Local precise drug delivery is conducive to improving therapeutic efficacy and minimizing off-target toxicity.Current local delivery approaches are focused mostly on superficial or postoperative tumor lesions,due to t...Local precise drug delivery is conducive to improving therapeutic efficacy and minimizing off-target toxicity.Current local delivery approaches are focused mostly on superficial or postoperative tumor lesions,due to the challenges posed by the inaccessibility of deep-seated tumors.Herein,we report a magnetic continuum soft robot capable of non-invasive and site-specific delivery of prodrug nanoassemblies-loaded hydrogel.The nanoassemblies are co-assembled from redox-responsive docetaxel prodrug and oxaliplatin prodrug,and subsequently embedded into a hydrogel matrix.The hydrogel precursor and crosslinker are synchronously delivered using the soft robot under magnetic guidance and in situ crosslinked at the gastric cancer lesions,forming a drug depot for sustained release and long-lasting treatment.As the hydrogel gradually degrades,the nanoassemblies are internalized by tumor cells.The redox response ability enables them to be selectively activatedwithin tumor cells to trigger the release of docetaxel and oxaliplatin,exerting a synergistic anti-tumor effect.We find that the combination effectively induces immunogenic cell death of gastric tumor,enhancing antitumor immune responses.This strategy offers an intelligent and controllable integration platform for precise drug delivery and combined chemo-immunotherapy.展开更多
Parallel Kinematic Machines(PKMs)are being widely used for precise applications to achieve complex motions and variable poses for the end effector tool.PKMs are found in medical,assembly and manufacturing industries w...Parallel Kinematic Machines(PKMs)are being widely used for precise applications to achieve complex motions and variable poses for the end effector tool.PKMs are found in medical,assembly and manufacturing industries where accuracy is necessary.It is often desired to have a compact and simple architecture for the robotic mechanism.In this paper,the kinematic and dynamic analysis of a novel 3-PRUS(P:prismatic joint,R:revolute joint,U:universal joint,S:spherical joint)parallel manipulator with a mobile platform having 6 Degree of Freedom(Do F)is explained.The kinematic equations for the proposed spatial parallel mechanism are formulated using the Modified Denavit-Hartenberg(DH)technique considering both active and passive joints.The kinematic equations are used to derive the Jacobian matrix of the mechanism to identify the singular points within the workspace.A Jacobian based sti ness analysis is done to understand the variations in sti ness for different poses of the mobile platform and further,it is used to decide trajectories for the end effector within the singularity free region.The analytical model of the robot dynamics is presented using the Euler-Lagrangian approach with Lagrangian multipliers to include the system constraints.The gravity and inertial forces of all links are considered in the mathematical model.The analytical results of the dynamic model are compared with ADAMS simulation results for a pre-defined trajectory of the end effector.展开更多
The concrete aging problem has gained more attention in recent years as more bridges and tunnels in the United States lack proper maintenance. Though the Federal Highway Administration requires these public concrete s...The concrete aging problem has gained more attention in recent years as more bridges and tunnels in the United States lack proper maintenance. Though the Federal Highway Administration requires these public concrete structures to be inspected regularly, on-site manual inspection by human operators is time-consuming and labor-intensive. Conventional inspection approaches for concrete inspection, using RGB imagebased thresholding methods, are not able to determine metric information as well as accurate location information for assessed defects for conditions. To address this challenge, we propose a deep neural network(DNN) based concrete inspection system using a quadrotor flying robot(referred to as City Flyer) mounted with an RGB-D camera. The inspection system introduces several novel modules. Firstly, a visual-inertial fusion approach is introduced to perform camera and robot positioning and structure 3 D metric reconstruction. The reconstructed map is used to retrieve the location and metric information of the defects.Secondly, we introduce a DNN model, namely Ada Net, to detect concrete spalling and cracking, with the capability of maintaining robustness under various distances between the camera and concrete surface. In order to train the model, we craft a new dataset, i.e., the concrete structure spalling and cracking(CSSC)dataset, which is released publicly to the research community.Finally, we introduce a 3 D semantic mapping method using the annotated framework to reconstruct the concrete structure for visualization. We performed comparative studies and demonstrated that our Ada Net can achieve 8.41% higher detection accuracy than Res Nets and VGGs. Moreover, we conducted five field tests, of which three are manual hand-held tests and two are drone-based field tests. These results indicate that our system is capable of performing metric field inspection,and can serve as an effective tool for civil engineers.展开更多
Impedance control is a well-established technique to control interaction forces in robotics.However,real implementations of impedance control with an inner loop may suffer from several limitations.In particular,the vi...Impedance control is a well-established technique to control interaction forces in robotics.However,real implementations of impedance control with an inner loop may suffer from several limitations.In particular,the viable range of stable stiffness and damping values can be strongly affected by the bandwidth of the inner control loops(e.g.,a torque loop)as well as by the filtering and sampling frequency.This paper provides an extensive analysis on how these aspects influence the stability region of impedance parameters as well as the passivity of the system.This will be supported by both simulations and experimental data.Moreover,a methodology for designing joint impedance controllers based on an inner torque loop and a positive velocity feedback loop will be presented.The goal of the velocity feedback is to increase(given the constraints to preserve stability)the bandwidth of the torque loop without the need of a complex controller.展开更多
