The paper proposes a novel multi-legged robot with pitch adjustive units aiming at obstacle surmounting.With only 6 degrees of freedom,the robot with 16 mechanical legs walks steadily and surmounts the obstacles on th...The paper proposes a novel multi-legged robot with pitch adjustive units aiming at obstacle surmounting.With only 6 degrees of freedom,the robot with 16 mechanical legs walks steadily and surmounts the obstacles on the complex terrain.The leg unit with adjustive pitch provides a large workspace and empowers the legs to climb up obstacles in large sizes,which enhances the obstacle surmounting capability.The pitch adjustment in leg unit requires as few independent adjusting actuators as possible.Based on the kinematic analysis of the mechanical leg,the biped and quadruped leg units with adjustive pitch are analyzed and compared.The configuration of the robot is designed to obtain a compact structure and pragmatic performance.The uncertainty of the obstacle size and position in the surmounting process is taken into consideration and the parameters of the adjustments and the feasible strategies for obstacle surmounting are presented.Then the 3D virtual model and the robot prototype are built and the multi-body dynamic simulations and prototype experiments are carried out.The results from the simulations and the experiments show that the robot possesses good obstacle surmounting capabilities.展开更多
Real-time slip detection and state estimation are crucial for locomotion control,facilitating posture adjustment and stability recovery of multi-legged robots moving on slippery terrain.However,existing proprioceptive...Real-time slip detection and state estimation are crucial for locomotion control,facilitating posture adjustment and stability recovery of multi-legged robots moving on slippery terrain.However,existing proprioceptive methods rely on the fixed-contact assumption with fixed noise and suffer from low accuracy when multiple legs slip simultaneously.This paper proposes a novel proprioceptive approach for multi-legged robots moving in slippery scenarios to cope with slippage of multiple legs.In slip detection,the proprioceptive states of the robot are fed into a convolutional neural network to detect slip event(s)of the robot,enabling accurate identification of slipping legs even under simultaneous multi-leg slippage.For state estimation,an invariant extended Kalman filter is employed to fuse the motion information with the detected slip event(s)to obtain the robot state.By incorporating slip event(s)and foot velocity into the system motion equation of the filter,the proposed method better leverages leg odometry information and achieves more precise state estimation compared with existing methods.Simulations on a quadruped and a hexapod demonstrate the effectiveness and increased accuracy during multi-leg slippage.Experimental results for the quadruped robot show that the proposed approach achieves a 48% reduction in the root mean square error and a 47%reduction in the maximum error in velocity estimation under severe multi-leg slippage compared with the existing methods.展开更多
In order to achieve omnidirectional locomotion on rough terrain with multi-legged biomimetic robot,a free gait generation approach is proposed based on local rules.The phase coordinates of each operation leg was estab...In order to achieve omnidirectional locomotion on rough terrain with multi-legged biomimetic robot,a free gait generation approach is proposed based on local rules.The phase coordinates of each operation leg was established according to the motion task and a universal depiction of leg-end locomotion was implemented;the mathematical relation of gait pattern and walking velocity of multi-legged robot was put forward;combined polynomial curve was adopted to generate the leg-end trajectory,which was capable of accomplishing walking missions and accommodating to landform conditions;a distributed network of local rules for gait control was constructed based on a set of local rules operating between adjacent legs.In the simulation experiments,adaptive regulation of inter-leg phase sequence,omnidirectional locomotion and ground accommodation were realized.Moreover,statically stable free gait was obtained simultaneously,which provided multi-legged robot with the capability of walking on irregular terrain reliably and expeditiously.展开更多
In this paper,we propose a new gait planning method for a multi-legged robot which has only 1 degree-of-freedom in each leg and has a passive body joint between two body segments.We firstly introduce the Finite State ...In this paper,we propose a new gait planning method for a multi-legged robot which has only 1 degree-of-freedom in each leg and has a passive body joint between two body segments.We firstly introduce the Finite State Machine(FSM)to the undulatory gait planning method of the 2n-legged robot.Then,the undulatory gait sequence for straight line motion is achieved by undulations motion.The idea that legged locomotion is achievable by less actuation of 2n-legged robot as well as the gait planning methods are verified finally by simulations and experiments.展开更多
