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
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 ability of mobile robots to plan and execute a path is foundational to various path-planning challenges,particularly Coverage Path Planning.While this task has been typically tackled with classical algorithms,thes...The ability of mobile robots to plan and execute a path is foundational to various path-planning challenges,particularly Coverage Path Planning.While this task has been typically tackled with classical algorithms,these often struggle with flexibility and adaptability in unknown environments.On the other hand,recent advances in Reinforcement Learning offer promising approaches,yet a significant gap in the literature remains when it comes to generalization over a large number of parameters.This paper presents a unified,generalized framework for coverage path planning that leverages value-based deep reinforcement learning techniques.The novelty of the framework comes from the design of an observation space that accommodates different map sizes,an action masking scheme that guarantees safety and robustness while also serving as a learning-fromdemonstration technique during training,and a unique reward function that yields value functions that are size-invariant.These are coupled with a curriculum learning-based training strategy and parametric environment randomization,enabling the agent to tackle complete or partial coverage path planning with perfect or incomplete knowledge while generalizing to different map sizes,configurations,sensor payloads,and sub-tasks.Our empirical results show that the algorithm can perform zero-shot learning scenarios at a near-optimal level in environments that follow a similar distribution as during training,outperforming a greedy heuristic by sixfold.Furthermore,in out-of-distribution environments,our method surpasses existing state-of-the-art algorithms in most zero-shot and all few-shot scenarios,paving the way for generalizable and adaptable path-planning algorithms.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
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
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
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
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
As one of the core issues of the mobile robot motion control, trajectory tracking has received extensive attention. At present, the solution of the problem only takes kinematic or dynamic model into account separately...As one of the core issues of the mobile robot motion control, trajectory tracking has received extensive attention. At present, the solution of the problem only takes kinematic or dynamic model into account separately, so that the presented strategy is difficult to realize satisfactory tracking quality in practical application. Considering the unknown parameters of two models, this paper presents an adaptive controller for solving the trajectory tracking problem of a mobile robot. Firstly, an adaptive kinematic controller utilized to generate the command of velocity is designed based on Backstepping method. Then, in order to make the real velocity of mobile robot reach the desired velocity asymptotically, a dynamic adaptive controller is proposed adopting reference model and Lyapunov stability theory. Finally, through simulating typical trajectories including circular trajectory, fold line and parabola trajectory in normal and perturbed cases, the results illustrate that the control scheme can solve the tracking problem effectively. The proposed control law, which can tune the kinematic and dynamic model parameters online and overcome external disturbances, provides a novel method for improving trajectory tracking performance of the mobile robot.展开更多
A robust adaptive controller for a nonholonomic mobile robot with unknown kinematic and dynamic parameters is proposed. A kinematic controller whose output is the input of the relevant dynamic controller is provided b...A robust adaptive controller for a nonholonomic mobile robot with unknown kinematic and dynamic parameters is proposed. A kinematic controller whose output is the input of the relevant dynamic controller is provided by using the concept of backstepping. An adaptive algorithm is developed in the kinematic controller to approximate the unknown kinematic parameters, and a simple single-layer neural network is used to express the highly nonlinear robot dynamics in terms of the known and unknown parameters. In order to attenuate the effects of the uncertainties and disturbances on tracking performance, a sliding mode control term is added to the dynamic controller. In the deterministic design of feedback controllers for the uncertain dynamic systems, upper bounds on the norm of the uncertainties are an important clue to guarantee the stability of the closed-loop system. However, sometimes these upper bounds may not be easily obtained because of the complexity of the structure of the uncertainties. Thereby, simple adaptation laws are proposed to approximate upper bounds on the norm of the uncertainties to address this problem. The stability of the proposed control system is shown through the Lyapunov method. Lastly, a design example for a mobile robot with two actuated wheels is provided and the feasibility of the controller is demonstrated by numerical simulations.展开更多