Current research on robotic dexterous hands mainly focuses on designing new finger and palm structures,as well as developing smarter control algorithms.Although the dimensional synthesis of dexterous hands with tradit...Current research on robotic dexterous hands mainly focuses on designing new finger and palm structures,as well as developing smarter control algorithms.Although the dimensional synthesis of dexterous hands with traditional rigid palms has been carried out,research on the dimensional synthesis of dexterous hands with metamorphic palms remains insufficient.This study investigated the dimensional synthesis of a palm of a novel metamorphic multi-fingered hand,and explored the geometric design for maximizing the precision manipulation workspace.Different indexes were used to value the workspace of the metamorphic hand,and the best proportions between the five links of the palm to obtain the optimal workspace of the metamorphic hand were explored.Based on the fixed total length of the palm member,four nondimensional design parameters that determine the size of the palm were introduced;through the discretization method,the influence of the four design parameters on the workspace of the metamorphic hand with full-actuated fingers and under-actuated fingers was analyzed.Based on the analysis of the metamorphic multi-fingered hand,the symmetrical structure of the palm was designed,resulting in the largest workspace of the multi-fingered hand,and proved that the metamorphic palm has a massive upgrade for the workspace of underactuated fingers.This research contributed to the dimensional synthesis of metamorphic dexterous hands,with practical significance for the design and optimization of novel metamorphic hands.展开更多
This work proposes a novel proportional-derivative(PD)-type state-dependent Riccati equation(SDRE)approach with iterative learning control(ILC)augmentation.On the one hand,the PD-type control gains could adopt many us...This work proposes a novel proportional-derivative(PD)-type state-dependent Riccati equation(SDRE)approach with iterative learning control(ILC)augmentation.On the one hand,the PD-type control gains could adopt many useful available criteria and tools of conventional PD controllers.On the other hand,the SDRE adds nonlinear and optimality characteristics to the controller,i.e.,increasing the stability margins.These advantages with the ILC correction part deliver a precise control law with the capability of error reduction by learning.The SDRE provides a symmetric-positive-definite distributed nonlinear suboptimal gain K(x)for the control input law u=–R–1(x)BT(x)K(x)x.The sub-blocks of the overall gain R–1(x)BT(x)K(x),are not necessarily symmetric positive definite.A new design is proposed to transform the optimal gain into two symmetric-positive-definite gains like PD-type controllers as u=–KSP(x)e–KSD(x)?.The new form allows us to analytically prove the stability of the proposed learning-based controller for mechanical systems;and presents guaranteed uniform boundedness in finite-time between learning loops.The symmetric PD-type controller is also developed for the state-dependent differential Riccati equation(SDDRE)to manipulate the final time.The SDDRE expresses a differential equation with a final boundary condition,which imposes a constraint on time that could be used for finitetime control.So,the availability of PD-type finite-time control is an asset for enhancing the conventional classical linear controllers with this tool.The learning rules benefit from the gradient descent method for both regulation and tracking cases.One of the advantages of this approach is a guaranteed-stability even from the first loop of learning.A mechanical manipulator,as an illustrative example,was simulated for both regulation and tracking problems.Successful experimental validation was done to show the capability of the system in practice by the implementation of the proposed method on a variable-pitch rotor benchmark.展开更多
Soft climbing/crawling robots have been attracting increasing attention in the soft robotics community,and many prototypes with basic locomotion have been implemented.Most existing soft robots achieve locomotion by pl...Soft climbing/crawling robots have been attracting increasing attention in the soft robotics community,and many prototypes with basic locomotion have been implemented.Most existing soft robots achieve locomotion by planar bending deformation and lack sufficient mobility.Enhancing the mobility of soft climbing/crawling robots is still an open and challenging issue.To this end,we present a novel pneumatic leech-like soft robot,Leechbot,with both bending and stretching deformation for locomotion.With a morphological structure,the robot consists of a three-chambered actuator in the middle for the main motion,two chamber-net actuators that act as ankles,and two suckers at the ends for anchoring on surfaces.The peristaltic motion for locomotion is implemented by body stretching,and direction changing is achieved by body bending.Due to the novel design and two deformation modes,the robot can make turns and transit between different surfaces;the robot,hence,has excellent mobility.The development of the robot prototype is presented in detail in this paper.To control its motion,tests were carried out to determine the relationship between step length and air pressure as well as the relationship between motion speed and periodic delay time.A kinematic model was established,and the kinematic mobility and surface transitionability were analyzed.Gait planning based on the inflating sequence of the actuating chambers is presented for straight crawling,turn making,and transiting between surfaces and was verified by a series of experiments with the prototype.The results show that a high mobility in soft climbing/crawling robots can be achieved by a novel design and by proper gait planning.展开更多
Flexible strain sensors play an important role in electronic skins,wearable medical devices,and advanced robots.Herein,a highly sensitive and fast response optical strain sensor with two evanescently coupled optical m...Flexible strain sensors play an important role in electronic skins,wearable medical devices,and advanced robots.Herein,a highly sensitive and fast response optical strain sensor with two evanescently coupled optical micro/nanofibers(MNFs)embedded in a polydimethylsiloxane(PDMS)film is proposed.The strain sensor exhibits a gauge factor as high as 64.5 for strain≤0.5%and a strain resolution of 0.0012%which corresponds to elongation of 120 nm on a 1 cm long device.As a proof-of-concept,highly sensitive fingertip pulse measurement is realized.The properties of fast temporal frequency response up to 30 kHz and a pressure sensitivity of 102 kPa^(−1) enable the sensor for sound detection.Such versatile sensor could be of great use in physiological signal monitoring,voice recognition and micro-displacement detection.展开更多
Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations dur...Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system（ANFIS）has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace.An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.展开更多
