Dear Editor,This letter considers the formation control of multiple mobile robot systems(MMRS)that only relies on the local observation information.A new distributed finite-time observer is proposed for MMRS under dir...Dear Editor,This letter considers the formation control of multiple mobile robot systems(MMRS)that only relies on the local observation information.A new distributed finite-time observer is proposed for MMRS under directed graph to estimate the relative information between each follower robot and the leader robot.Then the formation control problem is transformed into the tracking problem and a finite-time tracking controller is proposed based on the robot model feature.展开更多
Mobile robots represented by smart wheelchairs can assist elderly people with mobility difficulties.This paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,wh...Mobile robots represented by smart wheelchairs can assist elderly people with mobility difficulties.This paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior maps.In order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic goals.At last,comparative experiments were carried out in the real environment.Results show that our method is superior in terms of safety,comfort and docking accuracy.展开更多
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
To effectively improve the adaptability and traversal abilities of a multi-terrain mobile robot under the dynamic excitation of multiple roads,we explore the mobile robot’s vibration and joint driving output stall ca...To effectively improve the adaptability and traversal abilities of a multi-terrain mobile robot under the dynamic excitation of multiple roads,we explore the mobile robot’s vibration and joint driving output stall caused by the dynamic excitation of the road spectrum function and analyze techniques for reducing the vibration and enhancing the driving moment of a four-wheel differential-speed mobile robot.A double-wishbone vibration reduction suspension and a moment compensator were designed for a multi-terrain mobile robot by theoretically describing its suspensionwheel-road dynamics.Also,the mobile robot’s road adaptability and traversal abilities were mathematically characterized under dynamic excitation.Co-simulation in ADAMS-MATLAB/Simulink is performed such as the harsh condition of in situ rotation and outdoor experimental schemes are implemented in which the experimental data are analyzed.The experimental result verifies the correctness of the theoretical analysis,as well as the effectiveness of the vibration reduction suspension and the moment compensator.The compatibility of the mobile robot’s driving mechanisms with road traversal abilities has been improved under various terrain conditions in complex field operation scenarios.展开更多
Precise and robust three-dimensional object detection(3DOD)presents a promising opportunity in the field of mobile robot(MR)navigation.Monocular 3DOD techniques typically involve extending existing twodimensional obje...Precise and robust three-dimensional object detection(3DOD)presents a promising opportunity in the field of mobile robot(MR)navigation.Monocular 3DOD techniques typically involve extending existing twodimensional object detection(2DOD)frameworks to predict the three-dimensional bounding box(3DBB)of objects captured in 2D RGB images.However,these methods often require multiple images,making them less feasible for various real-time scenarios.To address these challenges,the emergence of agile convolutional neural networks(CNNs)capable of inferring depth froma single image opens a new avenue for investigation.The paper proposes a novel ELDENet network designed to produce cost-effective 3DBounding Box Estimation(3D-BBE)froma single image.This novel framework comprises the PP-LCNet as the encoder and a fast convolutional decoder.Additionally,this integration includes a Squeeze-Exploit(SE)module utilizing the Math Kernel Library for Deep Neural Networks(MKLDNN)optimizer to enhance convolutional efficiency and streamline model size during effective training.Meanwhile,the proposed multi-scale sub-pixel decoder generates high-quality depth maps while maintaining a compact structure.Furthermore,the generated depthmaps provide a clear perspective with distance details of objects in the environment.These depth insights are combined with 2DOD for precise evaluation of 3D Bounding Boxes(3DBB),facilitating scene understanding and optimal route planning for mobile robots.Based on the estimated object center of the 3DBB,the Deep Reinforcement Learning(DRL)-based obstacle avoidance strategy for MRs is developed.Experimental results demonstrate that our model achieves state-of-the-art performance across three datasets:NYU-V2,KITTI,and Cityscapes.Overall,this framework shows significant potential for adaptation in intelligent mechatronic systems,particularly in developing knowledge-driven systems for mobile robot navigation.展开更多
At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown ...At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.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
