The problem of trajectory tracking for a class of differentially driven wheeled mobile robots(WMRs)under partial loss of the effectiveness of the actuated wheels is investigated in this paper.Such actuator faults may ...The problem of trajectory tracking for a class of differentially driven wheeled mobile robots(WMRs)under partial loss of the effectiveness of the actuated wheels is investigated in this paper.Such actuator faults may cause the loss of strong controllability of the WMR,such that the conventional fault-tolerant control strategies unworkable.In this paper,a new mixed-gain adaption scheme is devised,which is adopted to adapt the gain of a decoupling prescribed performance controller to adaptively compensate for the loss of the effectiveness of the actuators.Different from the existing gain adaption technique which depends on both the barrier functions and their partial derivatives,ours involves only the barrier functions.This yields a lower magnitude of the resulting control signals.Our controller accomplishes trajectory tracking of the WMR with the prescribed rate and accuracy even in the faulty case,and the control design relies on neither the information of the WMR dynamics and the actuator faults nor the tools for function approximation,parameter identification,and fault detection or estimation.The comparative simulation results justify the theoretical findings.展开更多
Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems.Artificial intelligence enables real-time sensing,decision-making,and control on embedded platforms with impro...Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems.Artificial intelligence enables real-time sensing,decision-making,and control on embedded platforms with improved efficiency.This study presents the design and implementation of an autonomous radio-controlled(RC)vehicle prototype capable of lane line detection,obstacle avoidance,and navigation through dynamic path planning.The system integrates image processing and ultrasonic sensing,utilizing Raspberry Pi for vision-based tasks and ArduinoNano for real-time control.Lane line detection is achieved through conventional image processing techniques,providing the basis for local path generation,while traffic sign classification employs a You Only Look Once(YOLO)model optimized with TensorFlow Lite to support navigation decisions.Images captured by the onboard camera are processed on the Raspberry Pi to extract lane geometry and calculate steering angles,enabling the vehicle to follow the planned path.In addition,ultrasonic sensors placed in three directions at the front of the vehicle detect obstacles and allow real-time path adjustment for safe navigation.Experimental results demonstrate stable performance under controlled conditions,highlighting the system’s potential for scalable autonomous driving applications.This work confirms that deep learning methods can be efficiently deployed on low-power embedded systems,offering a practical framework for navigation,path planning,and intelligent transportation research.展开更多
In order to solve the control problem of multiple-input multiple-output(MIMO)systems in complex and variable control environments,a model-free adaptive LSAC-PID method based on deep reinforcement learning(RL)is propos...In order to solve the control problem of multiple-input multiple-output(MIMO)systems in complex and variable control environments,a model-free adaptive LSAC-PID method based on deep reinforcement learning(RL)is proposed in this paper for automatic control of mobile robots.According to the environmental feedback,the RL agent of the upper controller outputs the optimal parameters to the lower MIMO PID controllers,which can realize the real-time PID optimal control.First,a model-free adaptive MIMO PID hybrid control strategy is presented to realize real-time optimal tuning of control parameters in terms of soft-actor-critic(SAC)algorithm,which is state-of-the-art RL algorithm.Second,in order to improve the RL convergence speed and the control performance,a Lyapunov-based reward shaping method for off-policy RL algorithm is designed,and a self-adaptive LSAC-PID tuning approach with Lyapunov-based reward is then determined.Through the policy evaluation and policy improvement of the soft policy iteration,the convergence and optimality of the proposed LSAC-PID algorithm are proved mathematically.Finally,based on the proposed reward shaping method,the reward function is designed to improve the system stability for the line-following robot.The simulation and experiment results show that the proposed adaptive LSAC-PID approach has good control performance such as fast convergence speed,high generalization and high real-time performance,and achieves real-time optimal tuning of MIMO PID parameters without the system model and control loop decoupling.展开更多
Vision-based pose stabilization of nonholonomic mobile robots has received extensive attention. At present, most of the solutions of the problem do not take the robot dynamics into account in the controller design, so...Vision-based pose stabilization of nonholonomic mobile robots has received extensive attention. At present, most of the solutions of the problem do not take the robot dynamics into account in the controller design, so that these controllers are difficult to realize satisfactory control in practical application. Besides, many of the approaches suffer from the initial speed and torque jump which are not practical in the real world. Considering the kinematics and dynamics, a two-stage visual controller for solving the stabilization problem of a mobile robot is presented, applying the integration of adaptive control, sliding-mode control, and neural dynamics. In the first stage, an adaptive kinematic stabilization controller utilized to generate the command of velocity is developed based on Lyapunov theory. In the second stage, adopting the sliding-mode control approach, a dynamic controller with a variable speed function used to reduce the chattering is designed, which is utilized to generate the command of torque to make the actual velocity of the mobile robot asymptotically reach the desired velocity. Furthermore, to handle the speed and torque jump problems, the neural dynamics model is integrated into the above mentioned controllers. The stability of the proposed control system is analyzed by using Lyapunov theory. Finally, the simulation of the control law is implemented in perturbed case, and the results show that the control scheme can solve the stabilization problem effectively. The proposed control law can solve the speed and torque jump problems, overcome external disturbances, and provide a new solution for the vision-based stabilization of the mobile robot.展开更多
