Soft grippers research is gaining increasing attention for their flexibility.However,the conventional soft gripper primar-ily focuses on soft fingers,without considering the palm.This makes grasping forces concentrate...Soft grippers research is gaining increasing attention for their flexibility.However,the conventional soft gripper primar-ily focuses on soft fingers,without considering the palm.This makes grasping forces concentrated in the fingertip areas,resulting in objects being prone to damage and instability during handling,especially for delicate items.Additionally,pre-transportation classification faces challenges:tactile methods are complex,visual methods are environment-sensitive,and both struggle with similar objects.To address these problems,inspired by the human hand's transition between finger grasp and palm support and the lotus's hierarchical structure,this paper proposes a dual-layer gripper,named IOSGrip-per.It features four pneumatic soft fingers and a rotational soft-rigid palm.Through coordinated control of the fingers and palm,it transitions concentrated fingertip squeeze force to distributed palm support force,reducing squeeze force and squeeze duration.Moreover,it integrates a range sensor and four load cells,enabling stable and accurate measurements of the object's height and weight.Furthermore,a classifier is developed based on K-nearest neighbor algorithm,allowing real-time object classification.Experiments demonstrate that compared to only using soft fingers,the IOSGripper signifi-cantly reduces the squeeze force on the objects(with 0 N squeeze force during palm support)and damage on the delicate object,while improving grasping stability.Its height and weight measurement errors are within 2 mm and 1 g,respectively.And it achieves high accuracy in three test scenarios,including classifying similar objects.This study provides useful insights for the design of soft grippers capable of human-like grasping and sorting tasks.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
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
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
In recent years,robotic arm grasping has become a pivotal task in the field of robotics,with applications spanning from industrial automation to healthcare.The optimization of grasping strategies plays a crucial role ...In recent years,robotic arm grasping has become a pivotal task in the field of robotics,with applications spanning from industrial automation to healthcare.The optimization of grasping strategies plays a crucial role in enhancing the effectiveness,efficiency,and reliability of robotic systems.This paper presents a novel approach to optimizing robotic arm grasping strategies based on deep reinforcement learning(DRL).Through the utilization of advanced DRL algorithms,such as Q-Learning,Deep Q-Networks(DQN),Policy Gradient Methods,and Proximal Policy Optimization(PPO),the study aims to improve the performance of robotic arms in grasping objects with varying shapes,sizes,and environmental conditions.The paper provides a detailed analysis of the various deep reinforcement learning methods used for grasping strategy optimization,emphasizing the strengths and weaknesses of each algorithm.It also presents a comprehensive framework for training the DRL models,including simulation environment setup,the optimization process,and the evaluation metrics for grasping success.The results demonstrate that the proposed approach significantly enhances the accuracy and stability of the robotic arm in performing grasping tasks.The study further explores the challenges in training deep reinforcement learning models for real-time robotic applications and offers solutions for improving the efficiency and reliability of grasping strategies.展开更多
Grasping is one of the most fundamental operations in modern robotics applications.While deep rein-forcement learning(DRL)has demonstrated strong potential in robotics,there is too much emphasis on maximizing the cumu...Grasping is one of the most fundamental operations in modern robotics applications.While deep rein-forcement learning(DRL)has demonstrated strong potential in robotics,there is too much emphasis on maximizing the cumulative reward in executing tasks,and the potential safety risks are often ignored.In this paper,an optimization method based on safe reinforcement learning(Safe RL)is proposed to address the robotic grasping problem under safety constraints.Specifically,considering the obstacle avoidance constraints of the system,the grasping problem of the manipulator is modeled as a Constrained Markov Decision Process(CMDP).The Lagrange multiplier and a dynamic weighted mechanism are introduced into the Proximal Policy Optimization(PPO)framework,leading to the development of the dynamic weighted Lagrange PPO(DWL-PPO)algorithm.The behavior of violating safety constraints is punished while the policy is optimized in this proposed method.In addition,the orientation control of the end-effector is included in the reward function,and a compound reward function adapted to changes in pose is designed.Ultimately,the efficacy and advantages of the suggested method are proved by extensive training and testing in the Pybullet simulator.The results of grasping experiments reveal that the recommended approach provides superior safety and efficiency compared with other advanced RL methods and achieves a good trade-off between model learning and risk aversion.展开更多