Existing mobile robots mostly use graph search algorithms for path planning,which suffer from relatively low planning efficiency owing to high redundancy and large computational complexity.Due to the limitations of th...Existing mobile robots mostly use graph search algorithms for path planning,which suffer from relatively low planning efficiency owing to high redundancy and large computational complexity.Due to the limitations of the neighborhood search strategy,the robots could hardly obtain the most optimal global path.A global path planning algorithm,denoted as EDG*,is proposed by expanding nodes using a well-designed expanding disconnected graph operator(EDG)in this paper.Firstly,all obstacles are marked and their corners are located through the map pre-processing.Then,the EDG operator is designed to find points in non-obstruction areas to complete the rapid expansion of disconnected nodes.Finally,the EDG*heuristic iterative algorithm is proposed.It selects the candidate node through a specific valuation function and realizes the node expansion while avoiding collision with a minimum offset.Path planning experiments were conducted in a typical indoor environment and on the public dataset CSM.The result shows that the proposed EDG*reduced the planning time by more than 90%and total length of paths reduced by more than 4.6%.Compared to A*,Dijkstra and JPS,EDG*does not show an exponential explosion effect in map size.The EDG*showed better performance in terms of path smoothness,and collision avoidance.This shows that the EDG*algorithm proposed in this paper can improve the efficiency of path planning and enhance path quality.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
The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-base...The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.展开更多
This paper proposes a novel fixed-time sliding mode control approach for trajectory-tracking tasks of a mecanum-wheeled omnidirectional mobile robot.First,the idea of two-phase attractors is introduced into the domain...This paper proposes a novel fixed-time sliding mode control approach for trajectory-tracking tasks of a mecanum-wheeled omnidirectional mobile robot.First,the idea of two-phase attractors is introduced into the domain of sliding mode control,and a new fixed-time sliding surface is proposed.Then,according to this sliding surface,a new type of nonsingular fast terminal sliding mode control algorithm is designed for the omnidirectional mobile robot,which can realize a fast fixed-time convergence property.The stability of the control system is proven scrupulously,and a guideline for control-parameter tuning is expounded.Finally,experiments are implemented to test the trajectory-tracking performance of the robot.Experimental results demonstrate the superiority of the proposed sliding surface and the corresponding control scheme in comparison with benchmark controllers.展开更多
Object tracking is one of the major tasks for mobile robots in many real-world applications.Also,artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile r...Object tracking is one of the major tasks for mobile robots in many real-world applications.Also,artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation.In contrast to previous simulation studies,this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue(RGB)colored objects in a real experimental field.Moreover,a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative(PID)schemes to achieve accurate tracking results,considering robot command delay and tolerance errors.The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators.Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker.Classical PID control failed to handle some tracking scenarios in this study.The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets,while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm,respectively.These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads.展开更多
A distributed model predictive control(DMPC)method based on robust control barrier function(RCBF)is developed to achieve the safe formation target of multi-autonomous mobile robot systems in an uncertain disturbed env...A distributed model predictive control(DMPC)method based on robust control barrier function(RCBF)is developed to achieve the safe formation target of multi-autonomous mobile robot systems in an uncertain disturbed environment.The first step is to analyze the safety requirements of the system during safe formation and categorize them into collision avoidance and distance connectivity maintenance.RCBF constraints are designed based on collision avoidance and connectivity maintenance requirements,and security constraints are achieved through a combination.Then,the specified safety constraints are integrated with the objective of forming a multi-autonomous mobile robot formation.To ensure safe control,the optimization problem is integrated with the DMPC method.Finally,the RCBF-DMPC algorithm is proposed to ensure iterative feasibility and stability while meeting the constraints and expected objectives.Simulation experiments illustrate that the designed algorithm can achieve cooperative formation and ensure system security.展开更多
Model predictive control(MPC)is a model-based optimal control strategy widely used in robot systems.In this work,the MPC controller tuning problem for the path tracking of the wheeled mobile robot is studied and a nov...Model predictive control(MPC)is a model-based optimal control strategy widely used in robot systems.In this work,the MPC controller tuning problem for the path tracking of the wheeled mobile robot is studied and a novel self-tuning approach is developed.First,two novel path tracking performance indices,i.e.,steadystate time ratio and steady-state distance ratio are proposed to more accurately reflect the control performance.Second,the mapping relationship between the proposed indices and the MPC parameters is established based on machine learning technique,and then a novel controller structure which can automatically tune the control parameters online is further designed.Finally,experimental verification with an actual wheeled mobile robot is conducted,which shows that the proposed method could outperform the existing method via achieving significant improvement in the rapidity,accuracy and adaptability of the robot path tracking.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