Target tracking control for wheeled mobile robot(WMR)need resolve the problems of kinematics model and tracking algorithm.High-order sliding mode control is a valid method used in the nonlinear tracking control system...Target tracking control for wheeled mobile robot(WMR)need resolve the problems of kinematics model and tracking algorithm.High-order sliding mode control is a valid method used in the nonlinear tracking control system,which can eliminate the chattering of sliding mode control.Currently there lacks the research of robustness and uncertain factors for high-order sliding mode control.To address the fast convergence and robustness problems of tracking target,the tracking mathematical model of WMR and the target is derived.Based on the finite-time convergence theory and second order sliding mode method,a nonlinear tracking algorithm is designed which guarantees that WMR can catch the target in finite time.At the same time an observer is applied to substitute the uncertain acceleration of the target,then a smooth nonlinear tracking algorithm is proposed.Based on Lyapunov stability theory and finite-time convergence,a finite time convergent smooth second order sliding mode controller and a target tracking algorithm are designed by using second order sliding mode method.The simulation results verified that WMR can catch up the target quickly and reduce the control discontinuity of the velocity of WMR.展开更多
To obtain the near optimal path for the mobile robots in the present of the obstacles, where the robots are subject to both the nonholonomic constraints and the bound to the curvature of the path, a simple planning i...To obtain the near optimal path for the mobile robots in the present of the obstacles, where the robots are subject to both the nonholonomic constraints and the bound to the curvature of the path, a simple planning is applied by the heuristic searching method in which Reeds and Shepp’s shortest paths are chosen as heuristic functions. It has performed well in simulation of mobile robots moving in a cluttered environment.展开更多
Path planning of a mobile robot in the presence of multiple moving obstacles is found to be a complicated problem.A planning algorithm capable of negotiating both static and moving obstacles in an unpredictable(on-lin...Path planning of a mobile robot in the presence of multiple moving obstacles is found to be a complicated problem.A planning algorithm capable of negotiating both static and moving obstacles in an unpredictable(on-line)environment is proposed.The proposed incremental algorithm plans the path by considering the quadrants in which the current positions of obstacles as well as target are situated.Also,the governing equations for the shortest path are derived.The proposed mathematical model describes the motion(satisfying constraints of the mobile robot)along a collision-free path.Further,the algorithm is applicable to dynamic environments with fixed or moving targets.Simulation results show the effectiveness of the proposed algorithm.Comparison of results with the improved artificial potential field(iAPF)algorithm shows that the proposed algorithm yields shorter path length with less computation time.展开更多
Odometry using incremental wheel encoder odometry suffers from the accumulation of kinematic sensors provides the relative robot pose estimation. However, the modeling errors of wheels as the robot＇s travel distance ...Odometry using incremental wheel encoder odometry suffers from the accumulation of kinematic sensors provides the relative robot pose estimation. However, the modeling errors of wheels as the robot＇s travel distance increases. Therefore, the systematic errors need to be calibrated. The University of Michigan Benchmark（UMBmark） method is a widely used calibration scheme of the systematic errors in two wheel differential mobile robots. In this paper, the accurate parameter estimation of systematic errors is proposed by extending the conventional method. The contributions of this paper can be summarized as two issues. The first contribution is to present new calibration equations that reduce the systematic odometry errors. The new equations were derived to overcome the limitation of conventional schemes. The second contribu tion is to propose the design guideline of the test track for calibration experiments. The calibration performance can be im proved by appropriate design of the test track. The simulations and experimental results show that the accurate parameter es timation can be implemented by the proposed method.展开更多
This paper investigates the adaptive fuzzy finite-time output-feedback fault-tolerant control (FTC) problemfor a class of nonlinear underactuated wheeled mobile robots (UWMRs) system with intermittent actuatorfaults. ...This paper investigates the adaptive fuzzy finite-time output-feedback fault-tolerant control (FTC) problemfor a class of nonlinear underactuated wheeled mobile robots (UWMRs) system with intermittent actuatorfaults. The UWMR system includes unknown nonlinear dynamics and immeasurable states. Fuzzy logic systems(FLSs) are utilized to work out immeasurable functions. Furthermore, with the support of the backsteppingcontrol technique and adaptive fuzzy state observer, a fuzzy adaptive finite-time output-feedback FTC scheme isdeveloped under the intermittent actuator faults. It is testifying the scheme can ensure the controlled nonlinearUWMRs is stable and the estimation errors are convergent. Finally, the comparison results and simulationvalidate the effectiveness of the proposed fuzzy adaptive finite-time FTC approach.展开更多