One of the primary difficulties in using powered parafoil(PPF) systems is the lack of effective trajectory tracking controllers since the trajectory tracking control is the essential operation for PPF to accomplish au...One of the primary difficulties in using powered parafoil(PPF) systems is the lack of effective trajectory tracking controllers since the trajectory tracking control is the essential operation for PPF to accomplish autonomous tasks. The characteristic model(CM) based all-coefficient adaptive control(ACAC) designed for PPF systems in horizontal and vertical trajectory control is proposed. The method is easy to use and convenient to adjust and test. Just a few parameters are adapted during the control process. In application, vertical and horizontal CMs are designed and ACAC controllers are constructed to control vertical altitude and horizontal trajectory of PPF based on the proposed CMs, respectively. Result analysis of different simulations shows that the applied ACAC control method is effective for trajectory tracking of the PPF systems and the approach guarantees the transient performance of the PPF systems with better disturbance rejection ability.展开更多
Using Complete Coverage Path Planning （CCPP）, a cleaning robot could visit every accessible area in the workspace. The dynamic environment requires the higher computation of the CCPP algorithm because the path needs...Using Complete Coverage Path Planning （CCPP）, a cleaning robot could visit every accessible area in the workspace. The dynamic environment requires the higher computation of the CCPP algorithm because the path needs to be replanned when the path might become invalid. In previous CCPP methods, when the neighbours of the current position are obstacles or have been visited, it is challenging for the robot to escape from the deadlocks with the least extra time cost. In this study, a novel CCPP algorithm is proposed to deal with deadlock problems in a dynamic environment. A priority template inspired by the short memory model could reduce the number of deadlocks by giving the priority of directions. Simultaneously, a global backtracking mechanism guides the robot to move to the next unvisited area quickly, taking the use of the explored global environmental information. What＇s more, the authors extend their CCPP algorithm to a multi-robot system with a market-based bidding process which could deploy the coverage time. Experiments of apartment-like scenes show that the authors＇ proposed algorithm can guarantee an efficient collision-free coverage in dynamic environments. The proposed method performs better than related approaches on coverage rate and overlap length.展开更多
For BCI systems,it is important to have an accurate and less complex architecture to control a device with enhanced accuracy.In this paper,a novel methodology for more accurate detection of the hemodynamic response ha...For BCI systems,it is important to have an accurate and less complex architecture to control a device with enhanced accuracy.In this paper,a novel methodology for more accurate detection of the hemodynamic response has been developed using a multimodal brain-computer interface(BCI).An integrated classifier has been developed for achieving better classification accuracy using two modalities.An integrated EEG-fNIRS-based vector-phase analysis(VPA)has been conducted.An open-source dataset collected at the TechnischeUniversit鋞Berlin,including simultaneous electroencephalography(EEG)and functional near-infrared spectroscopy(fNIRS)signals of 26 healthy participants during n-back tests,has been used for this research.Instrumental and physiological noise removal has been done using preprocessing techniques followed by individually detecting activity in both modalities.With resting state threshold circle,VPA has been used to detect a hemodynamic response in fNIRS signals,whereas phase plots for EEG signals have been constructed using Hilbert Transform to detect the activity in each trial.Multiple threshold circles are drawn in the vector plane,where each circle is drawn after task completion in each trial of EEG signal.Finally,both processes are integrated into one vector-phase plot to get combined detection of hemodynamic response for activity.Results of this study illustrate that the combined EEG-fNIRS VPA yields considerably higher average classification accuracy,that is 91.35%,as compared to other classifiers such as support vector machine(SVM),convolutional neural networks(CNN),deep neural networks(DNN)and VPA(with dual-threshold circles)with classification accuracies 82%,89%,87%and 86%respectively.Outcomes of this research demonstrate that improved classification performance can be feasibly achieved using multimodal VPA for EEG-fNIRS hybrid data.展开更多
In this study, the authors present the role playing learning scheme for a mobile robot to navigate socially with its human companion in populated environments. Neural networks （NNs） are constructed to parameterise a...In this study, the authors present the role playing learning scheme for a mobile robot to navigate socially with its human companion in populated environments. Neural networks （NNs） are constructed to parameterise a stochastic policy that directly maps sensory data collected by the robot to its velocity outputs, while respecting a set of social norms. An efficient simulative learning environment is built with maps and pedestrians trajectories collected from a number of real-world crowd data sets. In each learning iteration, a robot equipped with the NN policy is created virtually in the learning environment to play itself as a companied pedestrian and navigate towards a goal in a socially concomitant manner. Thus, this process is called role playing learning, which is formulated under a reinforcement learning framework. The NN policy is optimised end-to-end using trust region policy optimisation, with consideration of the imperfectness of robot＇s sensor measurements. Simulative and experimental results are provided to demonstrate the efficacy and superiority of the proposed method.展开更多
This paper deals with the problem of robust output synchronization for heterogeneous multi-agent systems.First,a new synchronization approach is presented to synchronize the outputs of heterogeneous agents.Based on no...This paper deals with the problem of robust output synchronization for heterogeneous multi-agent systems.First,a new synchronization approach is presented to synchronize the outputs of heterogeneous agents.Based on noninteracting control techniques,a method is derived for homogenizing the input-output behavior of every agent.Hence,applying the same reference input signal to every agent leads to synchronization.Furthermore,a strategy for increasing the robustness of the synchronization process against exogenous disturbances is presented,which leads to a structurally constrained optimization problem.However,by a convenient reformulation of the problem,well established tools from robust control theory can be used.Moreover,it is shown that this procedure allows to separate the robustness issue from the synchronization task.The effectiveness of the approach is illustrated by a robust output synchronization example for a heterogeneous aircraft fleet.展开更多
