Humans excel at dexterous manipulation;however,achieving human-level dexterity remains a significant challenge for robots.Technological breakthroughs in the design of anthropomorphic robotic hands,as well as advanceme...Humans excel at dexterous manipulation;however,achieving human-level dexterity remains a significant challenge for robots.Technological breakthroughs in the design of anthropomorphic robotic hands,as well as advancements in visual and tactile perception,have demonstrated significant advantages in addressing this issue.However,coping with the inevitable uncertainty caused by unstructured and dynamic environments in human-like dexterous manipulation tasks,especially for anthropomorphic five-fingered hands,remains an open problem.In this paper,we present a focused review of human-like dexterous manipulation for anthropomorphic five-fingered hands.We begin by defining human-like dexterity and outlining the tasks associated with human-like robot dexterous manipulation.Subsequently,we delve into anthropomorphism and anthropomorphic five-fingered hands,covering definitions,robotic design,and evaluation criteria.Furthermore,we review the learning methods for achieving human-like dexterity in anthropomorphic five-fingered hands,including imitation learning,reinforcement learning and their integration.Finally,we discuss the existing challenges and propose future research directions.This review aims to stimulate interest in scientific research and future applications.展开更多
Keyhole procedures frequently necessitate passing instruments through narrow,elongated surgical channels,presenting a significant challenge in designing manipulators with compact structures and superior remote dexteri...Keyhole procedures frequently necessitate passing instruments through narrow,elongated surgical channels,presenting a significant challenge in designing manipulators with compact structures and superior remote dexterity.In this work,we propose a novel 7 degrees‐of‐freedom variable stiffness dexterous surgical manipulator based on a flexible parallel mechanism.The manipulator has an outer diameter of 4.5 mm and is characterized by a hollow structure that incorporates a central channel measuring 1.8 mm.This design facilitates the integration of tool drives and sensors within the manipulator,enhancing its overall functionality.The kinematics and stiffness model of the flexible parallel mechanism are derived.Additionally,a prototype of the proposed manipulator is presented and evaluated through sufficient experiments.We performed a teleoperation test to characterize the model accuracy of the prototype,and the average error is approximately 0.49 mm.Furthermore,we conduct a series of experiments to verify the prototype's performance,including workspace,stiffness,and dexterous operation.The results confirm that the surgical manipulator demonstrates excellent performance in the challenging environment of keyhole procedures.展开更多
Given the advantages of softness,lightness,low cost,and interaction safety,inverse kinematic modeling and control of soft actuators has caused a research boom.However,in realizing dexterous manipulation of space large...Given the advantages of softness,lightness,low cost,and interaction safety,inverse kinematic modeling and control of soft actuators has caused a research boom.However,in realizing dexterous manipulation of space large soft manipulators,it is much more difficult to achieve precise control not only because of the greater accumulation of errors in the multiple degrees of freedom and nonlinear properties of soft materials at large scales but also because of the inability of directly solving the inverse kinematics in the cases of singular pure elongation.In this work,a model-free intelligent kinematic control strategy is proposed for these problems that exhibit a mapping relationship between the output end-effector position and the input pressure.For multiple-degree-of-freedom robots,especially pneumatic soft manipulators,traditional inverse kinematic modeling methods are complex and inverse Jacobian matrix solution often encounters geometric singularities.To address this issue,this paper proposes an inverse kinematics–multilayer perceptron(IK-MLP)method for soft manipulators.In this strategy,the trained intelligent controller can be applied to control pneumatic manipulators without establishing a traditional inverse kinematic model.The control algorithm is experimentally tested based on the ground experiment system of the space soft manipulator.Simulations and experiments are carried out to validate the given model-free intelligent controller,proving that the IK-MLP method can effectively solve the singularity of inverse kinematics.展开更多
Compared with the traditional manipulator,the hyper‐redundant manipulator has the advantage of high flexibility,which is particularly suitable for all kinds of complex working environments.However,the complex space e...Compared with the traditional manipulator,the hyper‐redundant manipulator has the advantage of high flexibility,which is particularly suitable for all kinds of complex working environments.However,the complex space environment requires the hyper‐redundant manipulator to have stronger obstacle avoidance ability and adaptability.In order to solve the problems of a large amount of calculation and poor obstacle avoidance effects in the path planning of the hyper‐redundant manipulator,this paper introduces the‘backbone curve’approach,which transforms the problem of solving joint path points into the behaviour of determining the backbone curve.After the backbone curve approach is used to design the curve that meets the requirements of obstacle avoidance and the end pose,the least squares fitting and the improved space joint fitting are used to match the plane curve and the space curve respectively,and the angle value of each joint of the manipulator is limited by the algorithm.Furthermore,a fusion obstacle avoidance algorithm is proposed to obtain the joint path points of the hyper‐redundant manipulator.Compared with the classic Jacobian iteration method,this method can avoid obstacles better,has the advantages of simple calculation,high efficiency,and can fully reflect the geometric characteristics of the manipulator.Simulation experiments have proven the feasibility of the algorithm.展开更多