In this paper, the leader-following consensus problem for multi-agent linear dynamic systems is considered. All agents and leader have identical multi-input multi-output （MIMO） linear dynamics that can be of any ord...In this paper, the leader-following consensus problem for multi-agent linear dynamic systems is considered. All agents and leader have identical multi-input multi-output （MIMO） linear dynamics that can be of any order, and only the output information of each agent is delivered throughout the communication network. When the interaction topology is fixed, the leader-following consensus is attained by Ho~ dynamic output feedback control, and the sufficient condition of robust controllers is equal to the solvability of linear matrix inequality （LMI）. The whole analysis is based on spectral decomposition and an equivalent decoupled structure achieved, and the stability of the system is proved. Finally, we extended the theoretical results to the case that the interaction topology is switching. The simulation results for multiple mobile robots show the effectiveness of the devised methods.展开更多
A practical serf-localization scheme for mobile robots is proposed and implemented by utilizing sonar sensors. Specifically, the localization problem is solved by employing Monte Carlo method with a new mechanism prop...A practical serf-localization scheme for mobile robots is proposed and implemented by utilizing sonar sensors. Specifically, the localization problem is solved by employing Monte Carlo method with a new mechanism proposed to calculate the samples＇ weights; the convergence and veracity of the sample set are guaranteed by the designed resampling and scattering process. The proposed serf-localization algorithm is fully implemented on a specific mobile robot system, and experimental results illustrate that it provides an efficient solution for the kidnapped problem.展开更多
The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accur...The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.展开更多
The small-tracked mobile robots（ STMRs） are small,portable and concealed,and they are widely used in scouting,investigation,rescue and assistance. In this paper,a mechanical model is established based on the multi-b...The small-tracked mobile robots（ STMRs） are small,portable and concealed,and they are widely used in scouting,investigation,rescue and assistance. In this paper,a mechanical model is established based on the multi-body dynamic software RecurD yn,and a control system is simulated through Simulink,including its kinematics model,speed controller,motors＇ model. Associating the mechanical and control model,the cosimulation model is established for STMRs. The co-simulation approach is applied to optimize the motor parameters. A series of experiments are conducted to examine the accuracy of the virtual prototype,and the results demonstrate that the STMR virtual prototype can exactly illustrate the dynamic performance of the physical one.The co-simulation of mechanical model and control model is applied in forecasting and debugging critical parameters,also it provides guidance in defining motor＇s peak current.展开更多
This paper deals with the stabilization of dynamic systems for two omni directional mobile robots by using the inner product of two vectors, one is from a robot's position to another's, the other is from a ro...This paper deals with the stabilization of dynamic systems for two omni directional mobile robots by using the inner product of two vectors, one is from a robot's position to another's, the other is from a robot's target point to another's. The multi step control laws given can exponentially stabilize the dynamic system and make the distance between two robots be greater than or equal to the collision free safe distance. The application of it to two omni directional mobile robots is described. Simulation result shows that the proposed controller is effective.展开更多
This paper investigates the adaptive neural network(NN)event-triggered secure formation control problem for nonholonomic mobile robots(NMRs)subject to deception attacks.The NNs are employed to approximate unknown nonl...This paper investigates the adaptive neural network(NN)event-triggered secure formation control problem for nonholonomic mobile robots(NMRs)subject to deception attacks.The NNs are employed to approximate unknown nonlinear functions in robotic dynamics.Since the transmission channel from sensor-to-controller is vulnerable to deception attacks,a NN estimation technique is introduced to estimate the unknown deception attacks.In order to alleviate the amount of communication between controller-and-actuator,an event-triggered mechanism with relative threshold strategy is established.Then,an adaptive NN event-triggered secure formation control method is proposed.It is proved that all closed-loop signals of controlled systems are bounded and the formation tracking errors converge a neighborhood of the origin in the presence of deception attacks.The comparative simulations illustrate the effectiveness of the proposed secure formation control scheme.展开更多
Many applications above the capability of a single robot need the cooperation of multiple mobile robots, but effective cooperation is hard to achieve. In this paper, a master slave method is proposed to control the m...Many applications above the capability of a single robot need the cooperation of multiple mobile robots, but effective cooperation is hard to achieve. In this paper, a master slave method is proposed to control the motions of multiple mobile robots that cooperatively transport a common object from a start point to a goal point. A noholonomic kinematic model to constrain the motions of multiple mobile robots is built in order to achieve cooperative motions of them, and a “Dynamic Coordinator” strategy is used to deal with the collision avoidance of the master robot and slave robot individually. Simulation results show the robustness and effectiveness of the method.展开更多