Spherical mobile robot has compact structure, remarkable stability, and flexible motion,which make it have many advantages over traditional mobile robots when applied in those unmanned environments, such as outer plan...Spherical mobile robot has compact structure, remarkable stability, and flexible motion,which make it have many advantages over traditional mobile robots when applied in those unmanned environments, such as outer planets. However, spherical mobile robot is a special highly under-actuated nonholonomic system, which cannot be transformed to the classic chained form. At present, there has not been a kinematics-based trajectory tracking controller which could track both the position states and the attitude states of a spherical mobile robot. In this paper, the four-state(two position states and two attitude states) trajectory tracking control of a type of spherical mobile robot driven by a 2-DOF pendulum was studied. A controller based on the shunting model of neurodynamics and the kinematic model was deduced, and its stability was demonstrated with Lyapunov’s direct method. The control priorities of the four states were allocated according to the magnification of each state tracking error in order to firstly ensure the correct tracking of the position states. The outputs(motor speeds) of the controller were regulated according to the maximum speeds and the maximum accelerations of the actuation motors in order to solve the speed jump problem caused by initial state errors, and continuous and bounded outputs were obtained. The effectiveness including the anti-interference ability of the proposed trajectory tracking controller was verified through MATLAB simulations.展开更多
In this paper,a robust tracking control scheme based on nonlinear disturbance observer is developed for the self-balancing mobile robot with external unknown disturbances.A desired velocity control law is firstly desi...In this paper,a robust tracking control scheme based on nonlinear disturbance observer is developed for the self-balancing mobile robot with external unknown disturbances.A desired velocity control law is firstly designed using the Lyapunov analysis method and the arctan function.To improve the tracking control performance,a nonlinear disturbance observer is developed to estimate the unknown disturbance of the self-balancing mobile robot.Using the output of the designed disturbance observer,the robust tracking control scheme is presented employing the sliding mode method for the selfbalancing mobile robot.Numerical simulation results further demonstrate the effectiveness of the proposed robust tracking control scheme for the self-balancing mobile robot subject to external unknown disturbances.展开更多
In this paper, a fuzzy behavior-based approach for a three wheeled omnidirectional mobile robot(TWOMR) navigation has been proposed. The robot has to track either static or dynamic target while avoiding either static ...In this paper, a fuzzy behavior-based approach for a three wheeled omnidirectional mobile robot(TWOMR) navigation has been proposed. The robot has to track either static or dynamic target while avoiding either static or dynamic obstacles along its path. A simple controller design is adopted, and to do so, two fuzzy behaviors "Track the Target" and "Avoid Obstacles and Wall Following" are considered based on reduced rule bases(six and five rules respectively). This strategy employs a system of five ultrasonic sensors which provide the necessary information about obstacles in the environment. Simulation platform was designed to demonstrate the effectiveness of the proposed approach.展开更多
Pursuit-evasion games involving mobile robots provide an excellent platform to analyze the performance of pursuit and evasion strategies. Pursuit-evasion has received considerable attention from researchers in the pas...Pursuit-evasion games involving mobile robots provide an excellent platform to analyze the performance of pursuit and evasion strategies. Pursuit-evasion has received considerable attention from researchers in the past few decades due to its application to a broad spectrum of problems that arise in various domains such as defense research, robotics, computer games, drug delivery, cell biology, etc. Several methods have been introduced in the literature to compute the winning chances of a single pursuer or single evader in a two-player game. Over the past few decades, proportional navigation guidance (PNG) based methods have proved to be quite effective for the purpose of pursuit especially for missile navigation and target tracking. However, a performance comparison of these pursuer-centric strategies against recent evader-centric schemes has not been found in the literature, for wheeled mobile robot applications. With a view to understanding the performance of each of the evasion strategies against various pursuit strategies and vice versa, four different proportional navigation-based pursuit schemes have been evaluated against five evader-centric schemes and vice-versa for non-holonomic wheeled mobile robots. The pursuer′s strategies include three well-known schemes namely, augmented ideal proportional navigation guidance (AIPNG), modified AIPNG, angular acceleration guidance (AAG), and a recently introduced pursuer-centric scheme called anticipated trajectory-based proportional navigation guidance (ATPNG). Evader-centric schemes are classic evasion, random motion, optical-flow based evasion, Apollonius circle based evasion and another recently introduced evasion strategy called anticipated velocity based evasion. The performance of each of the pursuit methods was evaluated against five different evasion methods through hardware implementation. The performance was analyzed in terms of time of interception and the distance traveled by players. The working environment was obstacle-free and the maximum velocity of the pursuer was taken to be greater than that of the evader to conclude the game in finite time. It is concluded that ATPNG performs better than other PNG-based schemes, and the anticipated velocity based evasion scheme performs better than the other evasion schemes.展开更多
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 by project RELIABLE(PTDC/EEI-AUT/3522/2020)R&D Unit SYSTEC-Base(UIDB001472020)+1 种基金Programmatic(UIDP001472020)funds-and Associate Laboratory Advanced Production and Intelligent Systems ARISE-LAP01122020funded by national funds through the FCT/MCTES(PIDDAC).