Existing errors in the structure and kinematic parameters of multi-legged walking robots,the motion trajectory of robot will diverge from the ideal sports requirements in movement.Since the existing error compensation...Existing errors in the structure and kinematic parameters of multi-legged walking robots,the motion trajectory of robot will diverge from the ideal sports requirements in movement.Since the existing error compensation is usually used for control compensation of manipulator arm,the error compensation of multi-legged robots has seldom been explored.In order to reduce the kinematic error of robots,a motion error compensation method based on the feedforward for multi-legged mobile robots is proposed to improve motion precision of a mobile robot.The locus error of a robot body is measured,when robot moves along a given track.Error of driven joint variables is obtained by error calculation model in terms of the locus error of robot body.Error value is used to compensate driven joint variables and modify control model of robot,which can drive the robots following control model modified.The model of the relation between robot＇s locus errors and kinematic variables errors is set up to achieve the kinematic error compensation.On the basis of the inverse kinematics of a multi-legged walking robot,the relation between error of the motion trajectory and driven joint variables of robots is discussed.Moreover,the equation set is obtained,which expresses relation among error of driven joint variables,structure parameters and error of robot＇s locus.Take MiniQuad as an example,when the robot MiniQuad moves following beeline tread,motion error compensation is studied.The actual locus errors of the robot body are measured before and after compensation in the test.According to the test,variations of the actual coordinate value of the robot centroid in x-direction and z-direction are reduced more than one time.The kinematic errors of robot body are reduced effectively by the use of the motion error compensation method based on the feedforward.展开更多
The semi-round rigid feet would cause position-posture deviation problem because the actual foothold position is hardly known due to the rolling effect of the semi-round rigid feet during the robot walking. The positi...The semi-round rigid feet would cause position-posture deviation problem because the actual foothold position is hardly known due to the rolling effect of the semi-round rigid feet during the robot walking. The position-posture deviation problem may harm to the stability and the harmony of the robot, or even makes the robot tip over and fail to walk forward. Focused on the position-posture deviation problem of multi-legged walking robots with semi-round rigid feet, a new method of position-posture closed-loop control is proposed to solve the position-posture deviation problem caused by semi-round rigid feet, based on the inverse velocity kinematics of the multi-legged walking robots. The position-posture closed-loop control is divided into two parts: the position closed-loop control and the posture closed-loop control. Thus, the position-posture control for the robot which is a tight coupling and nonlinear system is decoupled. Co-simulations of position-posture open-loop control and position-posture closed-loop control by MATLAB and ADAMS are implemented, respectively. The co-simulation results verify that the position-posture closed-loop control performs well in solving the position-posture deviation problem caused by semi-round rigid feet.展开更多
This paper presents a new kind of leg mechanism with which the wall climbing robot can easily perform the ground to wall transition by itself.To get its walking envelope and limit position,the forward/inverse kinem...This paper presents a new kind of leg mechanism with which the wall climbing robot can easily perform the ground to wall transition by itself.To get its walking envelope and limit position,the forward/inverse kinematics and the statics of the mechanism are solved.All of these lay the foundation for ground to wall transition gait programing,mechanism parameter selection and optimization.展开更多
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
The Chinese SME’s vision shows the bright side of humanoid robots:rather than replacing human workers,they are handling everyday mechanical tasks,leaving human staff to focus on improving service and emotional experi...The Chinese SME’s vision shows the bright side of humanoid robots:rather than replacing human workers,they are handling everyday mechanical tasks,leaving human staff to focus on improving service and emotional experiences.IN the Jinqiao Economic and Technological Development Zone in Pudong New Area,Shanghai,KEENON Robotics,a national-level“Little Giant”(innovative SME),is leading the transformation of the service robots industry.Amid the wave of embodied intelligence development,the humanoid service robots created by this company have become a focal point of the industry and businesses alike.展开更多
Objective:Robotic colorectal surgery(RCS)provides a stable,magnifiedthree-dimensional visual field and enhanced ergonomics enabling precise dissection and tremor suppression.We postulate that this technique is associa...Objective:Robotic colorectal surgery(RCS)provides a stable,magnifiedthree-dimensional visual field and enhanced ergonomics enabling precise dissection and tremor suppression.We postulate that this technique is associated with less tissue trauma and improved postoperative outcomes than laparoscopic colorectal surgery(LCS).This study aimed to explore the inflammatoryresponse following RCS by measuring postoperative C-reactive protein(CRP)levels and compare them with LCS data reported in the literature.Methods:This single centre retrospective study included consecutive elective robotic colon and rectum resections via the da Vinci®Xi platform for benign and malignant colorectal tumours,performed by a single surgeon between January 2017 and December 2023 at the Sydney Adventist Hospital,Sydney.CRP values were measured on post-operative days(PODs)3 and 5.A narrative review of the literature was performed via EMBASE,MEDLINE via PubMed and Google Scholar from inception to December 2024 for comparative