This paper presents an extended Dyna-Q algorithm to improve efficiency of the standard Dyna-Q algorithm.In the first episodes of the standard Dyna-Q algorithm,the agent travels blindly to find a goal position.To overc...This paper presents an extended Dyna-Q algorithm to improve efficiency of the standard Dyna-Q algorithm.In the first episodes of the standard Dyna-Q algorithm,the agent travels blindly to find a goal position.To overcome this weakness,our approach is to use a maximum likelihood model of all state-action pairs to choose actions and update Q-values in the first few episodes.Our algorithm is compared with one-step Q-learning algorithm and the standard Dyna-Q algorithm for the path planning problem in maze environments.Experimental results show that the proposed algorithm is more efficient than the one-step Q-learning algorithm as well as the standard Dyna-Q algorithm,especially in the large environment of states.展开更多
This paper presnts a team-oriented programming method specially designed for multiple mobile robots. The team, which is a typical constitution structure in multi-robot system, forms after the user selects suitable rob...This paper presnts a team-oriented programming method specially designed for multiple mobile robots. The team, which is a typical constitution structure in multi-robot system, forms after the user selects suitable robots, assigns their roles and sets related parameters. Team behavior module are introduced for the team-level behavior description and the temporal chain of these modules, realized by finite state automata, partitions the team tasks into discrete operating states and triggers. A graphical programming tool is designed for the team task description with visual diagrams. The real robots experiment of adaptive formation shows the system＇s usability and effectivity.展开更多
This paper proposes a self-position estimate algorithm for the multiple mobile robots; each robot uses two omnidirectional cameras and an accelerometer. In recent years, the Great East Japan Earthquake and large-scale...This paper proposes a self-position estimate algorithm for the multiple mobile robots; each robot uses two omnidirectional cameras and an accelerometer. In recent years, the Great East Japan Earthquake and large-scale disasters have occurred frequently in Japan. From this, development of the searching robot which supports the rescue team to perform a relief activity at a large-scale disaster is indispensable. Then, this research has developed the searching robot group system with two or more mobile robots. In this research, the searching robot equips with two omnidirectional cameras and an accelerometer. In order to perform distance measurement using two omnidirectional cameras, each parameter of an omnidirectional camera and the position and posture between two omnidirectional cameras have to be calibrated in advance. If there are few mobile robots, the calibration time of each omnidirectional camera does not pose a problem. However, if the calibration is separately performed when using two or more robots in a disaster site, etc., it will take huge calibration time. Then, this paper proposed the algorithm which estimates a mobile robot＇s position and the parameter of the position and posture between two omnidirectional cameras simultaneously. The algorithm proposed in this paper extended Nonlinear Transformation （NLT） Method. This paper conducted the simulation experiment to check the validity of the proposed algorithm. In some simulation experiments, one mobile robot moves and observes the circumference of another mobile robot which has stopped at a certain place. This paper verified whether the mobile robot can estimate position using the measurement value when the number of observation times becomes 10 times in n/18 of observation intervals. The result of the simulation shows the effectiveness of the algorithm.展开更多
This paper proposes the cooperative position estimation of a group of mobile robots, which pertbrms disaster relief tasks in a wide area. When searching the wide area, it becomes important to know a robot＇s position ...This paper proposes the cooperative position estimation of a group of mobile robots, which pertbrms disaster relief tasks in a wide area. When searching the wide area, it becomes important to know a robot＇s position correctly. However, for each mobile robot, it is impossible to know its own position correctly. Therefore, each mobile robot estimates its position from the data of sensor equipped on it. Generally, the sensor data is incorrect since there is sensor noise, etc. This research considers two types of the sensor data errors from omnidirectional camera. One is the error of white noise of the image captured by omnidirectional camera and so on. Another is the error of position and posture between two omnidirectional cameras. To solve the error of latter case, we proposed a self-position estimation algorithm for multiple mobile robots using two omnidirectional cameras and an accelerometer. On the other hand, to solve the error of the former case, this paper proposed an algorithm of cooperative position estimation for multiple mobile robots. In this algorithm, each mobile robot uses two omnidirectional cameras to observe the surrounding mobile robot and get the relative position between mobile robots. Each mobile robot estimates its position with only measurement data of each other mobile robots. The algorithm is based on a Bayesian filtering. Simulations of the proposed cooperative position estimation algorithm for multiple mobile robots are performed. The results show that position estimation is possible by only using measurement value from each other robot.展开更多
基金supported in part by the National Natural Science Foundation of China under Grants 61991404,62103093 and 62473089the Research Program of the Liaoning Liaohe Laboratory,China under Grant LLL23ZZ-05-01+5 种基金the Key Research and Development Program of Liaoning Province of China under Grant 2023JH26/10200011the 111 Project 2.0 of China under Grant B08015,the National Key Research and Development Program of China under Grant 2022YFB3305905the Xingliao Talent Program of Liaoning Province of China under Grant XLYC2203130the Natural Science Foundation of Liaoning Province of China under Grants 2024JH3/10200012 and 2023-MS-087the Open Research Project of the State Key Laboratory of Industrial Control Technology of China under Grant ICT2024B12the Fundamental Research Funds for the Central Universities of China under Grants N2108003 and N2424004.
