SGL),the key element of SGT.This allows for the creation and distributed management of very large spatial networks with different orientation which can be self-spreading,self-analyzing,self-modifying,and self-recovering in complex terrestrial and celestial environments,and also organize dynamic multi-networking solutions supporting global evolution and integrity.
基金supported by the National Key R&D Program of China(NO.2024YFB3409900)the China Postdoctoral Science Foundation(NO.2023M730845)the Heilongjiang Postdoctoral Fund(NO.LBH-Z23182)。
文摘Currently,numerous biomimetic robots inspired by natural biological systems have been developed.However,creating soft robots with versatile locomotion modes remains a significant challenge.Snakes,as invertebrate reptiles,exhibit diverse and powerful locomotion abilities,including prey constriction,sidewinding,accordion locomotion,and winding climbing,making them a focus of robotics research.In this study,we present a snake-inspired soft robot with an initial coiling structure,fabricated using MXene-cellulose nanofiber ink printed on pre-expanded polyethylene film through direct ink writing technology.The controllable fabrication of initial coiling structure soft robot(ICSBot)has been achieved through theoretical calculations and finite element analysis to predict and analyze the initial structure of ICSBot,and programmable ICSBot has been designed and fabricated.This robot functions as a coiling gripper capable of grasping objects with complex shapes under near infrared light stimulation.Additionally,it demonstrates multi-modal crawling locomotion in various environments,including confined spaces,unstructured terrains,and both inside and outside tubes.These results offer a novel strategy for designing and fabricating coiling-structured soft robots and highlight their potential applications in smart and multifunctional robotics.
基金supported by grants from the National Key Research and Development Program of China(2023YFF0724902)the China Postdoctoral Science Foundation(2020?M670027,2023TQ0183)the Local Standards Research of BeiJing Laboratory Tree Shrew(CHYX-2023-DGB001)。
文摘Background:Q uantifying the rich home-c age activities of tree shrews provides a reliable basis for understanding their daily routines and building disease models.However,due to the lack of effective behavioral methods,most efforts on tree shrew behavior are limited to simple measures,resulting in the loss of much behavioral information.Methods:T o address this issue,we present a deep learning(DL)approach to achieve markerless pose estimation and recognize multiple spontaneous behaviors of tree shrews,including drinking,eating,resting,and staying in the dark house,etc.Results:T his high-t hroughput approach can monitor the home-cage activities of 16 tree shrews simultaneously over an extended period.Additionally,we demonstrated an innovative system with reliable apparatus,paradigms,and analysis methods for investigating food grasping behavior.The median duration for each bout of grasping was 0.20 s.Conclusion:T his study provides an efficient tool for quantifying and understand tree shrews'natural behaviors.
基金supported by the Fundamental Research Project of Shanxi Province(202403021211229).
文摘Humans can quickly perform adaptive grasping of soft objects by using visual perception and judgment of the grasping angle,which helps prevent the objects from sliding or deforming excessively.However,this easy task remains a challenge for robots.The grasping states of soft objects can be categorized into four types:sliding,appropriate,excessive and extreme.Effective recognition of different states is crucial for achieving adaptive grasping of soft objects.To address this problem,a novel visual-curvature fusion network based on YOLOv8(VCFN-YOLOv8)is proposed to evaluate the grasping state of various soft objects.In this framework,the robotic arm equipped with the wrist camera and the curvature sensor is established to perform generalization grasping and lifting experiments on 11 different objects.Meanwhile,the dataset is built for training and testing the proposed method.The results show a classification accuracy of 99.51% on four different grasping states.A series of grasping evaluation experiments is conducted based on the proposed framework,along with tests for the model's generality.The experiment results demonstrate that VCFN-YOLOv8 is accurate and efficient in evaluating the grasping state of soft objects and shows a certain degree of generalization for non-soft objects.It can be widely applied in fields such as automatic control,adaptive grasping and surgical robot.
基金Supported by National Key R&D Program of China(Grant No.2024YFB4709800)Fundamental Research Funds for the Central Universities。
文摘The“visual perception+hand-eye transformation+motion planning”paradigm of robotic coordination grasping has demonstrated feasibility in unstructured scenes such as logistics.However,further developments in handling complex and dynamic environments remain challenging.To address the issue of unknown targets requiring immediate deployment for grasping tasks,this paper proposes a novel hand-eye coordinated method for progressive grasping guided by the texture keypoints of the target.First,we develop an efficient system for acquiring texture-matching templates and an estimation algorithm for the feature region that filters the precisely registered texture feature points of the target.Then,we integrate optical flow estimation to update and track the robust texture region in real time,and design a feature-based servo grasping controller to map the optical flow points of the high-registration texture region to the robot joint velocities for precise tracking.Finally,we impose spatiotemporal constraints on the planned trajectory and decouple the target motion,to achieve progressive approach and rotationally invariant grasping for both dynamic and static targets.Comprehensive experiments demonstrate that this tracking grasping method exhibits a low latency,a high precision,and robustness in complex scenarios and dynamic disturbances,with an average position accuracy of approximately 5 mm,a rotation accuracy of approximately 0.02,and an overall grasping success rate of approximately 90%.
基金the National Natural Science Foundation of China(No.62173230)the Program of Science and Technology Commission of Shanghai Municipality(No.22511101400)。
文摘Robot grasp detection is a fundamental vision task for robots.Deep learning-based methods have shown excellent results in enhancing the grasp detection capabilities for model-free objects in unstructured scenes.Most popular approaches explore deep network models and exploit RGB-D images combining colour and depth data to acquire enriched feature expressions.However,current work struggles to achieve a satisfactory balance between the accuracy and real-time performance;the variability of RGB and depth feature distributions receives inadequate attention.The treatment of predicted failure cases is also lacking.We propose an efficient fully convolutional network to predict the pixel-level antipodal grasp parameters in RGB-D images.A structure with hierarchical feature fusion is established using multiple lightweight feature extraction blocks.The feature fusion module with 3D global attention is used to select the complementary information in RGB and depth images suficiently.Additionally,a grasp configuration optimization method based on local grasp path is proposed to cope with the possible failures predicted by the model.Extensive experiments on two public grasping datasets,Cornell and Jacquard,demonstrate that the approach can improve the performance of grasping unknown objects.