CRP values following LCS.Descriptive statistical comparisons were performed between the RCS and LCS.Results:One hundred ninety-three patients were identifiedin the RCS cohort.The median age was 73 y(range:62–83 y).Most colectomies were performed for adenocarcinoma(90.2%),with right hemicolectomy being the most common type of procedure(49.3%).The median CRP levels on PODs 3 and 5 were 83.10 mg/L(IQR:49.80–124.12 mg/L)and 26.20 mg/L(IQR:17.70–80.00 mg/L),respectively.The reported CRP after LCS was heterogeneous,with mean POD 3 values ranging from 69 mg/L to 99.5 mg/L,and mean POD 4–5 values ranging from 62.4 mg/L to 72.85 mg/L.Conclusions:There were similar,if not lower,POD 3 and 5 CRP values,suggesting that RCS was probably non-inferior to LCS regarding postoperative tissue trauma.In particular,there appeared to be a quicker recovery of the inflammatory response with RCS.展开更多
Single-cell biomechanics and electrophysiology measuring tools have transformed biological research over the last few decades,which enabling a comprehensive and nuanced understanding of cellular behavior and function....Single-cell biomechanics and electrophysiology measuring tools have transformed biological research over the last few decades,which enabling a comprehensive and nuanced understanding of cellular behavior and function.Despite their high-quality information content,these single-cell measuring techniques suffer from laborious manual processing by highly skilled workers and extremely low throughput(tens of cells per day).Recently,numerous researchers have automated the measurement of cell mechanical and electrical signals through robotic localization and control processes.While these efforts have demonstrated promising progress,critical challenges persist,including human dependency,learning complexity,in-situ measurement,and multidimensional signal acquisition.To identify key limitations and highlight emerging opportunities for innovation,in this review,we comprehensively summarize the key steps of robotic technologies in single-cell biomechanics and electrophysiology.We also discussed the prospects and challenges of robotics and automation in biological research.By bridging gaps between engineering,biology,and data science,this work aims to stimulate interdisciplinary research and accelerate the translation of robotic single-cell technologies into practical applications in the life sciences and medical fields.展开更多
In this research,a comparative analysis was conducted on the performance and efficiency of the dual-anchor soft robot(DASR)and the extension-contraction soft robot(ECSR).These robots were constructed by imitating the ...In this research,a comparative analysis was conducted on the performance and efficiency of the dual-anchor soft robot(DASR)and the extension-contraction soft robot(ECSR).These robots were constructed by imitating the locomotion of razor clams.The penetration force for extension actuators and the anchorage force for expansion actuators in dry sand with distinct relative densities were tested by differentiating input air pressure and length-to-diameter ratios(λ).On the basis of the findings,a DASR and an ECSR were developed.DASR comprised two expansion actuators as the head and the tail segments at two ends,and one extension actuator as the middle segment.ECSR was composed of an extension actuator.A method based on the force equilibrium was introduced to ascertain and adjust the geometric parameters(length of each segment)of DASR.The burrowing-out performance and efficiency of DASR and ECSR in sands with distinct relative densities were explored.The results revealed that DASR exhibited high efficiency in dense sand in terms of lower time of burrowing-out,slip-to-advancement ratio,and cost of transport.ECSR might perform better in looser sand in terms of higher average burrowing-out velocity,higher advancement in each cycle,and lower energy consumption.However,it had larger slips than DASR.DASR could realize steady advancement and net displacement in each cycle and effectively decrease slips.These findings demonstrate the benefits and usability of the dual-anchor motion and offer new insights into the application of the dual-anchor mechanism in the burrowing of robots.展开更多
The capability of whole-body proprioception,e.g.,pose estimation,is important for the control and interaction of continuum robots.However,existing pose estimation methods are often simplified through geometric assumpt...The capability of whole-body proprioception,e.g.,pose estimation,is important for the control and interaction of continuum robots.However,existing pose estimation methods are often simplified through geometric assumptions,primarily due to constraints such as computational and sensor deployment costs.We propose an explicit posture estimation method through a neural network,and implement it using an embedded camera for vision-based proprioception.We design a continuous location encoding neural network(LENN)by encoding continuous locational information.The LENN can capture deformation from changes in internal texture observed by an integrated camera,and output pose information—both position and orientation—for any point along the robot backbone,rather than only discrete points.Compared with interpolation-based estimation using a reduced model,our method reduces single-point estimation error by 33.6%.Furthermore,a systematic evaluation of hardware configurations demonstrates that our prototype achieves sub-millimetre accuracy in shape estimation(0.383 mm)while maintaining real-time inference speeds below 12 ms per frame.By combining a learning-based approach with a simple mechanical design,our method leverages internal visual information to estimate the whole-body pose,providing an effective solution for accurate shape estimation in continuum robots.展开更多