文摘The problem of trajectory tracking for a class of differentially driven wheeled mobile robots(WMRs)under partial loss of the effectiveness of the actuated wheels is investigated in this paper.Such actuator faults may cause the loss of strong controllability of the WMR,such that the conventional fault-tolerant control strategies unworkable.In this paper,a new mixed-gain adaption scheme is devised,which is adopted to adapt the gain of a decoupling prescribed performance controller to adaptively compensate for the loss of the effectiveness of the actuators.Different from the existing gain adaption technique which depends on both the barrier functions and their partial derivatives,ours involves only the barrier functions.This yields a lower magnitude of the resulting control signals.Our controller accomplishes trajectory tracking of the WMR with the prescribed rate and accuracy even in the faulty case,and the control design relies on neither the information of the WMR dynamics and the actuator faults nor the tools for function approximation,parameter identification,and fault detection or estimation.The comparative simulation results justify the theoretical findings.
文摘Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems.Artificial intelligence enables real-time sensing,decision-making,and control on embedded platforms with improved efficiency.This study presents the design and implementation of an autonomous radio-controlled(RC)vehicle prototype capable of lane line detection,obstacle avoidance,and navigation through dynamic path planning.The system integrates image processing and ultrasonic sensing,utilizing Raspberry Pi for vision-based tasks and ArduinoNano for real-time control.Lane line detection is achieved through conventional image processing techniques,providing the basis for local path generation,while traffic sign classification employs a You Only Look Once(YOLO)model optimized with TensorFlow Lite to support navigation decisions.Images captured by the onboard camera are processed on the Raspberry Pi to extract lane geometry and calculate steering angles,enabling the vehicle to follow the planned path.In addition,ultrasonic sensors placed in three directions at the front of the vehicle detect obstacles and allow real-time path adjustment for safe navigation.Experimental results demonstrate stable performance under controlled conditions,highlighting the system’s potential for scalable autonomous driving applications.This work confirms that deep learning methods can be efficiently deployed on low-power embedded systems,offering a practical framework for navigation,path planning,and intelligent transportation research.
基金the National Key R&D Program of China(No.2018YFB1308400)。
文摘In order to solve the control problem of multiple-input multiple-output(MIMO)systems in complex and variable control environments,a model-free adaptive LSAC-PID method based on deep reinforcement learning(RL)is proposed in this paper for automatic control of mobile robots.According to the environmental feedback,the RL agent of the upper controller outputs the optimal parameters to the lower MIMO PID controllers,which can realize the real-time PID optimal control.First,a model-free adaptive MIMO PID hybrid control strategy is presented to realize real-time optimal tuning of control parameters in terms of soft-actor-critic(SAC)algorithm,which is state-of-the-art RL algorithm.Second,in order to improve the RL convergence speed and the control performance,a Lyapunov-based reward shaping method for off-policy RL algorithm is designed,and a self-adaptive LSAC-PID tuning approach with Lyapunov-based reward is then determined.Through the policy evaluation and policy improvement of the soft policy iteration,the convergence and optimality of the proposed LSAC-PID algorithm are proved mathematically.Finally,based on the proposed reward shaping method,the reward function is designed to improve the system stability for the line-following robot.The simulation and experiment results show that the proposed adaptive LSAC-PID approach has good control performance such as fast convergence speed,high generalization and high real-time performance,and achieves real-time optimal tuning of MIMO PID parameters without the system model and control loop decoupling.