In order to improve a mobile robot＇s autonomy in unknown environments, a novel intelligent controller is designed. The proposed controller is based on fuzzy logic with the aim of assisting a multi-sensor equipped mob...In order to improve a mobile robot＇s autonomy in unknown environments, a novel intelligent controller is designed. The proposed controller is based on fuzzy logic with the aim of assisting a multi-sensor equipped mobile robot to safely navigate in an indoor environment. First, the designs of two behaviors for a robot＇s autonomous navigation are described, including path tracking and obstacle avoidance, which emulate human driving behaviors and reduce the complexity of the robot＇s navigation problems in unknown environments. Secondly, the two behaviors are combined by using a finite state machine （FSM）, which ensures that the robot can safely track a predefined path in an unknown indoor environment. The inputs to this controller are the readings from the sensors. The corresponding output is the desired direction of the robot. Finally, both the simulation and experimental results verify the effectiveness of the proposed method.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
The PBJ- 01 robot is a kind of mobile robot featuring six wheels and two swing arms which can help it to fit many terrains. The robot has a sophisticated sensor system, which includes ultrasonic sensors, tentacle sens...The PBJ- 01 robot is a kind of mobile robot featuring six wheels and two swing arms which can help it to fit many terrains. The robot has a sophisticated sensor system, which includes ultrasonic sensors, tentacle sensors and a vision sensor. The PBJ- 01 adopts behavior-based reactive control architecture in which the key part is an object recognition system based on a fuzzy neural network. Simulation validates that this system can conclude the obstacle type from the sensor data, and help the robot decide whether to negotiate or to avoid obstacles.展开更多
This paper presents the rigorous study of mobile robot navigation techniques used so far.The step by step investigations of classical and reactive approaches are made here to understand the development of path plannin...This paper presents the rigorous study of mobile robot navigation techniques used so far.The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap.The classical approaches such as cell decomposition(CD),roadmap approach(RA),artificial potential field(APF);reactive approaches such as genetic algorithm(GA),fuzzy logic(FL),neural network(NN),firefly algorithm(FA),particle swarm optimization(PSO),ant colony optimization(ACO),bacterial foraging optimization(BFO),artificial bee colony(ABC),cuckoo search(CS),shuffled frog leaping algorithm(SFLA)and other miscellaneous algorithms(OMA)are considered for study.The navigation over static and dynamic condition is analyzed(for single and multiple robot systems)and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches.It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm.Hence,reactive approaches are more popular and widely used for path planning of mobile robot.The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics.展开更多
When the mobile robot performs certain motion tasks in complex environment, wheel slipping inevitably occurs due to the wet or icy road and other reasons, thus directly influences the motion control accuracy. To addre...When the mobile robot performs certain motion tasks in complex environment, wheel slipping inevitably occurs due to the wet or icy road and other reasons, thus directly influences the motion control accuracy. To address unknown wheel longitudinal slipping problem for mobile robot, a RBF neural network approach based on whole model approximation is presented. The real-time data acquisition of inertial measure unit(IMU), encoders and other sensors is employed to get the mobile robot’s position and orientation in the movement, which is applied to compensate the unknown bounds of the longitudinal slipping using the adaptive technique. Both the simulation and experimental results prove that the control scheme possesses good practical performance and realize the motion control with unknown longitudinal slipping.展开更多
基金supported by the National Natural Science Foundation of China(62073113,62003122,62303148)the Fundamental Research Funds for the Central Universities(MCCSE2023A01,JZ2023HGTA0201,JZ2023HGQA0109)the Anhui Provincial Natural Science Foundation(2308085QF204)
文摘Dear Editor,This letter considers the formation control of multiple mobile robot systems(MMRS)that only relies on the local observation information.A new distributed finite-time observer is proposed for MMRS under directed graph to estimate the relative information between each follower robot and the leader robot.Then the formation control problem is transformed into the tracking problem and a finite-time tracking controller is proposed based on the robot model feature.