It is common for robotic fish to generate thrust using reactive force generated by the tail’s physical motion, which interacts with the surrounding fluid. The coupling effect of the body strongly correlates with this...It is common for robotic fish to generate thrust using reactive force generated by the tail’s physical motion, which interacts with the surrounding fluid. The coupling effect of the body strongly correlates with this thrust. However, hydrodynamics cannot be wholly modeled in analytical form. Therefore, data-assisted modeling is necessary for robotic fish. This work presents the first method of its kind using Genetic Algorithm (GA)-based optimization methods for data-assistive modeling for robotic fish applications. To begin, experimental data are collected in real time with the robotic fish that has been designed and fabricated using 3D printing. Then, the model’s influential parameters are estimated using an optimization problem. Further, a model-based deep reinforcement learning (DRL) controller is proposed to track the desired speed through extensive simulation work. In addition to a deep deterministic policy gradient (DDPG), a twin delayed DDPG (TD3) is employed in the training of the RL agent. Unfortunately, due to its local optimization problem, the RL-DDPG controller failed to perform well during training. In contrast, the RL-TD3 controller effectively learns the control policies and overcomes the local optima problem. As a final step, controller performance is evaluated under different disturbance conditions. In contrast to DDPG and GA-tuned proportional-integral controllers, the proposed model with RL-TD3 controller significantly improves the performance.展开更多
This paper considers the torque control problem for robots with flexible joints driven by electrical actuators. It is shown that the achievable closed-loop tracking bandwidth using PI torque controllers may be limited...This paper considers the torque control problem for robots with flexible joints driven by electrical actuators. It is shown that the achievable closed-loop tracking bandwidth using PI torque controllers may be limited due to transmission zeros introduced by the load dynamics. This limitation is overcome by using positive feedback from the load motion in unison with PI torque controllers. The positive feedback is given in terms of load velocity, acceleration and jerk. Stability conditions for designing decentralized PI torque controllers are derived in terms of Routh-Hurwitz criteria. Disturbance rejection properties of the closed system are characterized and an analysis is carried out investigating the use of approximate positive feedback by omitting acceleration and/or jerk signals. The results of this paper are illustrated for a two DoF （degrees of freedom） system. Experimental results for a one DoF system are also included.展开更多
This paper introduces a 3-dof hybrid robotic manipulator which is constructed by combining a parallel mechanism and a pantograph to increase stiffness as well as workspace. And by analyzing its kinematics and dynamics...This paper introduces a 3-dof hybrid robotic manipulator which is constructed by combining a parallel mechanism and a pantograph to increase stiffness as well as workspace. And by analyzing its kinematics and dynamics with Lagrange’s method, the dynamic model is obtained which is essential for feed-forward control of the manipulator. An explicit solution is given out. Finally, a simulation test is carried out on computers.展开更多
Synthetic micromotor has gained substantial attention in biomedicine and environmental remediation.Metal-based degradable micromotor composed of magnesium(Mg),zinc(Zn),and iron(Fe)have promise due to their nontoxic fu...Synthetic micromotor has gained substantial attention in biomedicine and environmental remediation.Metal-based degradable micromotor composed of magnesium(Mg),zinc(Zn),and iron(Fe)have promise due to their nontoxic fuel-free propulsion,favorable biocompatibility,and safe excretion of degradation products Recent advances in degradable metallic micromotor have shown their fast movement in complex biological media,efficient cargo delivery and favorable biocompatibility.A noteworthy number of degradable metal-based micromotors employ bubble propulsion,utilizing water as fuel to generate hydrogen bubbles.This novel feature has projected degradable metallic micromotors for active in vivo drug delivery applications.In addition,understanding the degradation mechanism of these micromotors is also a key parameter for their design and performance.Its propulsion efficiency and life span govern the overall performance of a degradable metallic micromotor.Here we review the design and recent advancements of metallic degradable micromotors.Furthermore,we describe the controlled degradation,efficient in vivo drug delivery,and built-in acid neutralization capabilities of degradable micromotors with versatile biomedical applications.Moreover,we discuss micromotors’efficacy in detecting and destroying environmental pollutants.Finally,we address the limitations and future research directions of degradable metallic micromotors.展开更多
Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging environments.In aut...Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging environments.In autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of CNNs.In this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best results.Literature has proven the superiority of metaheuristic algorithms over the manual-tuning of CNNs.However,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle prediction.Thus,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction problem.To validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear unit.As per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower ones.Results show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning approach.Infield testing was also performed using the model trained with the optimal architecture,which we developed using our approach.展开更多
基金supported by National Natural Science Foundation of China(No.82161138029)Liaoning Revitalization Talents Program(No.XLYC2402040)the Project of China-Japan Joint International Laboratory of Advanced Drug Delivery System Research and Translation of Liaoning Province(No.2024JH2/102100007).