This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obsta...This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.展开更多
Protective hardware is essential for mitigating damage caused by unavoidable falls in humanoid robots.Despite notable progress in fall protection hardware,the theoretical foundation for modeling and the feasibility of...Protective hardware is essential for mitigating damage caused by unavoidable falls in humanoid robots.Despite notable progress in fall protection hardware,the theoretical foundation for modeling and the feasibility of conducting full-scale fall experiments on robots or their surrogates remain somewhat limited.This paper proposes a method for optimizing the thickness of Expandable Polyethylene(EPE),which is used as back protection for the Chubao humanoid robot,based on small-scale impact test data to predict full-scale behavior.The optimal thickness is defined as a balance between compact design and protective effectiveness.An equivalent impact model characterized by four parameters:contact area S,mass m,fall height h,and cushioning material thickness d is introduced to describe impact conditions.The relationship between the peak impact acceleration ap and material thickness d,which forms the core of the method and gives rise to the name AP-D,is analyzed through their plotted curves.After introducing three characteristic parameters and two correction fac-tors,the relationship among the aforementioned variables is derived.Subsequently,both the optimal thickness do and its corresponding peak impact acceleration aop are predicted via nonlinear and linear regression models.Finally,the accuracy and effectiveness of the theoretically derived optimal thickness are validated on both a dummy and the actual robot.With the cushioning material applied,the peak chest acceleration is reduced to 41.57g for the dummy and 32.08g for the robot.展开更多
Industrial robot dynamics lay the foundation for high-precision and high-speed control, and accurate identification of dynamic parameters is essential for precise dynamic calculations. The choice of friction models is...Industrial robot dynamics lay the foundation for high-precision and high-speed control, and accurate identification of dynamic parameters is essential for precise dynamic calculations. The choice of friction models is a critical component in the identification of industrial robot dynamics. Traditional static friction models struggle to capture the hysteresis effects caused by robot joint elasticity and clearances, leading to large torque prediction errors when the joint velocity crosses zero. Due to the presence of hysteresis effects, the joint velocity crosses zero in the forward direction, and the reverse direction will have different friction patterns. Although the hysteresis effects can be modeled as an ordinary differential equation(ODE), it is difficult to determine the ODE structure that achieves both generalization and accuracy to describe the hysteresis effects of the friction model. To address this issue, we propose the neural hysteresis friction(NHF), which uses neural ODE to model the hysteresis effects in a data-driven manner, thereby mitigating the current inadequacies in the study of dynamic friction characteristics. The experiments on a real 6-axis industrial robot demonstrate that our proposed method can accurately model the friction dynamics during directional switching and outperform other modeling methods. Velocity tracking control experiments show that NHF can effectively reduce tracking errors when the velocity crosses zero.展开更多
Objective:Open retroperitoneal lymph node dissection(RPLND)is the gold-standard surgical approach for the management of metastatic testicular cancer,but robotic RPLND is becoming increasingly popular.There is limited ...Objective:Open retroperitoneal lymph node dissection(RPLND)is the gold-standard surgical approach for the management of metastatic testicular cancer,but robotic RPLND is becoming increasingly popular.There is limited research directly comparing open and robotic RPLND.The objective of this systematic review is to identify all the literature with direct comparisons between the open and robotic techniques for RPLND and to compare the two techniques.The primary outcome was peri-operative outcomes,and the secondary outcomes included oncological outcomes and patient demographics.Methods:This systematic review was prospectively registered and was conducted in accordance with the PRISMA statement.The PubMed,Embase and MEDLINE databases were searched for relevant publication from January 2006 to August 2024.Results:Eight studies,totaling 3995 patients,are included in this systematic review,with 3521 patients who underwent open RPLND and 474 who underwent robotic RPLND.For open RPLND,the mean operative duration,blood loss and length of stay were 267.8 min,475 mL and 7.3 d,respectively.For robotic RPLND,the mean operative duration,blood loss and length of stay were 334.5 min,94.6 mL and 3.7 d,respectively.Teratoma was the most common RPLND specimen pathology from both open and robotic surgeries.For open RPLND,the specimens have 13–23 nodes(26–32 mm),whereas the robotic RPLND specimens have 13–28 nodes(18–20 mm).Conclusion:This systematic review suggests that the benefitsof robotic RPLND may be associated with reduced blood loss,shorter hospitalisation and an overall lower risk of minor and major complications while maintaining oncological safety.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
基金Supported by National Natural Science Foundation of China(Grant No.51735009).