Deep learning has become integral to robotics,particularly in tasks such as robotic grasping,where objects often exhibit diverse shapes,textures,and physical properties.In robotic grasping tasks,due to the diverse cha...Deep learning has become integral to robotics,particularly in tasks such as robotic grasping,where objects often exhibit diverse shapes,textures,and physical properties.In robotic grasping tasks,due to the diverse characteristics of the targets,frequent adjustments to the network architecture and parameters are required to avoid a decrease in model accuracy,which presents a significant challenge for non-experts.Neural Architecture Search(NAS)provides a compelling method through the automated generation of network architectures,enabling the discovery of models that achieve high accuracy through efficient search algorithms.Compared to manually designed networks,NAS methods can significantly reduce design costs,time expenditure,and improve model performance.However,such methods often involve complex topological connections,and these redundant structures can severely reduce computational efficiency.To overcome this challenge,this work puts forward a robotic grasp detection framework founded on NAS.The method automatically designs a lightweight network with high accuracy and low topological complexity,effectively adapting to the target object to generate the optimal grasp pose,thereby significantly improving the success rate of robotic grasping.Additionally,we use Class Activation Mapping(CAM)as an interpretability tool,which captures sensitive information during the perception process through visualized results.The searched model achieved competitive,and in some cases superior,performance on the Cornell and Jacquard public datasets,achieving accuracies of 98.3%and 96.8%,respectively,while sustaining a detection speed of 89 frames per second with only 0.41 million parameters.To further validate its effectiveness beyond benchmark evaluations,we conducted real-world grasping experiments on a UR5 robotic arm,where the model demonstrated reliable performance across diverse objects and high grasp success rates,thereby confirming its practical applicability in robotic manipulation tasks.展开更多
When the space robot captures a floating target, contact impact occurs inevitably and frequently between the manipulator hand and the target, which seriously impacts the position and attitude of the robot and grasping...When the space robot captures a floating target, contact impact occurs inevitably and frequently between the manipulator hand and the target, which seriously impacts the position and attitude of the robot and grasping security. ＂Dynamic grasping area＂ is introduced to describe the collision process of manipulator grasping target, and grasping area control equation is established. By analyzing the impact of grasping control parameters, base and target mass on the grasping process and combining the life experience, it is found that if the product of speed control parameter and dB adjustment parameter is close to but smaller than the minimum grasping speed, collision impact in the grasping process could be reduced greatly, and then an ideal grasping strategy is proposed. Simulation results indicate that during the same period, the strategy grasping is superior to the accelerating grasping, in that the amplitude of impact force is reduced to 20%, and the attitude control torque is reduced to 15%, and the impact on the robot is eliminated significantly. The results would have important academic value and engineering significance.展开更多
Endoscopic submucosal dissection(ESD) has allowed the achievement of histologically curative en bloc resection of gastrointestinal neoplasms regardless of size,permitting the resection of previously non-resectable tum...Endoscopic submucosal dissection(ESD) has allowed the achievement of histologically curative en bloc resection of gastrointestinal neoplasms regardless of size,permitting the resection of previously non-resectable tumors.The ESD technique for treatment of early gastric cancer has spread rapidly in Japan and a few other Asian countries due to its excellent eradication rate compared to endoscopic mucosal resection.Although numerous electrosurgical knives have been developed for ESD,technical difficulties and high complication rates(bleeding and perforation) have limited their use worldwide.We developed the grasping type scissor forceps(GSF) to resolve such ESD-related problems.Our animal and preliminary clinical studies showed that ESD using GSF is a safe(no intraoperative complication) and technically efficient(curative en bloc resection rate 92%) method for dissection of early gastrointestinal tumors.The use of GSF is a promising option for performing ESD on early stage GI tract tumors both safely and effectively.展开更多
It is important for robotic hands to obtain optimal grasping performance inthe meanwhile balancing external forces and maintaining grasp stability. The problem of forceoptimization of grasping is solved in the space o...It is important for robotic hands to obtain optimal grasping performance inthe meanwhile balancing external forces and maintaining grasp stability. The problem of forceoptimization of grasping is solved in the space of joint torques. A measure of grasping performanceis presented to protect joint actuators from working in heavy payloads. The joint torques arecalculated for the optimal performance under the frictional constraints and the physical limits ofmotor outputs. By formulating the grasping forces into the explicit function of joint torques, thefrictional constraints imposed on the grasping forces are transformed into the constraints on jointtorques. Without further simplification, the nonlinear frictional constraints can be simply handledin the process of optimization. Two numerical examples demonstrate the simplicity and effectivenessof the approach.展开更多