文摘Local precise drug delivery is conducive to improving therapeutic efficacy and minimizing off-target toxicity.Current local delivery approaches are focused mostly on superficial or postoperative tumor lesions,due to the challenges posed by the inaccessibility of deep-seated tumors.Herein,we report a magnetic continuum soft robot capable of non-invasive and site-specific delivery of prodrug nanoassemblies-loaded hydrogel.The nanoassemblies are co-assembled from redox-responsive docetaxel prodrug and oxaliplatin prodrug,and subsequently embedded into a hydrogel matrix.The hydrogel precursor and crosslinker are synchronously delivered using the soft robot under magnetic guidance and in situ crosslinked at the gastric cancer lesions,forming a drug depot for sustained release and long-lasting treatment.As the hydrogel gradually degrades,the nanoassemblies are internalized by tumor cells.The redox response ability enables them to be selectively activatedwithin tumor cells to trigger the release of docetaxel and oxaliplatin,exerting a synergistic anti-tumor effect.We find that the combination effectively induces immunogenic cell death of gastric tumor,enhancing antitumor immune responses.This strategy offers an intelligent and controllable integration platform for precise drug delivery and combined chemo-immunotherapy.
文摘Parallel Kinematic Machines(PKMs)are being widely used for precise applications to achieve complex motions and variable poses for the end effector tool.PKMs are found in medical,assembly and manufacturing industries where accuracy is necessary.It is often desired to have a compact and simple architecture for the robotic mechanism.In this paper,the kinematic and dynamic analysis of a novel 3-PRUS(P:prismatic joint,R:revolute joint,U:universal joint,S:spherical joint)parallel manipulator with a mobile platform having 6 Degree of Freedom(Do F)is explained.The kinematic equations for the proposed spatial parallel mechanism are formulated using the Modified Denavit-Hartenberg(DH)technique considering both active and passive joints.The kinematic equations are used to derive the Jacobian matrix of the mechanism to identify the singular points within the workspace.A Jacobian based sti ness analysis is done to understand the variations in sti ness for different poses of the mobile platform and further,it is used to decide trajectories for the end effector within the singularity free region.The analytical model of the robot dynamics is presented using the Euler-Lagrangian approach with Lagrangian multipliers to include the system constraints.The gravity and inertial forces of all links are considered in the mathematical model.The analytical results of the dynamic model are compared with ADAMS simulation results for a pre-defined trajectory of the end effector.
基金supported in part by the U.S.National Science Foundation(IIP-1915721)the U.S.Department of TransportationOffice of the Assistant Secretary for Research and Technology(USDOTOST-R)(69A3551747126)through INSPIRE University Transportation Center(http//inspire-utc.mst.edu)at Missouri University of Science and Technology。
文摘The concrete aging problem has gained more attention in recent years as more bridges and tunnels in the United States lack proper maintenance. Though the Federal Highway Administration requires these public concrete structures to be inspected regularly, on-site manual inspection by human operators is time-consuming and labor-intensive. Conventional inspection approaches for concrete inspection, using RGB imagebased thresholding methods, are not able to determine metric information as well as accurate location information for assessed defects for conditions. To address this challenge, we propose a deep neural network(DNN) based concrete inspection system using a quadrotor flying robot(referred to as City Flyer) mounted with an RGB-D camera. The inspection system introduces several novel modules. Firstly, a visual-inertial fusion approach is introduced to perform camera and robot positioning and structure 3 D metric reconstruction. The reconstructed map is used to retrieve the location and metric information of the defects.Secondly, we introduce a DNN model, namely Ada Net, to detect concrete spalling and cracking, with the capability of maintaining robustness under various distances between the camera and concrete surface. In order to train the model, we craft a new dataset, i.e., the concrete structure spalling and cracking(CSSC)dataset, which is released publicly to the research community.Finally, we introduce a 3 D semantic mapping method using the annotated framework to reconstruct the concrete structure for visualization. We performed comparative studies and demonstrated that our Ada Net can achieve 8.41% higher detection accuracy than Res Nets and VGGs. Moreover, we conducted five field tests, of which three are manual hand-held tests and two are drone-based field tests. These results indicate that our system is capable of performing metric field inspection,and can serve as an effective tool for civil engineers.
基金supported by the Istituto Italiano di Tecnologia,and Dr.J.Buchli was supported by a Swiss National Science Foundation professorship.
文摘Impedance control is a well-established technique to control interaction forces in robotics.However,real implementations of impedance control with an inner loop may suffer from several limitations.In particular,the viable range of stable stiffness and damping values can be strongly affected by the bandwidth of the inner control loops(e.g.,a torque loop)as well as by the filtering and sampling frequency.This paper provides an extensive analysis on how these aspects influence the stability region of impedance parameters as well as the passivity of the system.This will be supported by both simulations and experimental data.Moreover,a methodology for designing joint impedance controllers based on an inner torque loop and a positive velocity feedback loop will be presented.The goal of the velocity feedback is to increase(given the constraints to preserve stability)the bandwidth of the torque loop without the need of a complex controller.
基金Supported by National Natural Science Foundation of China(Grant No.51535008).