With the rapid development of artificial intelligence(AI),the application of this technology in the medical field is becoming increasingly extensive,along with a gradual increase in the amount of intelligent equipment...With the rapid development of artificial intelligence(AI),the application of this technology in the medical field is becoming increasingly extensive,along with a gradual increase in the amount of intelligent equipment in hospitals.Service robots can save human resources and replace nursing staff to achieve some work.In view of the phenomenon of mobile service robots'grabbing and distribution of patients'drugs in hospitals,a real‐time object detection and positioning system based on image and text information is proposed,which realizes the precise positioning and tracking of the grabbing objects and completes the grasping of a specific object(medicine bottle).The lightweight object detection model NanoDet is used to learn the features of the grasping objects and the object category,and bounding boxes are regressed.Then,the images in the bounding boxes are enhanced to overcome unfavourable factors,such as a small object region.The text detection and recognition model PP‐OCR is used to detect and recognise the enhanced images and extract the text information.The object information provided by the two models is fused,and the text recognition result is matched with the object detection box to achieve the precise posi-tioning of the grasping object.The kernel correlation filter(KCF)tracking algorithm is introduced to achieve real‐time tracking of specific objects to precisely control the robot's grasping.Both deep learning models adopt lightweight networks to facilitate direct deployment.The experiments show that the proposed robot grasping detection system has high reliability,accuracy and real‐time performance.展开更多
基金supported in part by the National Natural Science Foundation of China(91748131,62006229,and 61771471)in part by Young Scientists Fund of the National Natural Science Foundation of China(62303454)+1 种基金in part by the Strategic Priority Research Program of Chinese Academy of Science(XDB32050106)in part by the InnoHK Project.
文摘Humans excel at dexterous manipulation;however,achieving human-level dexterity remains a significant challenge for robots.Technological breakthroughs in the design of anthropomorphic robotic hands,as well as advancements in visual and tactile perception,have demonstrated significant advantages in addressing this issue.However,coping with the inevitable uncertainty caused by unstructured and dynamic environments in human-like dexterous manipulation tasks,especially for anthropomorphic five-fingered hands,remains an open problem.In this paper,we present a focused review of human-like dexterous manipulation for anthropomorphic five-fingered hands.We begin by defining human-like dexterity and outlining the tasks associated with human-like robot dexterous manipulation.Subsequently,we delve into anthropomorphism and anthropomorphic five-fingered hands,covering definitions,robotic design,and evaluation criteria.Furthermore,we review the learning methods for achieving human-like dexterity in anthropomorphic five-fingered hands,including imitation learning,reinforcement learning and their integration.Finally,we discuss the existing challenges and propose future research directions.This review aims to stimulate interest in scientific research and future applications.
基金supported by the National Key Research and Development Program of China(Grant 2022YFB4703000)National Natural Science Foundation of China(Grant 62373054).
文摘Keyhole procedures frequently necessitate passing instruments through narrow,elongated surgical channels,presenting a significant challenge in designing manipulators with compact structures and superior remote dexterity.In this work,we propose a novel 7 degrees‐of‐freedom variable stiffness dexterous surgical manipulator based on a flexible parallel mechanism.The manipulator has an outer diameter of 4.5 mm and is characterized by a hollow structure that incorporates a central channel measuring 1.8 mm.This design facilitates the integration of tool drives and sensors within the manipulator,enhancing its overall functionality.The kinematics and stiffness model of the flexible parallel mechanism are derived.Additionally,a prototype of the proposed manipulator is presented and evaluated through sufficient experiments.We performed a teleoperation test to characterize the model accuracy of the prototype,and the average error is approximately 0.49 mm.Furthermore,we conduct a series of experiments to verify the prototype's performance,including workspace,stiffness,and dexterous operation.The results confirm that the surgical manipulator demonstrates excellent performance in the challenging environment of keyhole procedures.