Grasping is a significant yet challenging task for the robots. In this paper, the grasping problem for a class of dexterous robotic hands is investigated based on the novel concept of constrained region in environment...Grasping is a significant yet challenging task for the robots. In this paper, the grasping problem for a class of dexterous robotic hands is investigated based on the novel concept of constrained region in environment, which is inspired by the grasping operations of the human beings. More precisely, constrained region in environment is formed by the environment, which integrates a bio-inspired co-sensing framework. By utilizing the concept of constrained region in environment, the grasping by robots can be effectively accomplished with relatively low-precision sensors. For the grasping of dexterous robotic hands, the attractive region in environment is first established by model primitives in the configuration space to generate offline grasping planning. Then, online dynamic adjustment is implemented by integrating the visual sensory and force sensory information, such that the uncertainty can be further eliminated and certain compliance can be obtained. In the end, an experimental example of BarrettHand is provided to show the effectiveness of our proposed grasping strategy based on constrained region in environment.展开更多
The stable grasping gesture of a novel cable-driven robotic hand is analyzed. The robotic hand is underactuated, using tendon-pulley transmission and a parallel four-linkage mechanism to realize grasp. The structure d...The stable grasping gesture of a novel cable-driven robotic hand is analyzed. The robotic hand is underactuated, using tendon-pulley transmission and a parallel four-linkage mechanism to realize grasp. The structure design and a basic grasping strategy of one finger was introduced. According to the established round object enveloping grasp model, the relationship between the contacting and driving forces in a finger and stable grasping conditions were expounded. A method of interpolation and iteration was proposed to obtain the stable grasping gesture of the cable-driven hand grasping a round target. Quasi-statics analysis in ADAMS validated the variation of grasping forces, which illustrated the feasibility and validity of the proposed analytical method. Three basic types of grasping gestures of the underactuated hand were obtained on the basis of the relationship between the contact forces and position of a grasped object.展开更多
Robot hands have been developing during the last few decades. There are many mechanical structures and analyti?cal methods for di erent hands. But many tough problems still limit robot hands to apply in homelike envir...Robot hands have been developing during the last few decades. There are many mechanical structures and analyti?cal methods for di erent hands. But many tough problems still limit robot hands to apply in homelike environment. The ability of grasping objects covering a large range of sizes and various shapes is fundamental for a home service robot to serve people better. In this paper, a new grasping mode based on a novel sucked?type underactuated(STU) hand is proposed. By combining the flexibility of soft material and the e ect of suction cups, the STU hand can grasp objects with a wide range of sizes, shapes and materials. Moreover, the new grasping mode is suitable for some situations where the force closure is failure. In this paper, we deduce the e ective range of sizes of objects which our hand using the new grasping mode can grasp. Thanks to the new grasping mode, the ratio of grasping size between the biggest object and the smallest is beyond 40, which makes it possible for our robot hand to grasp diverse objects in our daily life. For example, the STU hand can grasp a soccer(220 mm diameter, 420 g) and a fountain pen(9 mm diameter, 9 g). What’s more, we use the rigid body equilibrium conditions to analysis the force condition. Experiment evaluates the high load capacity, stability of the new grasping mode and displays the versatility of the STU hand. The STU hand has a wide range of applications especially in unstructured environment.展开更多
In this paper,we propose a fully Soft Bionic Grasping Device(SBGD),which has advantages in automatically adjusting the grasping range,variable stiffness,and controllable bending shape.This device consists of soft grip...In this paper,we propose a fully Soft Bionic Grasping Device(SBGD),which has advantages in automatically adjusting the grasping range,variable stiffness,and controllable bending shape.This device consists of soft gripper structures and a soft bionic bracket structure.We adopt the local thin-walled design in the soft gripper structures.This design improves the grippers’bending efficiency,and imitate human finger’s segmental bending function.In addition,this work also proposes a pneumatic soft bionic bracket structure,which not only can fix grippers,but also can automatically adjust the grasping space by imitating the human adjacent fingers’opening and closing movements.Due to the above advantages,the SBGD can grasp larger or smaller objects than the regular grasping devices.Particularly,to grasp small objects reliably,we further present a new Pinching Grasping(PG)method.The great performance of the fully SBGD is verified by experiments.This work will promote innovative development of the soft bionic grasping robots,and greatly meet the applications of dexterous grasping multi-size and multi-shape objects.展开更多