基金supported by National Key Basic Research and Development Program of China (973 Program,Grant No. 2009CB320602)National Natural Science Foundation of China (Grant Nos. 60834004,61025018)+2 种基金National Science and Technology Major Project of China(Grant No. 2011ZX02504-008)Fundamental Research Funds for the Central Universities of China (Grant No. ZZ1222)Key Laboratory of Advanced Engineering Surveying of NASMG of China (Grant No.TJES1106)
文摘Vision-based pose stabilization of nonholonomic mobile robots has received extensive attention. At present, most of the solutions of the problem do not take the robot dynamics into account in the controller design, so that these controllers are difficult to realize satisfactory control in practical application. Besides, many of the approaches suffer from the initial speed and torque jump which are not practical in the real world. Considering the kinematics and dynamics, a two-stage visual controller for solving the stabilization problem of a mobile robot is presented, applying the integration of adaptive control, sliding-mode control, and neural dynamics. In the first stage, an adaptive kinematic stabilization controller utilized to generate the command of velocity is developed based on Lyapunov theory. In the second stage, adopting the sliding-mode control approach, a dynamic controller with a variable speed function used to reduce the chattering is designed, which is utilized to generate the command of torque to make the actual velocity of the mobile robot asymptotically reach the desired velocity. Furthermore, to handle the speed and torque jump problems, the neural dynamics model is integrated into the above mentioned controllers. The stability of the proposed control system is analyzed by using Lyapunov theory. Finally, the simulation of the control law is implemented in perturbed case, and the results show that the control scheme can solve the stabilization problem effectively. The proposed control law can solve the speed and torque jump problems, overcome external disturbances, and provide a new solution for the vision-based stabilization of the mobile robot.
基金supported by National Natural Science Foundation of China(Grant No.61075081)State Key Laboratory of Robotics Technique and System Foundation,Harbin Institute of Technology,China(Grant No.SKIRS200802A02)
文摘Target tracking control for wheeled mobile robot(WMR)need resolve the problems of kinematics model and tracking algorithm.High-order sliding mode control is a valid method used in the nonlinear tracking control system,which can eliminate the chattering of sliding mode control.Currently there lacks the research of robustness and uncertain factors for high-order sliding mode control.To address the fast convergence and robustness problems of tracking target,the tracking mathematical model of WMR and the target is derived.Based on the finite-time convergence theory and second order sliding mode method,a nonlinear tracking algorithm is designed which guarantees that WMR can catch the target in finite time.At the same time an observer is applied to substitute the uncertain acceleration of the target,then a smooth nonlinear tracking algorithm is proposed.Based on Lyapunov stability theory and finite-time convergence,a finite time convergent smooth second order sliding mode controller and a target tracking algorithm are designed by using second order sliding mode method.The simulation results verified that WMR can catch up the target quickly and reduce the control discontinuity of the velocity of WMR.
文摘To obtain the near optimal path for the mobile robots in the present of the obstacles, where the robots are subject to both the nonholonomic constraints and the bound to the curvature of the path, a simple planning is applied by the heuristic searching method in which Reeds and Shepp’s shortest paths are chosen as heuristic functions. It has performed well in simulation of mobile robots moving in a cluttered environment.
文摘Path planning of a mobile robot in the presence of multiple moving obstacles is found to be a complicated problem.A planning algorithm capable of negotiating both static and moving obstacles in an unpredictable(on-line)environment is proposed.The proposed incremental algorithm plans the path by considering the quadrants in which the current positions of obstacles as well as target are situated.Also,the governing equations for the shortest path are derived.The proposed mathematical model describes the motion(satisfying constraints of the mobile robot)along a collision-free path.Further,the algorithm is applicable to dynamic environments with fixed or moving targets.Simulation results show the effectiveness of the proposed algorithm.Comparison of results with the improved artificial potential field(iAPF)algorithm shows that the proposed algorithm yields shorter path length with less computation time.
基金The MKE(The Ministry of Knowledge Economy),Korea,under the ITRC(Infor mation Technology Research Center)support programsupervised by the NIPA(National ITIndustry Promotion Agency)(NIPA-2012-C1090-1221-0010)TheMKE,Korea,under the Human Resources Development Programfor Convergence Robot Specialists support programsu-pervised by the NIPA(NIPA-2012-H1502-12-1002)Basic Science Research Program through the NRF funded by the MEST(2011-0025980)and MEST(2012-0005487)
文摘Odometry using incremental wheel encoder odometry suffers from the accumulation of kinematic sensors provides the relative robot pose estimation. However, the modeling errors of wheels as the robot＇s travel distance increases. Therefore, the systematic errors need to be calibrated. The University of Michigan Benchmark（UMBmark） method is a widely used calibration scheme of the systematic errors in two wheel differential mobile robots. In this paper, the accurate parameter estimation of systematic errors is proposed by extending the conventional method. The contributions of this paper can be summarized as two issues. The first contribution is to present new calibration equations that reduce the systematic odometry errors. The new equations were derived to overcome the limitation of conventional schemes. The second contribu tion is to propose the design guideline of the test track for calibration experiments. The calibration performance can be im proved by appropriate design of the test track. The simulations and experimental results show that the accurate parameter es timation can be implemented by the proposed method.