基金National Defense Science and Technology Foundation Strengthening Plan(2020-JCJQ-QT-039)National Natural Science Foundation of China(grant numbers 11725211 and 52072408).
文摘Given the advantages of softness,lightness,low cost,and interaction safety,inverse kinematic modeling and control of soft actuators has caused a research boom.However,in realizing dexterous manipulation of space large soft manipulators,it is much more difficult to achieve precise control not only because of the greater accumulation of errors in the multiple degrees of freedom and nonlinear properties of soft materials at large scales but also because of the inability of directly solving the inverse kinematics in the cases of singular pure elongation.In this work,a model-free intelligent kinematic control strategy is proposed for these problems that exhibit a mapping relationship between the output end-effector position and the input pressure.For multiple-degree-of-freedom robots,especially pneumatic soft manipulators,traditional inverse kinematic modeling methods are complex and inverse Jacobian matrix solution often encounters geometric singularities.To address this issue,this paper proposes an inverse kinematics–multilayer perceptron(IK-MLP)method for soft manipulators.In this strategy,the trained intelligent controller can be applied to control pneumatic manipulators without establishing a traditional inverse kinematic model.The control algorithm is experimentally tested based on the ground experiment system of the space soft manipulator.Simulations and experiments are carried out to validate the given model-free intelligent controller,proving that the IK-MLP method can effectively solve the singularity of inverse kinematics.
基金State Administration of Science.Technology and Industry for National Defense,Grant/Award Number:JCKY2020404C001。
文摘Compared with the traditional manipulator,the hyper‐redundant manipulator has the advantage of high flexibility,which is particularly suitable for all kinds of complex working environments.However,the complex space environment requires the hyper‐redundant manipulator to have stronger obstacle avoidance ability and adaptability.In order to solve the problems of a large amount of calculation and poor obstacle avoidance effects in the path planning of the hyper‐redundant manipulator,this paper introduces the‘backbone curve’approach,which transforms the problem of solving joint path points into the behaviour of determining the backbone curve.After the backbone curve approach is used to design the curve that meets the requirements of obstacle avoidance and the end pose,the least squares fitting and the improved space joint fitting are used to match the plane curve and the space curve respectively,and the angle value of each joint of the manipulator is limited by the algorithm.Furthermore,a fusion obstacle avoidance algorithm is proposed to obtain the joint path points of the hyper‐redundant manipulator.Compared with the classic Jacobian iteration method,this method can avoid obstacles better,has the advantages of simple calculation,high efficiency,and can fully reflect the geometric characteristics of the manipulator.Simulation experiments have proven the feasibility of the algorithm.
基金National Natural Science Foundation of China under Grants,Grant/Award Number:61973184Young Scholars Program of Shandong University,Weihai,Grant/Award Number:20820211010+1 种基金National Key Research and Development Plan of China under Grant,Grant/Award Number:2020AAA0108903Natural Science Foundation of Shandong Province,Grant/Award Numbers:ZR2020MD041,ZR2020MF077。
文摘With the rapid development of artificial intelligence(AI),the application of this technology in the medical field is becoming increasingly extensive,along with a gradual increase in the amount of intelligent equipment in hospitals.Service robots can save human resources and replace nursing staff to achieve some work.In view of the phenomenon of mobile service robots'grabbing and distribution of patients'drugs in hospitals,a real‐time object detection and positioning system based on image and text information is proposed,which realizes the precise positioning and tracking of the grabbing objects and completes the grasping of a specific object(medicine bottle).The lightweight object detection model NanoDet is used to learn the features of the grasping objects and the object category,and bounding boxes are regressed.Then,the images in the bounding boxes are enhanced to overcome unfavourable factors,such as a small object region.The text detection and recognition model PP‐OCR is used to detect and recognise the enhanced images and extract the text information.The object information provided by the two models is fused,and the text recognition result is matched with the object detection box to achieve the precise posi-tioning of the grasping object.The kernel correlation filter(KCF)tracking algorithm is introduced to achieve real‐time tracking of specific objects to precisely control the robot's grasping.Both deep learning models adopt lightweight networks to facilitate direct deployment.The experiments show that the proposed robot grasping detection system has high reliability,accuracy and real‐time performance.