A mathematical model expressing the motion of a pair of multi-DOF robot fingers with hemi-spherical ends, grasping a 3-D rigid object with parallel fiat surfaces, is derived, together with non-holonomic constraints. B...A mathematical model expressing the motion of a pair of multi-DOF robot fingers with hemi-spherical ends, grasping a 3-D rigid object with parallel fiat surfaces, is derived, together with non-holonomic constraints. By referring to the fact that humans grasp an object in the form of precision prehension, dynamically and stably by opposable forces, between the thumb and another finger （index or middle finger）, a simple control signal constructed from finger-thumb opposition is proposed, and shown to realize stable grasping in a dynamic sense without using object information or external sensing （this is called ＂blind grasp＂ in this paper）. The stability of grasping with force/torque balance under non-holonomic constraints is analyzed on the basis of a new concept named ＂stability on a manifold＂. Preliminary simulation results are shown to verify the validity of the theoretical results.展开更多
In order to ensure that the off-line arm of a two-arm-wheel combined inspection robot can reliably grasp the line in case of autonomous obstacle crossing,a control method is proposed for line grasping based on hand-ey...In order to ensure that the off-line arm of a two-arm-wheel combined inspection robot can reliably grasp the line in case of autonomous obstacle crossing,a control method is proposed for line grasping based on hand-eye visual servo.On the basis of the transmission line's geometrical characteristics and the camera's imaging principle,a line recognition and extraction method based on structure constraint is designed.The line's intercept and inclination are defined in an imaging space to represent the robot's change of pose and a law governing the pose decoupling servo control is developed.Under the integrated consideration of the influence of light intensity and background change,noise(from the camera itself and electromagnetic field)as well as the robot's kinetic inertia on the robot's imaging quality in the course of motion and the grasping control precision,a servo controller for grasping the line of the robot's off-line arm is designed with the method of fuzzy control.An experiment is conducted on a 1:1 simulation line using an inspection robot and the robot is put into on-line operation on a real overhead transmission line,where the robot can grasp the line within 18 s in the case of autonomous obstacle-crossing.The robot's autonomous line-grasping function is realized without manual intervention and the robot can grasp the line in a precise,reliable and efficient manner,thus the need of actual operation can be satisfied.展开更多
The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We...The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.展开更多
基金the Major research program of national natural science foundation of China(91848206).
文摘Soft grippers research is gaining increasing attention for their flexibility.However,the conventional soft gripper primar-ily focuses on soft fingers,without considering the palm.This makes grasping forces concentrated in the fingertip areas,resulting in objects being prone to damage and instability during handling,especially for delicate items.Additionally,pre-transportation classification faces challenges:tactile methods are complex,visual methods are environment-sensitive,and both struggle with similar objects.To address these problems,inspired by the human hand's transition between finger grasp and palm support and the lotus's hierarchical structure,this paper proposes a dual-layer gripper,named IOSGrip-per.It features four pneumatic soft fingers and a rotational soft-rigid palm.Through coordinated control of the fingers and palm,it transitions concentrated fingertip squeeze force to distributed palm support force,reducing squeeze force and squeeze duration.Moreover,it integrates a range sensor and four load cells,enabling stable and accurate measurements of the object's height and weight.Furthermore,a classifier is developed based on K-nearest neighbor algorithm,allowing real-time object classification.Experiments demonstrate that compared to only using soft fingers,the IOSGripper signifi-cantly reduces the squeeze force on the objects(with 0 N squeeze force during palm support)and damage on the delicate object,while improving grasping stability.Its height and weight measurement errors are within 2 mm and 1 g,respectively.And it achieves high accuracy in three test scenarios,including classifying similar objects.This study provides useful insights for the design of soft grippers capable of human-like grasping and sorting tasks.
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.