文摘The paper proposes a novel multi-legged robot with pitch adjustive units aiming at obstacle surmounting.With only 6 degrees of freedom,the robot with 16 mechanical legs walks steadily and surmounts the obstacles on the complex terrain.The leg unit with adjustive pitch provides a large workspace and empowers the legs to climb up obstacles in large sizes,which enhances the obstacle surmounting capability.The pitch adjustment in leg unit requires as few independent adjusting actuators as possible.Based on the kinematic analysis of the mechanical leg,the biped and quadruped leg units with adjustive pitch are analyzed and compared.The configuration of the robot is designed to obtain a compact structure and pragmatic performance.The uncertainty of the obstacle size and position in the surmounting process is taken into consideration and the parameters of the adjustments and the feasible strategies for obstacle surmounting are presented.Then the 3D virtual model and the robot prototype are built and the multi-body dynamic simulations and prototype experiments are carried out.The results from the simulations and the experiments show that the robot possesses good obstacle surmounting capabilities.
基金supported by the National Natural Science Foundation of China(Grant No.52375014)Guangdong Innovative and Entrepreneurial Research Team Program,China(Grant No.2019ZT08Z780)Dongguan Introduction Program of Leading Innovative and Entrepreneurial Talents,China(Grant No.20181220).
文摘Real-time slip detection and state estimation are crucial for locomotion control,facilitating posture adjustment and stability recovery of multi-legged robots moving on slippery terrain.However,existing proprioceptive methods rely on the fixed-contact assumption with fixed noise and suffer from low accuracy when multiple legs slip simultaneously.This paper proposes a novel proprioceptive approach for multi-legged robots moving in slippery scenarios to cope with slippage of multiple legs.In slip detection,the proprioceptive states of the robot are fed into a convolutional neural network to detect slip event(s)of the robot,enabling accurate identification of slipping legs even under simultaneous multi-leg slippage.For state estimation,an invariant extended Kalman filter is employed to fuse the motion information with the detected slip event(s)to obtain the robot state.By incorporating slip event(s)and foot velocity into the system motion equation of the filter,the proposed method better leverages leg odometry information and achieves more precise state estimation compared with existing methods.Simulations on a quadruped and a hexapod demonstrate the effectiveness and increased accuracy during multi-leg slippage.Experimental results for the quadruped robot show that the proposed approach achieves a 48% reduction in the root mean square error and a 47%reduction in the maximum error in velocity estimation under severe multi-leg slippage compared with the existing methods.
基金Sponsored by the National High Technology Research and Development Program of China(Grant No. 2006AA04Z245)the Program for Changjiang Scholars and Innovative Research Team in University(Grant No. IRT0423)
文摘In order to achieve omnidirectional locomotion on rough terrain with multi-legged biomimetic robot,a free gait generation approach is proposed based on local rules.The phase coordinates of each operation leg was established according to the motion task and a universal depiction of leg-end locomotion was implemented;the mathematical relation of gait pattern and walking velocity of multi-legged robot was put forward;combined polynomial curve was adopted to generate the leg-end trajectory,which was capable of accomplishing walking missions and accommodating to landform conditions;a distributed network of local rules for gait control was constructed based on a set of local rules operating between adjacent legs.In the simulation experiments,adaptive regulation of inter-leg phase sequence,omnidirectional locomotion and ground accommodation were realized.Moreover,statically stable free gait was obtained simultaneously,which provided multi-legged robot with the capability of walking on irregular terrain reliably and expeditiously.
文摘In this paper,we propose a new gait planning method for a multi-legged robot which has only 1 degree-of-freedom in each leg and has a passive body joint between two body segments.We firstly introduce the Finite State Machine(FSM)to the undulatory gait planning method of the 2n-legged robot.Then,the undulatory gait sequence for straight line motion is achieved by undulations motion.The idea that legged locomotion is achievable by less actuation of 2n-legged robot as well as the gait planning methods are verified finally by simulations and experiments.