基金the National Natural Science Foundation of China under Grant U22A2043.
文摘This paper investigates the adaptive fuzzy finite-time output-feedback fault-tolerant control (FTC) problemfor a class of nonlinear underactuated wheeled mobile robots (UWMRs) system with intermittent actuatorfaults. The UWMR system includes unknown nonlinear dynamics and immeasurable states. Fuzzy logic systems(FLSs) are utilized to work out immeasurable functions. Furthermore, with the support of the backsteppingcontrol technique and adaptive fuzzy state observer, a fuzzy adaptive finite-time output-feedback FTC scheme isdeveloped under the intermittent actuator faults. It is testifying the scheme can ensure the controlled nonlinearUWMRs is stable and the estimation errors are convergent. Finally, the comparison results and simulationvalidate the effectiveness of the proposed fuzzy adaptive finite-time FTC approach.
文摘In this paper, the leader-following consensus problem for multi-agent linear dynamic systems is considered. All agents and leader have identical multi-input multi-output （MIMO） linear dynamics that can be of any order, and only the output information of each agent is delivered throughout the communication network. When the interaction topology is fixed, the leader-following consensus is attained by Ho~ dynamic output feedback control, and the sufficient condition of robust controllers is equal to the solvability of linear matrix inequality （LMI）. The whole analysis is based on spectral decomposition and an equivalent decoupled structure achieved, and the stability of the system is proved. Finally, we extended the theoretical results to the case that the interaction topology is switching. The simulation results for multiple mobile robots show the effectiveness of the devised methods.
基金Supported by the National Natural Science Foundation of China (No. 60875055)Natural Science Foundation of Tianjin (No. 07JCY-BJC05400)Program for New Century Excellent Talents in University (No. NCET-06-0210)
文摘A practical serf-localization scheme for mobile robots is proposed and implemented by utilizing sonar sensors. Specifically, the localization problem is solved by employing Monte Carlo method with a new mechanism proposed to calculate the samples＇ weights; the convergence and veracity of the sample set are guaranteed by the designed resampling and scattering process. The proposed serf-localization algorithm is fully implemented on a specific mobile robot system, and experimental results illustrate that it provides an efficient solution for the kidnapped problem.
文摘The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.
基金Supported by Basic Research Foundation of Beijing Institute of Technology(20130242015)
文摘The small-tracked mobile robots（ STMRs） are small,portable and concealed,and they are widely used in scouting,investigation,rescue and assistance. In this paper,a mechanical model is established based on the multi-body dynamic software RecurD yn,and a control system is simulated through Simulink,including its kinematics model,speed controller,motors＇ model. Associating the mechanical and control model,the cosimulation model is established for STMRs. The co-simulation approach is applied to optimize the motor parameters. A series of experiments are conducted to examine the accuracy of the virtual prototype,and the results demonstrate that the STMR virtual prototype can exactly illustrate the dynamic performance of the physical one.The co-simulation of mechanical model and control model is applied in forecasting and debugging critical parameters,also it provides guidance in defining motor＇s peak current.
文摘This paper deals with the stabilization of dynamic systems for two omni directional mobile robots by using the inner product of two vectors, one is from a robot's position to another's, the other is from a robot's target point to another's. The multi step control laws given can exponentially stabilize the dynamic system and make the distance between two robots be greater than or equal to the collision free safe distance. The application of it to two omni directional mobile robots is described. Simulation result shows that the proposed controller is effective.
基金supported by the National Natural Science Foundation of China under 62173172.
文摘This paper investigates the adaptive neural network(NN)event-triggered secure formation control problem for nonholonomic mobile robots(NMRs)subject to deception attacks.The NNs are employed to approximate unknown nonlinear functions in robotic dynamics.Since the transmission channel from sensor-to-controller is vulnerable to deception attacks,a NN estimation technique is introduced to estimate the unknown deception attacks.In order to alleviate the amount of communication between controller-and-actuator,an event-triggered mechanism with relative threshold strategy is established.Then,an adaptive NN event-triggered secure formation control method is proposed.It is proved that all closed-loop signals of controlled systems are bounded and the formation tracking errors converge a neighborhood of the origin in the presence of deception attacks.The comparative simulations illustrate the effectiveness of the proposed secure formation control scheme.