文摘In recent years,robotic arm grasping has become a pivotal task in the field of robotics,with applications spanning from industrial automation to healthcare.The optimization of grasping strategies plays a crucial role in enhancing the effectiveness,efficiency,and reliability of robotic systems.This paper presents a novel approach to optimizing robotic arm grasping strategies based on deep reinforcement learning(DRL).Through the utilization of advanced DRL algorithms,such as Q-Learning,Deep Q-Networks(DQN),Policy Gradient Methods,and Proximal Policy Optimization(PPO),the study aims to improve the performance of robotic arms in grasping objects with varying shapes,sizes,and environmental conditions.The paper provides a detailed analysis of the various deep reinforcement learning methods used for grasping strategy optimization,emphasizing the strengths and weaknesses of each algorithm.It also presents a comprehensive framework for training the DRL models,including simulation environment setup,the optimization process,and the evaluation metrics for grasping success.The results demonstrate that the proposed approach significantly enhances the accuracy and stability of the robotic arm in performing grasping tasks.The study further explores the challenges in training deep reinforcement learning models for real-time robotic applications and offers solutions for improving the efficiency and reliability of grasping strategies.
文摘Grasping is one of the most fundamental operations in modern robotics applications.While deep rein-forcement learning(DRL)has demonstrated strong potential in robotics,there is too much emphasis on maximizing the cumulative reward in executing tasks,and the potential safety risks are often ignored.In this paper,an optimization method based on safe reinforcement learning(Safe RL)is proposed to address the robotic grasping problem under safety constraints.Specifically,considering the obstacle avoidance constraints of the system,the grasping problem of the manipulator is modeled as a Constrained Markov Decision Process(CMDP).The Lagrange multiplier and a dynamic weighted mechanism are introduced into the Proximal Policy Optimization(PPO)framework,leading to the development of the dynamic weighted Lagrange PPO(DWL-PPO)algorithm.The behavior of violating safety constraints is punished while the policy is optimized in this proposed method.In addition,the orientation control of the end-effector is included in the reward function,and a compound reward function adapted to changes in pose is designed.Ultimately,the efficacy and advantages of the suggested method are proved by extensive training and testing in the Pybullet simulator.The results of grasping experiments reveal that the recommended approach provides superior safety and efficiency compared with other advanced RL methods and achieves a good trade-off between model learning and risk aversion.
基金funded by Guangdong Basic and Applied Basic Research Foundation(2023B1515120064)National Natural Science Foundation of China(62273097).
文摘Deep learning has become integral to robotics,particularly in tasks such as robotic grasping,where objects often exhibit diverse shapes,textures,and physical properties.In robotic grasping tasks,due to the diverse characteristics of the targets,frequent adjustments to the network architecture and parameters are required to avoid a decrease in model accuracy,which presents a significant challenge for non-experts.Neural Architecture Search(NAS)provides a compelling method through the automated generation of network architectures,enabling the discovery of models that achieve high accuracy through efficient search algorithms.Compared to manually designed networks,NAS methods can significantly reduce design costs,time expenditure,and improve model performance.However,such methods often involve complex topological connections,and these redundant structures can severely reduce computational efficiency.To overcome this challenge,this work puts forward a robotic grasp detection framework founded on NAS.The method automatically designs a lightweight network with high accuracy and low topological complexity,effectively adapting to the target object to generate the optimal grasp pose,thereby significantly improving the success rate of robotic grasping.Additionally,we use Class Activation Mapping(CAM)as an interpretability tool,which captures sensitive information during the perception process through visualized results.The searched model achieved competitive,and in some cases superior,performance on the Cornell and Jacquard public datasets,achieving accuracies of 98.3%and 96.8%,respectively,while sustaining a detection speed of 89 frames per second with only 0.41 million parameters.To further validate its effectiveness beyond benchmark evaluations,we conducted real-world grasping experiments on a UR5 robotic arm,where the model demonstrated reliable performance across diverse objects and high grasp success rates,thereby confirming its practical applicability in robotic manipulation tasks.