基金supported by National Natural Science Foundation of China (Grant Nos. 50675079,50875246)Program for Innovative Research Team (in Science and Technology) in University of Henan Province,China
文摘Existing errors in the structure and kinematic parameters of multi-legged walking robots,the motion trajectory of robot will diverge from the ideal sports requirements in movement.Since the existing error compensation is usually used for control compensation of manipulator arm,the error compensation of multi-legged robots has seldom been explored.In order to reduce the kinematic error of robots,a motion error compensation method based on the feedforward for multi-legged mobile robots is proposed to improve motion precision of a mobile robot.The locus error of a robot body is measured,when robot moves along a given track.Error of driven joint variables is obtained by error calculation model in terms of the locus error of robot body.Error value is used to compensate driven joint variables and modify control model of robot,which can drive the robots following control model modified.The model of the relation between robot＇s locus errors and kinematic variables errors is set up to achieve the kinematic error compensation.On the basis of the inverse kinematics of a multi-legged walking robot,the relation between error of the motion trajectory and driven joint variables of robots is discussed.Moreover,the equation set is obtained,which expresses relation among error of driven joint variables,structure parameters and error of robot＇s locus.Take MiniQuad as an example,when the robot MiniQuad moves following beeline tread,motion error compensation is studied.The actual locus errors of the robot body are measured before and after compensation in the test.According to the test,variations of the actual coordinate value of the robot centroid in x-direction and z-direction are reduced more than one time.The kinematic errors of robot body are reduced effectively by the use of the motion error compensation method based on the feedforward.
基金Project(51221004)supported by the Science Fund for Creative Research Groups of National Natural Science Foundation of ChinaProject supported by the Program for Zhejiang Leading Team of S&T Innovation,China
文摘The semi-round rigid feet would cause position-posture deviation problem because the actual foothold position is hardly known due to the rolling effect of the semi-round rigid feet during the robot walking. The position-posture deviation problem may harm to the stability and the harmony of the robot, or even makes the robot tip over and fail to walk forward. Focused on the position-posture deviation problem of multi-legged walking robots with semi-round rigid feet, a new method of position-posture closed-loop control is proposed to solve the position-posture deviation problem caused by semi-round rigid feet, based on the inverse velocity kinematics of the multi-legged walking robots. The position-posture closed-loop control is divided into two parts: the position closed-loop control and the posture closed-loop control. Thus, the position-posture control for the robot which is a tight coupling and nonlinear system is decoupled. Co-simulations of position-posture open-loop control and position-posture closed-loop control by MATLAB and ADAMS are implemented, respectively. The co-simulation results verify that the position-posture closed-loop control performs well in solving the position-posture deviation problem caused by semi-round rigid feet.
文摘This paper presents a new kind of leg mechanism with which the wall climbing robot can easily perform the ground to wall transition by itself.To get its walking envelope and limit position,the forward/inverse kinematics and the statics of the mechanism are solved.All of these lay the foundation for ground to wall transition gait programing,mechanism parameter selection and optimization.
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
文摘The Chinese SME’s vision shows the bright side of humanoid robots:rather than replacing human workers,they are handling everyday mechanical tasks,leaving human staff to focus on improving service and emotional experiences.IN the Jinqiao Economic and Technological Development Zone in Pudong New Area,Shanghai,KEENON Robotics,a national-level“Little Giant”(innovative SME),is leading the transformation of the service robots industry.Amid the wave of embodied intelligence development,the humanoid service robots created by this company have become a focal point of the industry and businesses alike.