基金the Technology Project Managed by the State Grid Corporation of China(No.5108-202218280A-2-249-XG)。
文摘Mobile robots represented by smart wheelchairs can assist elderly people with mobility difficulties.This paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior maps.In order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic goals.At last,comparative experiments were carried out in the real environment.Results show that our method is superior in terms of safety,comfort and docking accuracy.
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.
基金Supported by Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot(Grant No.IFCIR2024014)Open Fund Key Laboratory of Machine Vision Inspection of Anhui Provincial,China(Grant No.KLMVI-2024-HIT-14)+2 种基金University Synergy Innovation Program of Anhui Province,China(Grant No.GXXT-2023-076)Anhui Future Technology Research Institute Enterprise Cooperation Project(Grant No.2023qyhz35)2024 Wuhu Science and Technology Planning Project(Grant Nos.2024cj40,2024cxy24).
文摘To effectively improve the adaptability and traversal abilities of a multi-terrain mobile robot under the dynamic excitation of multiple roads,we explore the mobile robot’s vibration and joint driving output stall caused by the dynamic excitation of the road spectrum function and analyze techniques for reducing the vibration and enhancing the driving moment of a four-wheel differential-speed mobile robot.A double-wishbone vibration reduction suspension and a moment compensator were designed for a multi-terrain mobile robot by theoretically describing its suspensionwheel-road dynamics.Also,the mobile robot’s road adaptability and traversal abilities were mathematically characterized under dynamic excitation.Co-simulation in ADAMS-MATLAB/Simulink is performed such as the harsh condition of in situ rotation and outdoor experimental schemes are implemented in which the experimental data are analyzed.The experimental result verifies the correctness of the theoretical analysis,as well as the effectiveness of the vibration reduction suspension and the moment compensator.The compatibility of the mobile robot’s driving mechanisms with road traversal abilities has been improved under various terrain conditions in complex field operation scenarios.
文摘Precise and robust three-dimensional object detection(3DOD)presents a promising opportunity in the field of mobile robot(MR)navigation.Monocular 3DOD techniques typically involve extending existing twodimensional object detection(2DOD)frameworks to predict the three-dimensional bounding box(3DBB)of objects captured in 2D RGB images.However,these methods often require multiple images,making them less feasible for various real-time scenarios.To address these challenges,the emergence of agile convolutional neural networks(CNNs)capable of inferring depth froma single image opens a new avenue for investigation.The paper proposes a novel ELDENet network designed to produce cost-effective 3DBounding Box Estimation(3D-BBE)froma single image.This novel framework comprises the PP-LCNet as the encoder and a fast convolutional decoder.Additionally,this integration includes a Squeeze-Exploit(SE)module utilizing the Math Kernel Library for Deep Neural Networks(MKLDNN)optimizer to enhance convolutional efficiency and streamline model size during effective training.Meanwhile,the proposed multi-scale sub-pixel decoder generates high-quality depth maps while maintaining a compact structure.Furthermore,the generated depthmaps provide a clear perspective with distance details of objects in the environment.These depth insights are combined with 2DOD for precise evaluation of 3D Bounding Boxes(3DBB),facilitating scene understanding and optimal route planning for mobile robots.Based on the estimated object center of the 3DBB,the Deep Reinforcement Learning(DRL)-based obstacle avoidance strategy for MRs is developed.Experimental results demonstrate that our model achieves state-of-the-art performance across three datasets:NYU-V2,KITTI,and Cityscapes.Overall,this framework shows significant potential for adaptation in intelligent mechatronic systems,particularly in developing knowledge-driven systems for mobile robot navigation.