基金Program for Innovative Research Team in University(IRT520)CAST of China (20090703)
文摘When the space robot captures a floating target, contact impact occurs inevitably and frequently between the manipulator hand and the target, which seriously impacts the position and attitude of the robot and grasping security. ＂Dynamic grasping area＂ is introduced to describe the collision process of manipulator grasping target, and grasping area control equation is established. By analyzing the impact of grasping control parameters, base and target mass on the grasping process and combining the life experience, it is found that if the product of speed control parameter and dB adjustment parameter is close to but smaller than the minimum grasping speed, collision impact in the grasping process could be reduced greatly, and then an ideal grasping strategy is proposed. Simulation results indicate that during the same period, the strategy grasping is superior to the accelerating grasping, in that the amplitude of impact force is reduced to 20%, and the attitude control torque is reduced to 15%, and the impact on the robot is eliminated significantly. The results would have important academic value and engineering significance.
文摘Endoscopic submucosal dissection(ESD) has allowed the achievement of histologically curative en bloc resection of gastrointestinal neoplasms regardless of size,permitting the resection of previously non-resectable tumors.The ESD technique for treatment of early gastric cancer has spread rapidly in Japan and a few other Asian countries due to its excellent eradication rate compared to endoscopic mucosal resection.Although numerous electrosurgical knives have been developed for ESD,technical difficulties and high complication rates(bleeding and perforation) have limited their use worldwide.We developed the grasping type scissor forceps(GSF) to resolve such ESD-related problems.Our animal and preliminary clinical studies showed that ESD using GSF is a safe(no intraoperative complication) and technically efficient(curative en bloc resection rate 92%) method for dissection of early gastrointestinal tumors.The use of GSF is a promising option for performing ESD on early stage GI tract tumors both safely and effectively.
基金This project is supported by National Natural Science Foundation of China (No.59985001)Doctoral Grant of Education Ministry of China (No.2000000605)
文摘It is important for robotic hands to obtain optimal grasping performance inthe meanwhile balancing external forces and maintaining grasp stability. The problem of forceoptimization of grasping is solved in the space of joint torques. A measure of grasping performanceis presented to protect joint actuators from working in heavy payloads. The joint torques arecalculated for the optimal performance under the frictional constraints and the physical limits ofmotor outputs. By formulating the grasping forces into the explicit function of joint torques, thefrictional constraints imposed on the grasping forces are transformed into the constraints on jointtorques. Without further simplification, the nonlinear frictional constraints can be simply handledin the process of optimization. Two numerical examples demonstrate the simplicity and effectivenessof the approach.
基金supported by National Natural Science Foundation of China(No.61210009)Beijing Municipal Science and Technology(Nos.D16110400140000 and D161100001416001)+1 种基金Fundamental Research Funds for the Central Universities(No.FRF-TP-15-115A1)the Strategic Priority Research Program of the CAS(No.XDB02080003)
文摘Grasping is a significant yet challenging task for the robots. In this paper, the grasping problem for a class of dexterous robotic hands is investigated based on the novel concept of constrained region in environment, which is inspired by the grasping operations of the human beings. More precisely, constrained region in environment is formed by the environment, which integrates a bio-inspired co-sensing framework. By utilizing the concept of constrained region in environment, the grasping by robots can be effectively accomplished with relatively low-precision sensors. For the grasping of dexterous robotic hands, the attractive region in environment is first established by model primitives in the configuration space to generate offline grasping planning. Then, online dynamic adjustment is implemented by integrating the visual sensory and force sensory information, such that the uncertainty can be further eliminated and certain compliance can be obtained. In the end, an experimental example of BarrettHand is provided to show the effectiveness of our proposed grasping strategy based on constrained region in environment.
基金The National Natural Science Foundation of China(No.U1613201,51275107)Shenzhen Research Funds(No.JCYJ20170413104438332)
文摘The stable grasping gesture of a novel cable-driven robotic hand is analyzed. The robotic hand is underactuated, using tendon-pulley transmission and a parallel four-linkage mechanism to realize grasp. The structure design and a basic grasping strategy of one finger was introduced. According to the established round object enveloping grasp model, the relationship between the contacting and driving forces in a finger and stable grasping conditions were expounded. A method of interpolation and iteration was proposed to obtain the stable grasping gesture of the cable-driven hand grasping a round target. Quasi-statics analysis in ADAMS validated the variation of grasping forces, which illustrated the feasibility and validity of the proposed analytical method. Three basic types of grasping gestures of the underactuated hand were obtained on the basis of the relationship between the contact forces and position of a grasped object.