文摘Objective:Robotic colorectal surgery(RCS)provides a stable,magnifiedthree-dimensional visual field and enhanced ergonomics enabling precise dissection and tremor suppression.We postulate that this technique is associated with less tissue trauma and improved postoperative outcomes than laparoscopic colorectal surgery(LCS).This study aimed to explore the inflammatoryresponse following RCS by measuring postoperative C-reactive protein(CRP)levels and compare them with LCS data reported in the literature.Methods:This single centre retrospective study included consecutive elective robotic colon and rectum resections via the da Vinci®Xi platform for benign and malignant colorectal tumours,performed by a single surgeon between January 2017 and December 2023 at the Sydney Adventist Hospital,Sydney.CRP values were measured on post-operative days(PODs)3 and 5.A narrative review of the literature was performed via EMBASE,MEDLINE via PubMed and Google Scholar from inception to December 2024 for comparative CRP values following LCS.Descriptive statistical comparisons were performed between the RCS and LCS.Results:One hundred ninety-three patients were identifiedin the RCS cohort.The median age was 73 y(range:62–83 y).Most colectomies were performed for adenocarcinoma(90.2%),with right hemicolectomy being the most common type of procedure(49.3%).The median CRP levels on PODs 3 and 5 were 83.10 mg/L(IQR:49.80–124.12 mg/L)and 26.20 mg/L(IQR:17.70–80.00 mg/L),respectively.The reported CRP after LCS was heterogeneous,with mean POD 3 values ranging from 69 mg/L to 99.5 mg/L,and mean POD 4–5 values ranging from 62.4 mg/L to 72.85 mg/L.Conclusions:There were similar,if not lower,POD 3 and 5 CRP values,suggesting that RCS was probably non-inferior to LCS regarding postoperative tissue trauma.In particular,there appeared to be a quicker recovery of the inflammatory response with RCS.
基金the National Natural Science Foundation of China[62525301,62127811,62433019]the New Cornerstone Science Foundation through the XPLORER PRIZEthe financial support by the China Postdoctoral Science Foundation[GZB20240797].
文摘Single-cell biomechanics and electrophysiology measuring tools have transformed biological research over the last few decades,which enabling a comprehensive and nuanced understanding of cellular behavior and function.Despite their high-quality information content,these single-cell measuring techniques suffer from laborious manual processing by highly skilled workers and extremely low throughput(tens of cells per day).Recently,numerous researchers have automated the measurement of cell mechanical and electrical signals through robotic localization and control processes.While these efforts have demonstrated promising progress,critical challenges persist,including human dependency,learning complexity,in-situ measurement,and multidimensional signal acquisition.To identify key limitations and highlight emerging opportunities for innovation,in this review,we comprehensively summarize the key steps of robotic technologies in single-cell biomechanics and electrophysiology.We also discussed the prospects and challenges of robotics and automation in biological research.By bridging gaps between engineering,biology,and data science,this work aims to stimulate interdisciplinary research and accelerate the translation of robotic single-cell technologies into practical applications in the life sciences and medical fields.
基金financially supported by the Natural Science Foundation of Jiangsu Province,China(No.BK 20221502)the National Natural Science Foundation of China(No.42477147)。
文摘In this research,a comparative analysis was conducted on the performance and efficiency of the dual-anchor soft robot(DASR)and the extension-contraction soft robot(ECSR).These robots were constructed by imitating the locomotion of razor clams.The penetration force for extension actuators and the anchorage force for expansion actuators in dry sand with distinct relative densities were tested by differentiating input air pressure and length-to-diameter ratios(λ).On the basis of the findings,a DASR and an ECSR were developed.DASR comprised two expansion actuators as the head and the tail segments at two ends,and one extension actuator as the middle segment.ECSR was composed of an extension actuator.A method based on the force equilibrium was introduced to ascertain and adjust the geometric parameters(length of each segment)of DASR.The burrowing-out performance and efficiency of DASR and ECSR in sands with distinct relative densities were explored.The results revealed that DASR exhibited high efficiency in dense sand in terms of lower time of burrowing-out,slip-to-advancement ratio,and cost of transport.ECSR might perform better in looser sand in terms of higher average burrowing-out velocity,higher advancement in each cycle,and lower energy consumption.However,it had larger slips than DASR.DASR could realize steady advancement and net displacement in each cycle and effectively decrease slips.These findings demonstrate the benefits and usability of the dual-anchor motion and offer new insights into the application of the dual-anchor mechanism in the burrowing of robots.
基金supported by the National Natural Science Foundation of China(Grant Nos.52188102,52505008)the National Key Research and Development Program of China(Grant No.2024YFB4707902)。
文摘The capability of whole-body proprioception,e.g.,pose estimation,is important for the control and interaction of continuum robots.However,existing pose estimation methods are often simplified through geometric assumptions,primarily due to constraints such as computational and sensor deployment costs.We propose an explicit posture estimation method through a neural network,and implement it using an embedded camera for vision-based proprioception.We design a continuous location encoding neural network(LENN)by encoding continuous locational information.The LENN can capture deformation from changes in internal texture observed by an integrated camera,and output pose information—both position and orientation—for any point along the robot backbone,rather than only discrete points.Compared with interpolation-based estimation using a reduced model,our method reduces single-point estimation error by 33.6%.Furthermore,a systematic evaluation of hardware configurations demonstrates that our prototype achieves sub-millimetre accuracy in shape estimation(0.383 mm)while maintaining real-time inference speeds below 12 ms per frame.By combining a learning-based approach with a simple mechanical design,our method leverages internal visual information to estimate the whole-body pose,providing an effective solution for accurate shape estimation in continuum robots.