文摘The ability of mobile robots to plan and execute a path is foundational to various path-planning challenges,particularly Coverage Path Planning.While this task has been typically tackled with classical algorithms,these often struggle with flexibility and adaptability in unknown environments.On the other hand,recent advances in Reinforcement Learning offer promising approaches,yet a significant gap in the literature remains when it comes to generalization over a large number of parameters.This paper presents a unified,generalized framework for coverage path planning that leverages value-based deep reinforcement learning techniques.The novelty of the framework comes from the design of an observation space that accommodates different map sizes,an action masking scheme that guarantees safety and robustness while also serving as a learning-fromdemonstration technique during training,and a unique reward function that yields value functions that are size-invariant.These are coupled with a curriculum learning-based training strategy and parametric environment randomization,enabling the agent to tackle complete or partial coverage path planning with perfect or incomplete knowledge while generalizing to different map sizes,configurations,sensor payloads,and sub-tasks.Our empirical results show that the algorithm can perform zero-shot learning scenarios at a near-optimal level in environments that follow a similar distribution as during training,outperforming a greedy heuristic by sixfold.Furthermore,in out-of-distribution environments,our method surpasses existing state-of-the-art algorithms in most zero-shot and all few-shot scenarios,paving the way for generalizable and adaptable path-planning algorithms.
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.
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.
基金supported by State Key Laboratory of Robotics and System of China (Grant No. SKLR-2010 -MS - 14)State Key Lab of Embedded System and Service Computing of China(Grant No. 2010-11)
文摘As one of the core issues of the mobile robot motion control, trajectory tracking has received extensive attention. At present, the solution of the problem only takes kinematic or dynamic model into account separately, so that the presented strategy is difficult to realize satisfactory tracking quality in practical application. Considering the unknown parameters of two models, this paper presents an adaptive controller for solving the trajectory tracking problem of a mobile robot. Firstly, an adaptive kinematic controller utilized to generate the command of velocity is designed based on Backstepping method. Then, in order to make the real velocity of mobile robot reach the desired velocity asymptotically, a dynamic adaptive controller is proposed adopting reference model and Lyapunov stability theory. Finally, through simulating typical trajectories including circular trajectory, fold line and parabola trajectory in normal and perturbed cases, the results illustrate that the control scheme can solve the tracking problem effectively. The proposed control law, which can tune the kinematic and dynamic model parameters online and overcome external disturbances, provides a novel method for improving trajectory tracking performance of the mobile robot.
基金partly supported by the National Natural Science Foundation of China (No.50625516)the 863 program of China(No.2006AA09Z203,2006AA04A110)
文摘A robust adaptive controller for a nonholonomic mobile robot with unknown kinematic and dynamic parameters is proposed. A kinematic controller whose output is the input of the relevant dynamic controller is provided by using the concept of backstepping. An adaptive algorithm is developed in the kinematic controller to approximate the unknown kinematic parameters, and a simple single-layer neural network is used to express the highly nonlinear robot dynamics in terms of the known and unknown parameters. In order to attenuate the effects of the uncertainties and disturbances on tracking performance, a sliding mode control term is added to the dynamic controller. In the deterministic design of feedback controllers for the uncertain dynamic systems, upper bounds on the norm of the uncertainties are an important clue to guarantee the stability of the closed-loop system. However, sometimes these upper bounds may not be easily obtained because of the complexity of the structure of the uncertainties. Thereby, simple adaptation laws are proposed to approximate upper bounds on the norm of the uncertainties to address this problem. The stability of the proposed control system is shown through the Lyapunov method. Lastly, a design example for a mobile robot with two actuated wheels is provided and the feasibility of the controller is demonstrated by numerical simulations.