文摘At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.
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 Research and Development Program of China(Grant No.2022YFB4700402).
文摘Existing mobile robots mostly use graph search algorithms for path planning,which suffer from relatively low planning efficiency owing to high redundancy and large computational complexity.Due to the limitations of the neighborhood search strategy,the robots could hardly obtain the most optimal global path.A global path planning algorithm,denoted as EDG*,is proposed by expanding nodes using a well-designed expanding disconnected graph operator(EDG)in this paper.Firstly,all obstacles are marked and their corners are located through the map pre-processing.Then,the EDG operator is designed to find points in non-obstruction areas to complete the rapid expansion of disconnected nodes.Finally,the EDG*heuristic iterative algorithm is proposed.It selects the candidate node through a specific valuation function and realizes the node expansion while avoiding collision with a minimum offset.Path planning experiments were conducted in a typical indoor environment and on the public dataset CSM.The result shows that the proposed EDG*reduced the planning time by more than 90%and total length of paths reduced by more than 4.6%.Compared to A*,Dijkstra and JPS,EDG*does not show an exponential explosion effect in map size.The EDG*showed better performance in terms of path smoothness,and collision avoidance.This shows that the EDG*algorithm proposed in this paper can improve the efficiency of path planning and enhance path quality.
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.
基金the China Scholarship Council(202106690037)the Natural Science Foundation of Anhui Province(19080885QE194)。
文摘The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.
基金supported by the National Natural Science Foundation of China(62003305)the Natural Science Foundation of Zhejiang Province(LQ21F030015)+1 种基金the Key Research and Development Program of Zhejiang Province(2022C03029)the Public Welfare Application Research Project of Huzhou City(2022GZ15).
文摘This paper proposes a novel fixed-time sliding mode control approach for trajectory-tracking tasks of a mecanum-wheeled omnidirectional mobile robot.First,the idea of two-phase attractors is introduced into the domain of sliding mode control,and a new fixed-time sliding surface is proposed.Then,according to this sliding surface,a new type of nonsingular fast terminal sliding mode control algorithm is designed for the omnidirectional mobile robot,which can realize a fast fixed-time convergence property.The stability of the control system is proven scrupulously,and a guideline for control-parameter tuning is expounded.Finally,experiments are implemented to test the trajectory-tracking performance of the robot.Experimental results demonstrate the superiority of the proposed sliding surface and the corresponding control scheme in comparison with benchmark controllers.
基金The authors extend their appreciation to the Deanship of Scientific Research at Shaqra University for funding this research work through the Project Number(SU-ANN-2023016).
文摘Object tracking is one of the major tasks for mobile robots in many real-world applications.Also,artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation.In contrast to previous simulation studies,this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue(RGB)colored objects in a real experimental field.Moreover,a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative(PID)schemes to achieve accurate tracking results,considering robot command delay and tolerance errors.The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators.Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker.Classical PID control failed to handle some tracking scenarios in this study.The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets,while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm,respectively.These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads.
基金National Natural Science Foundation of China(Nos.62173303 and 62273307)Natural Science Foundation of Zhejiang Province(No.LQ24F030023)。
文摘A distributed model predictive control(DMPC)method based on robust control barrier function(RCBF)is developed to achieve the safe formation target of multi-autonomous mobile robot systems in an uncertain disturbed environment.The first step is to analyze the safety requirements of the system during safe formation and categorize them into collision avoidance and distance connectivity maintenance.RCBF constraints are designed based on collision avoidance and connectivity maintenance requirements,and security constraints are achieved through a combination.Then,the specified safety constraints are integrated with the objective of forming a multi-autonomous mobile robot formation.To ensure safe control,the optimization problem is integrated with the DMPC method.Finally,the RCBF-DMPC algorithm is proposed to ensure iterative feasibility and stability while meeting the constraints and expected objectives.Simulation experiments illustrate that the designed algorithm can achieve cooperative formation and ensure system security.