基金supported by the National Science and Technology Council of under Grant NSTC 114-2221-E-130-007.
文摘This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.
基金Natural Science Foundation of Beijing Municipality under Grant L243004the National Natural Science Foundation of China under Grant 62403060.
文摘Protective hardware is essential for mitigating damage caused by unavoidable falls in humanoid robots.Despite notable progress in fall protection hardware,the theoretical foundation for modeling and the feasibility of conducting full-scale fall experiments on robots or their surrogates remain somewhat limited.This paper proposes a method for optimizing the thickness of Expandable Polyethylene(EPE),which is used as back protection for the Chubao humanoid robot,based on small-scale impact test data to predict full-scale behavior.The optimal thickness is defined as a balance between compact design and protective effectiveness.An equivalent impact model characterized by four parameters:contact area S,mass m,fall height h,and cushioning material thickness d is introduced to describe impact conditions.The relationship between the peak impact acceleration ap and material thickness d,which forms the core of the method and gives rise to the name AP-D,is analyzed through their plotted curves.After introducing three characteristic parameters and two correction fac-tors,the relationship among the aforementioned variables is derived.Subsequently,both the optimal thickness do and its corresponding peak impact acceleration aop are predicted via nonlinear and linear regression models.Finally,the accuracy and effectiveness of the theoretically derived optimal thickness are validated on both a dummy and the actual robot.With the cushioning material applied,the peak chest acceleration is reduced to 41.57g for the dummy and 32.08g for the robot.
基金supported by the National Natural Science Foundation of China (Grant No.52188102)。
文摘Industrial robot dynamics lay the foundation for high-precision and high-speed control, and accurate identification of dynamic parameters is essential for precise dynamic calculations. The choice of friction models is a critical component in the identification of industrial robot dynamics. Traditional static friction models struggle to capture the hysteresis effects caused by robot joint elasticity and clearances, leading to large torque prediction errors when the joint velocity crosses zero. Due to the presence of hysteresis effects, the joint velocity crosses zero in the forward direction, and the reverse direction will have different friction patterns. Although the hysteresis effects can be modeled as an ordinary differential equation(ODE), it is difficult to determine the ODE structure that achieves both generalization and accuracy to describe the hysteresis effects of the friction model. To address this issue, we propose the neural hysteresis friction(NHF), which uses neural ODE to model the hysteresis effects in a data-driven manner, thereby mitigating the current inadequacies in the study of dynamic friction characteristics. The experiments on a real 6-axis industrial robot demonstrate that our proposed method can accurately model the friction dynamics during directional switching and outperform other modeling methods. Velocity tracking control experiments show that NHF can effectively reduce tracking errors when the velocity crosses zero.
文摘Objective:Open retroperitoneal lymph node dissection(RPLND)is the gold-standard surgical approach for the management of metastatic testicular cancer,but robotic RPLND is becoming increasingly popular.There is limited research directly comparing open and robotic RPLND.The objective of this systematic review is to identify all the literature with direct comparisons between the open and robotic techniques for RPLND and to compare the two techniques.The primary outcome was peri-operative outcomes,and the secondary outcomes included oncological outcomes and patient demographics.Methods:This systematic review was prospectively registered and was conducted in accordance with the PRISMA statement.The PubMed,Embase and MEDLINE databases were searched for relevant publication from January 2006 to August 2024.Results:Eight studies,totaling 3995 patients,are included in this systematic review,with 3521 patients who underwent open RPLND and 474 who underwent robotic RPLND.For open RPLND,the mean operative duration,blood loss and length of stay were 267.8 min,475 mL and 7.3 d,respectively.For robotic RPLND,the mean operative duration,blood loss and length of stay were 334.5 min,94.6 mL and 3.7 d,respectively.Teratoma was the most common RPLND specimen pathology from both open and robotic surgeries.For open RPLND,the specimens have 13–23 nodes(26–32 mm),whereas the robotic RPLND specimens have 13–28 nodes(18–20 mm).Conclusion:This systematic review suggests that the benefitsof robotic RPLND may be associated with reduced blood loss,shorter hospitalisation and an overall lower risk of minor and major complications while maintaining oncological safety.
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.