文摘Current research on robotic dexterous hands mainly focuses on designing new finger and palm structures,as well as developing smarter control algorithms.Although the dimensional synthesis of dexterous hands with traditional rigid palms has been carried out,research on the dimensional synthesis of dexterous hands with metamorphic palms remains insufficient.This study investigated the dimensional synthesis of a palm of a novel metamorphic multi-fingered hand,and explored the geometric design for maximizing the precision manipulation workspace.Different indexes were used to value the workspace of the metamorphic hand,and the best proportions between the five links of the palm to obtain the optimal workspace of the metamorphic hand were explored.Based on the fixed total length of the palm member,four nondimensional design parameters that determine the size of the palm were introduced;through the discretization method,the influence of the four design parameters on the workspace of the metamorphic hand with full-actuated fingers and under-actuated fingers was analyzed.Based on the analysis of the metamorphic multi-fingered hand,the symmetrical structure of the palm was designed,resulting in the largest workspace of the multi-fingered hand,and proved that the metamorphic palm has a massive upgrade for the workspace of underactuated fingers.This research contributed to the dimensional synthesis of metamorphic dexterous hands,with practical significance for the design and optimization of novel metamorphic hands.
基金supported by the European Commission H2020 Programme under HYFLIERS project contract 779411AERIAL-CORE project contract number 871479 and the ARTIC(RTI2018-102224-B-I00)projectfunded by the Spanish Agencia Estatal de Investigación。
文摘This work proposes a novel proportional-derivative(PD)-type state-dependent Riccati equation(SDRE)approach with iterative learning control(ILC)augmentation.On the one hand,the PD-type control gains could adopt many useful available criteria and tools of conventional PD controllers.On the other hand,the SDRE adds nonlinear and optimality characteristics to the controller,i.e.,increasing the stability margins.These advantages with the ILC correction part deliver a precise control law with the capability of error reduction by learning.The SDRE provides a symmetric-positive-definite distributed nonlinear suboptimal gain K(x)for the control input law u=–R–1(x)BT(x)K(x)x.The sub-blocks of the overall gain R–1(x)BT(x)K(x),are not necessarily symmetric positive definite.A new design is proposed to transform the optimal gain into two symmetric-positive-definite gains like PD-type controllers as u=–KSP(x)e–KSD(x)?.The new form allows us to analytically prove the stability of the proposed learning-based controller for mechanical systems;and presents guaranteed uniform boundedness in finite-time between learning loops.The symmetric PD-type controller is also developed for the state-dependent differential Riccati equation(SDDRE)to manipulate the final time.The SDDRE expresses a differential equation with a final boundary condition,which imposes a constraint on time that could be used for finitetime control.So,the availability of PD-type finite-time control is an asset for enhancing the conventional classical linear controllers with this tool.The learning rules benefit from the gradient descent method for both regulation and tracking cases.One of the advantages of this approach is a guaranteed-stability even from the first loop of learning.A mechanical manipulator,as an illustrative example,was simulated for both regulation and tracking problems.Successful experimental validation was done to show the capability of the system in practice by the implementation of the proposed method on a variable-pitch rotor benchmark.
基金supported by the National Natural Science Foundation of China(Grant no.51975126)the China Postdoctoral Science Foundation(Grant no.2021M700882)+1 种基金the Frontier and Key Technology Innovation Funds of Guangdong Province(Grant no.2017B050506008)the Guangdong Yangfan Program for Innovative and Entrepreneurial Teams(Grant no.2017YT05G026).
文摘Soft climbing/crawling robots have been attracting increasing attention in the soft robotics community,and many prototypes with basic locomotion have been implemented.Most existing soft robots achieve locomotion by planar bending deformation and lack sufficient mobility.Enhancing the mobility of soft climbing/crawling robots is still an open and challenging issue.To this end,we present a novel pneumatic leech-like soft robot,Leechbot,with both bending and stretching deformation for locomotion.With a morphological structure,the robot consists of a three-chambered actuator in the middle for the main motion,two chamber-net actuators that act as ankles,and two suckers at the ends for anchoring on surfaces.The peristaltic motion for locomotion is implemented by body stretching,and direction changing is achieved by body bending.Due to the novel design and two deformation modes,the robot can make turns and transit between different surfaces;the robot,hence,has excellent mobility.The development of the robot prototype is presented in detail in this paper.To control its motion,tests were carried out to determine the relationship between step length and air pressure as well as the relationship between motion speed and periodic delay time.A kinematic model was established,and the kinematic mobility and surface transitionability were analyzed.Gait planning based on the inflating sequence of the actuating chambers is presented for straight crawling,turn making,and transiting between surfaces and was verified by a series of experiments with the prototype.The results show that a high mobility in soft climbing/crawling robots can be achieved by a novel design and by proper gait planning.
基金We are grateful for financial supports from the National Natural Science Foundation of China(No.61975173)the National Key Research and Development Program of China(No.SQ2019YFC170311)+3 种基金the Major Scientific Research Project of Zhejiang Lab(No.2019MC0AD01)the Key Research and Development Project of Zhejiang Province(No.2021C05003)the Quantum Joint Funds of the Natural Foundation of Shandong Province(No.ZR2020LLZ007)the CIE-Tencent Robotics X Rhino-Bird Focused Research Program(No.2020-01-006).