基金National Natural Science Foundation of China(Grant Nos.U1613216,61573333)
文摘Robot hands have been developing during the last few decades. There are many mechanical structures and analyti?cal methods for di erent hands. But many tough problems still limit robot hands to apply in homelike environment. The ability of grasping objects covering a large range of sizes and various shapes is fundamental for a home service robot to serve people better. In this paper, a new grasping mode based on a novel sucked?type underactuated(STU) hand is proposed. By combining the flexibility of soft material and the e ect of suction cups, the STU hand can grasp objects with a wide range of sizes, shapes and materials. Moreover, the new grasping mode is suitable for some situations where the force closure is failure. In this paper, we deduce the e ective range of sizes of objects which our hand using the new grasping mode can grasp. Thanks to the new grasping mode, the ratio of grasping size between the biggest object and the smallest is beyond 40, which makes it possible for our robot hand to grasp diverse objects in our daily life. For example, the STU hand can grasp a soccer(220 mm diameter, 420 g) and a fountain pen(9 mm diameter, 9 g). What’s more, we use the rigid body equilibrium conditions to analysis the force condition. Experiment evaluates the high load capacity, stability of the new grasping mode and displays the versatility of the STU hand. The STU hand has a wide range of applications especially in unstructured environment.
基金This work was funded by the National Natural Science Foundation of Chinaunder Grant 62073305the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(Nos.CUG170610 and CUGGC02).
文摘In this paper,we propose a fully Soft Bionic Grasping Device(SBGD),which has advantages in automatically adjusting the grasping range,variable stiffness,and controllable bending shape.This device consists of soft gripper structures and a soft bionic bracket structure.We adopt the local thin-walled design in the soft gripper structures.This design improves the grippers’bending efficiency,and imitate human finger’s segmental bending function.In addition,this work also proposes a pneumatic soft bionic bracket structure,which not only can fix grippers,but also can automatically adjust the grasping space by imitating the human adjacent fingers’opening and closing movements.Due to the above advantages,the SBGD can grasp larger or smaller objects than the regular grasping devices.Particularly,to grasp small objects reliably,we further present a new Pinching Grasping(PG)method.The great performance of the fully SBGD is verified by experiments.This work will promote innovative development of the soft bionic grasping robots,and greatly meet the applications of dexterous grasping multi-size and multi-shape objects.
基金This work was supported in part by the Grant-in-Aid for Exploratory Research of the JSPS (No. 16656085).
文摘A mathematical model expressing the motion of a pair of multi-DOF robot fingers with hemi-spherical ends, grasping a 3-D rigid object with parallel fiat surfaces, is derived, together with non-holonomic constraints. By referring to the fact that humans grasp an object in the form of precision prehension, dynamically and stably by opposable forces, between the thumb and another finger （index or middle finger）, a simple control signal constructed from finger-thumb opposition is proposed, and shown to realize stable grasping in a dynamic sense without using object information or external sensing （this is called ＂blind grasp＂ in this paper）. The stability of grasping with force/torque balance under non-holonomic constraints is analyzed on the basis of a new concept named ＂stability on a manifold＂. Preliminary simulation results are shown to verify the validity of the theoretical results.
基金Project(2006AA04Z202)supported by the National High Technology Research and Development Program of ChinaProject(51105281)supported by the National Natural Science Foundation of China
文摘In order to ensure that the off-line arm of a two-arm-wheel combined inspection robot can reliably grasp the line in case of autonomous obstacle crossing,a control method is proposed for line grasping based on hand-eye visual servo.On the basis of the transmission line's geometrical characteristics and the camera's imaging principle,a line recognition and extraction method based on structure constraint is designed.The line's intercept and inclination are defined in an imaging space to represent the robot's change of pose and a law governing the pose decoupling servo control is developed.Under the integrated consideration of the influence of light intensity and background change,noise(from the camera itself and electromagnetic field)as well as the robot's kinetic inertia on the robot's imaging quality in the course of motion and the grasping control precision,a servo controller for grasping the line of the robot's off-line arm is designed with the method of fuzzy control.An experiment is conducted on a 1:1 simulation line using an inspection robot and the robot is put into on-line operation on a real overhead transmission line,where the robot can grasp the line within 18 s in the case of autonomous obstacle-crossing.The robot's autonomous line-grasping function is realized without manual intervention and the robot can grasp the line in a precise,reliable and efficient manner,thus the need of actual operation can be satisfied.
文摘The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.