基金the National Natural Science Foundation of China(No.61903291)the Key Research and Development Program of Shaanxi Province(No.2022NY-094)。
文摘Model predictive control(MPC)is a model-based optimal control strategy widely used in robot systems.In this work,the MPC controller tuning problem for the path tracking of the wheeled mobile robot is studied and a novel self-tuning approach is developed.First,two novel path tracking performance indices,i.e.,steadystate time ratio and steady-state distance ratio are proposed to more accurately reflect the control performance.Second,the mapping relationship between the proposed indices and the MPC parameters is established based on machine learning technique,and then a novel controller structure which can automatically tune the control parameters online is further designed.Finally,experimental verification with an actual wheeled mobile robot is conducted,which shows that the proposed method could outperform the existing method via achieving significant improvement in the rapidity,accuracy and adaptability of the robot path tracking.
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.
基金Cultivation Fund for Innovation Project of Ministry of Education (No.708045)
文摘In order to improve a mobile robot＇s autonomy in unknown environments, a novel intelligent controller is designed. The proposed controller is based on fuzzy logic with the aim of assisting a multi-sensor equipped mobile robot to safely navigate in an indoor environment. First, the designs of two behaviors for a robot＇s autonomous navigation are described, including path tracking and obstacle avoidance, which emulate human driving behaviors and reduce the complexity of the robot＇s navigation problems in unknown environments. Secondly, the two behaviors are combined by using a finite state machine （FSM）, which ensures that the robot can safely track a predefined path in an unknown indoor environment. The inputs to this controller are the readings from the sensors. The corresponding output is the desired direction of the robot. Finally, both the simulation and experimental results verify the effectiveness of the proposed method.
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.
文摘The PBJ- 01 robot is a kind of mobile robot featuring six wheels and two swing arms which can help it to fit many terrains. The robot has a sophisticated sensor system, which includes ultrasonic sensors, tentacle sensors and a vision sensor. The PBJ- 01 adopts behavior-based reactive control architecture in which the key part is an object recognition system based on a fuzzy neural network. Simulation validates that this system can conclude the obstacle type from the sensor data, and help the robot decide whether to negotiate or to avoid obstacles.
文摘This paper presents the rigorous study of mobile robot navigation techniques used so far.The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap.The classical approaches such as cell decomposition(CD),roadmap approach(RA),artificial potential field(APF);reactive approaches such as genetic algorithm(GA),fuzzy logic(FL),neural network(NN),firefly algorithm(FA),particle swarm optimization(PSO),ant colony optimization(ACO),bacterial foraging optimization(BFO),artificial bee colony(ABC),cuckoo search(CS),shuffled frog leaping algorithm(SFLA)and other miscellaneous algorithms(OMA)are considered for study.The navigation over static and dynamic condition is analyzed(for single and multiple robot systems)and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches.It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm.Hence,reactive approaches are more popular and widely used for path planning of mobile robot.The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics.
基金Supported by Scientific and Innovation Research Funds for the Beijing University of Posts and Telecommunications(Grant No.2017RC22)
文摘When the mobile robot performs certain motion tasks in complex environment, wheel slipping inevitably occurs due to the wet or icy road and other reasons, thus directly influences the motion control accuracy. To address unknown wheel longitudinal slipping problem for mobile robot, a RBF neural network approach based on whole model approximation is presented. The real-time data acquisition of inertial measure unit(IMU), encoders and other sensors is employed to get the mobile robot’s position and orientation in the movement, which is applied to compensate the unknown bounds of the longitudinal slipping using the adaptive technique. Both the simulation and experimental results prove that the control scheme possesses good practical performance and realize the motion control with unknown longitudinal slipping.