文摘Flexible strain sensors play an important role in electronic skins,wearable medical devices,and advanced robots.Herein,a highly sensitive and fast response optical strain sensor with two evanescently coupled optical micro/nanofibers(MNFs)embedded in a polydimethylsiloxane(PDMS)film is proposed.The strain sensor exhibits a gauge factor as high as 64.5 for strain≤0.5%and a strain resolution of 0.0012%which corresponds to elongation of 120 nm on a 1 cm long device.As a proof-of-concept,highly sensitive fingertip pulse measurement is realized.The properties of fast temporal frequency response up to 30 kHz and a pressure sensitivity of 102 kPa^(−1) enable the sensor for sound detection.Such versatile sensor could be of great use in physiological signal monitoring,voice recognition and micro-displacement detection.
文摘Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system（ANFIS）has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace.An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.
基金Project(61273138)supported by the National Natural Science Foundation of ChinaProject(14JCZDJC39300)supported by the Key Fund of Tianjin,China
文摘One of the primary difficulties in using powered parafoil(PPF) systems is the lack of effective trajectory tracking controllers since the trajectory tracking control is the essential operation for PPF to accomplish autonomous tasks. The characteristic model(CM) based all-coefficient adaptive control(ACAC) designed for PPF systems in horizontal and vertical trajectory control is proposed. The method is easy to use and convenient to adjust and test. Just a few parameters are adapted during the control process. In application, vertical and horizontal CMs are designed and ACAC controllers are constructed to control vertical altitude and horizontal trajectory of PPF based on the proposed CMs, respectively. Result analysis of different simulations shows that the applied ACAC control method is effective for trajectory tracking of the PPF systems and the approach guarantees the transient performance of the PPF systems with better disturbance rejection ability.
基金This work was supported by the National Natural Science Foundation （NSFC, nos. 61340046, 61673030, U1613209）, Natural Science Foundation of Guangdong Province （no. 2015A030311034）, Scientific Research Project of Guangdong Province （no. 2015B010919004）, Specialized Research Fund for Strategic and Prospective Industrial Development of Shenzhen City （no. ZLZBCXLJZI20160729020003）, Shenzhen Key Laboratory for Intelligent Multimedia and Virtual Reality （ZDSYS201703031405467）.
文摘Using Complete Coverage Path Planning （CCPP）, a cleaning robot could visit every accessible area in the workspace. The dynamic environment requires the higher computation of the CCPP algorithm because the path needs to be replanned when the path might become invalid. In previous CCPP methods, when the neighbours of the current position are obstacles or have been visited, it is challenging for the robot to escape from the deadlocks with the least extra time cost. In this study, a novel CCPP algorithm is proposed to deal with deadlock problems in a dynamic environment. A priority template inspired by the short memory model could reduce the number of deadlocks by giving the priority of directions. Simultaneously, a global backtracking mechanism guides the robot to move to the next unvisited area quickly, taking the use of the explored global environmental information. What＇s more, the authors extend their CCPP algorithm to a multi-robot system with a market-based bidding process which could deploy the coverage time. Experiments of apartment-like scenes show that the authors＇ proposed algorithm can guarantee an efficient collision-free coverage in dynamic environments. The proposed method performs better than related approaches on coverage rate and overlap length.
基金National University of Sciences and Technology supported the research.
文摘For BCI systems,it is important to have an accurate and less complex architecture to control a device with enhanced accuracy.In this paper,a novel methodology for more accurate detection of the hemodynamic response has been developed using a multimodal brain-computer interface(BCI).An integrated classifier has been developed for achieving better classification accuracy using two modalities.An integrated EEG-fNIRS-based vector-phase analysis(VPA)has been conducted.An open-source dataset collected at the TechnischeUniversit鋞Berlin,including simultaneous electroencephalography(EEG)and functional near-infrared spectroscopy(fNIRS)signals of 26 healthy participants during n-back tests,has been used for this research.Instrumental and physiological noise removal has been done using preprocessing techniques followed by individually detecting activity in both modalities.With resting state threshold circle,VPA has been used to detect a hemodynamic response in fNIRS signals,whereas phase plots for EEG signals have been constructed using Hilbert Transform to detect the activity in each trial.Multiple threshold circles are drawn in the vector plane,where each circle is drawn after task completion in each trial of EEG signal.Finally,both processes are integrated into one vector-phase plot to get combined detection of hemodynamic response for activity.Results of this study illustrate that the combined EEG-fNIRS VPA yields considerably higher average classification accuracy,that is 91.35%,as compared to other classifiers such as support vector machine(SVM),convolutional neural networks(CNN),deep neural networks(DNN)and VPA(with dual-threshold circles)with classification accuracies 82%,89%,87%and 86%respectively.Outcomes of this research demonstrate that improved classification performance can be feasibly achieved using multimodal VPA for EEG-fNIRS hybrid data.
文摘In this study, the authors present the role playing learning scheme for a mobile robot to navigate socially with its human companion in populated environments. Neural networks （NNs） are constructed to parameterise a stochastic policy that directly maps sensory data collected by the robot to its velocity outputs, while respecting a set of social norms. An efficient simulative learning environment is built with maps and pedestrians trajectories collected from a number of real-world crowd data sets. In each learning iteration, a robot equipped with the NN policy is created virtually in the learning environment to play itself as a companied pedestrian and navigate towards a goal in a socially concomitant manner. Thus, this process is called role playing learning, which is formulated under a reinforcement learning framework. The NN policy is optimised end-to-end using trust region policy optimisation, with consideration of the imperfectness of robot＇s sensor measurements. Simulative and experimental results are provided to demonstrate the efficacy and superiority of the proposed method.