文摘Many applications above the capability of a single robot need the cooperation of multiple mobile robots, but effective cooperation is hard to achieve. In this paper, a master slave method is proposed to control the motions of multiple mobile robots that cooperatively transport a common object from a start point to a goal point. A noholonomic kinematic model to constrain the motions of multiple mobile robots is built in order to achieve cooperative motions of them, and a “Dynamic Coordinator” strategy is used to deal with the collision avoidance of the master robot and slave robot individually. Simulation results show the robustness and effectiveness of the method.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,Science and Technology(2010-0012609)
文摘This paper presents an extended Dyna-Q algorithm to improve efficiency of the standard Dyna-Q algorithm.In the first episodes of the standard Dyna-Q algorithm,the agent travels blindly to find a goal position.To overcome this weakness,our approach is to use a maximum likelihood model of all state-action pairs to choose actions and update Q-values in the first few episodes.Our algorithm is compared with one-step Q-learning algorithm and the standard Dyna-Q algorithm for the path planning problem in maze environments.Experimental results show that the proposed algorithm is more efficient than the one-step Q-learning algorithm as well as the standard Dyna-Q algorithm,especially in the large environment of states.
基金国家高技术研究发展计划(863计划)，the National Natural Science Foundation of China
文摘This paper presnts a team-oriented programming method specially designed for multiple mobile robots. The team, which is a typical constitution structure in multi-robot system, forms after the user selects suitable robots, assigns their roles and sets related parameters. Team behavior module are introduced for the team-level behavior description and the temporal chain of these modules, realized by finite state automata, partitions the team tasks into discrete operating states and triggers. A graphical programming tool is designed for the team task description with visual diagrams. The real robots experiment of adaptive formation shows the system＇s usability and effectivity.
文摘This paper proposes a self-position estimate algorithm for the multiple mobile robots; each robot uses two omnidirectional cameras and an accelerometer. In recent years, the Great East Japan Earthquake and large-scale disasters have occurred frequently in Japan. From this, development of the searching robot which supports the rescue team to perform a relief activity at a large-scale disaster is indispensable. Then, this research has developed the searching robot group system with two or more mobile robots. In this research, the searching robot equips with two omnidirectional cameras and an accelerometer. In order to perform distance measurement using two omnidirectional cameras, each parameter of an omnidirectional camera and the position and posture between two omnidirectional cameras have to be calibrated in advance. If there are few mobile robots, the calibration time of each omnidirectional camera does not pose a problem. However, if the calibration is separately performed when using two or more robots in a disaster site, etc., it will take huge calibration time. Then, this paper proposed the algorithm which estimates a mobile robot＇s position and the parameter of the position and posture between two omnidirectional cameras simultaneously. The algorithm proposed in this paper extended Nonlinear Transformation （NLT） Method. This paper conducted the simulation experiment to check the validity of the proposed algorithm. In some simulation experiments, one mobile robot moves and observes the circumference of another mobile robot which has stopped at a certain place. This paper verified whether the mobile robot can estimate position using the measurement value when the number of observation times becomes 10 times in n/18 of observation intervals. The result of the simulation shows the effectiveness of the algorithm.
文摘This paper proposes the cooperative position estimation of a group of mobile robots, which pertbrms disaster relief tasks in a wide area. When searching the wide area, it becomes important to know a robot＇s position correctly. However, for each mobile robot, it is impossible to know its own position correctly. Therefore, each mobile robot estimates its position from the data of sensor equipped on it. Generally, the sensor data is incorrect since there is sensor noise, etc. This research considers two types of the sensor data errors from omnidirectional camera. One is the error of white noise of the image captured by omnidirectional camera and so on. Another is the error of position and posture between two omnidirectional cameras. To solve the error of latter case, we proposed a self-position estimation algorithm for multiple mobile robots using two omnidirectional cameras and an accelerometer. On the other hand, to solve the error of the former case, this paper proposed an algorithm of cooperative position estimation for multiple mobile robots. In this algorithm, each mobile robot uses two omnidirectional cameras to observe the surrounding mobile robot and get the relative position between mobile robots. Each mobile robot estimates its position with only measurement data of each other mobile robots. The algorithm is based on a Bayesian filtering. Simulations of the proposed cooperative position estimation algorithm for multiple mobile robots are performed. The results show that position estimation is possible by only using measurement value from each other robot.