基金supported by the German Research Foundation(DFG)within the GRK 1362"Cooperative,Adaptive and Responsive Monitoring of Mixed Mode Environments"(www.gkmm.de)
文摘This paper deals with the problem of robust output synchronization for heterogeneous multi-agent systems.First,a new synchronization approach is presented to synchronize the outputs of heterogeneous agents.Based on noninteracting control techniques,a method is derived for homogenizing the input-output behavior of every agent.Hence,applying the same reference input signal to every agent leads to synchronization.Furthermore,a strategy for increasing the robustness of the synchronization process against exogenous disturbances is presented,which leads to a structurally constrained optimization problem.However,by a convenient reformulation of the problem,well established tools from robust control theory can be used.Moreover,it is shown that this procedure allows to separate the robustness issue from the synchronization task.The effectiveness of the approach is illustrated by a robust output synchronization example for a heterogeneous aircraft fleet.
文摘It is common for robotic fish to generate thrust using reactive force generated by the tail’s physical motion, which interacts with the surrounding fluid. The coupling effect of the body strongly correlates with this thrust. However, hydrodynamics cannot be wholly modeled in analytical form. Therefore, data-assisted modeling is necessary for robotic fish. This work presents the first method of its kind using Genetic Algorithm (GA)-based optimization methods for data-assistive modeling for robotic fish applications. To begin, experimental data are collected in real time with the robotic fish that has been designed and fabricated using 3D printing. Then, the model’s influential parameters are estimated using an optimization problem. Further, a model-based deep reinforcement learning (DRL) controller is proposed to track the desired speed through extensive simulation work. In addition to a deep deterministic policy gradient (DDPG), a twin delayed DDPG (TD3) is employed in the training of the RL agent. Unfortunately, due to its local optimization problem, the RL-DDPG controller failed to perform well during training. In contrast, the RL-TD3 controller effectively learns the control policies and overcomes the local optima problem. As a final step, controller performance is evaluated under different disturbance conditions. In contrast to DDPG and GA-tuned proportional-integral controllers, the proposed model with RL-TD3 controller significantly improves the performance.
基金supported by the AMARSI(Adaptive Modular Architecture for Rich Motor Skills,FP7-ICT-248311)Walk-Man(FP7-ICT-611832)European projects
文摘This paper considers the torque control problem for robots with flexible joints driven by electrical actuators. It is shown that the achievable closed-loop tracking bandwidth using PI torque controllers may be limited due to transmission zeros introduced by the load dynamics. This limitation is overcome by using positive feedback from the load motion in unison with PI torque controllers. The positive feedback is given in terms of load velocity, acceleration and jerk. Stability conditions for designing decentralized PI torque controllers are derived in terms of Routh-Hurwitz criteria. Disturbance rejection properties of the closed system are characterized and an analysis is carried out investigating the use of approximate positive feedback by omitting acceleration and/or jerk signals. The results of this paper are illustrated for a two DoF （degrees of freedom） system. Experimental results for a one DoF system are also included.
文摘This paper introduces a 3-dof hybrid robotic manipulator which is constructed by combining a parallel mechanism and a pantograph to increase stiffness as well as workspace. And by analyzing its kinematics and dynamics with Lagrange’s method, the dynamic model is obtained which is essential for feed-forward control of the manipulator. An explicit solution is given out. Finally, a simulation test is carried out on computers.
基金the National Convergence Research of Scientific Challenges through the National Research Foundation of Korea(NRF)the DGIST R&D Program(No.2021M3F7A1082275 and 23-CoE-BT-02)funded by the Ministry of Science and ICT.
文摘Synthetic micromotor has gained substantial attention in biomedicine and environmental remediation.Metal-based degradable micromotor composed of magnesium(Mg),zinc(Zn),and iron(Fe)have promise due to their nontoxic fuel-free propulsion,favorable biocompatibility,and safe excretion of degradation products Recent advances in degradable metallic micromotor have shown their fast movement in complex biological media,efficient cargo delivery and favorable biocompatibility.A noteworthy number of degradable metal-based micromotors employ bubble propulsion,utilizing water as fuel to generate hydrogen bubbles.This novel feature has projected degradable metallic micromotors for active in vivo drug delivery applications.In addition,understanding the degradation mechanism of these micromotors is also a key parameter for their design and performance.Its propulsion efficiency and life span govern the overall performance of a degradable metallic micromotor.Here we review the design and recent advancements of metallic degradable micromotors.Furthermore,we describe the controlled degradation,efficient in vivo drug delivery,and built-in acid neutralization capabilities of degradable micromotors with versatile biomedical applications.Moreover,we discuss micromotors’efficacy in detecting and destroying environmental pollutants.Finally,we address the limitations and future research directions of degradable metallic micromotors.
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation,Ministry of Education,Kingdom of Saudi Arabia for this research through a grant(NU/IFC/INT/01/008)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging environments.In autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of CNNs.In this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best results.Literature has proven the superiority of metaheuristic algorithms over the manual-tuning of CNNs.However,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle prediction.Thus,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction problem.To validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear unit.As per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower ones.Results show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning approach.Infield testing was also performed using the model trained with the optimal architecture,which we developed using our approach.