文摘Spherical mobile robot has compact structure, remarkable stability, and flexible motion,which make it have many advantages over traditional mobile robots when applied in those unmanned environments, such as outer planets. However, spherical mobile robot is a special highly under-actuated nonholonomic system, which cannot be transformed to the classic chained form. At present, there has not been a kinematics-based trajectory tracking controller which could track both the position states and the attitude states of a spherical mobile robot. In this paper, the four-state(two position states and two attitude states) trajectory tracking control of a type of spherical mobile robot driven by a 2-DOF pendulum was studied. A controller based on the shunting model of neurodynamics and the kinematic model was deduced, and its stability was demonstrated with Lyapunov’s direct method. The control priorities of the four states were allocated according to the magnification of each state tracking error in order to firstly ensure the correct tracking of the position states. The outputs(motor speeds) of the controller were regulated according to the maximum speeds and the maximum accelerations of the actuation motors in order to solve the speed jump problem caused by initial state errors, and continuous and bounded outputs were obtained. The effectiveness including the anti-interference ability of the proposed trajectory tracking controller was verified through MATLAB simulations.
基金supported by the National Natural Science Foundation of China(61573184)the Specialized Research Fund for the Doctoral Program of Higher Education(20133218110013)+1 种基金the Six Talents Peak Project of Jainism Province(2012-XRAY-010)the Fundamental Research Funds for theCentral Universities(NE2016101)
文摘In this paper,a robust tracking control scheme based on nonlinear disturbance observer is developed for the self-balancing mobile robot with external unknown disturbances.A desired velocity control law is firstly designed using the Lyapunov analysis method and the arctan function.To improve the tracking control performance,a nonlinear disturbance observer is developed to estimate the unknown disturbance of the self-balancing mobile robot.Using the output of the designed disturbance observer,the robust tracking control scheme is presented employing the sliding mode method for the selfbalancing mobile robot.Numerical simulation results further demonstrate the effectiveness of the proposed robust tracking control scheme for the self-balancing mobile robot subject to external unknown disturbances.
文摘In this paper, a fuzzy behavior-based approach for a three wheeled omnidirectional mobile robot(TWOMR) navigation has been proposed. The robot has to track either static or dynamic target while avoiding either static or dynamic obstacles along its path. A simple controller design is adopted, and to do so, two fuzzy behaviors "Track the Target" and "Avoid Obstacles and Wall Following" are considered based on reduced rule bases(six and five rules respectively). This strategy employs a system of five ultrasonic sensors which provide the necessary information about obstacles in the environment. Simulation platform was designed to demonstrate the effectiveness of the proposed approach.
文摘Pursuit-evasion games involving mobile robots provide an excellent platform to analyze the performance of pursuit and evasion strategies. Pursuit-evasion has received considerable attention from researchers in the past few decades due to its application to a broad spectrum of problems that arise in various domains such as defense research, robotics, computer games, drug delivery, cell biology, etc. Several methods have been introduced in the literature to compute the winning chances of a single pursuer or single evader in a two-player game. Over the past few decades, proportional navigation guidance (PNG) based methods have proved to be quite effective for the purpose of pursuit especially for missile navigation and target tracking. However, a performance comparison of these pursuer-centric strategies against recent evader-centric schemes has not been found in the literature, for wheeled mobile robot applications. With a view to understanding the performance of each of the evasion strategies against various pursuit strategies and vice versa, four different proportional navigation-based pursuit schemes have been evaluated against five evader-centric schemes and vice-versa for non-holonomic wheeled mobile robots. The pursuer′s strategies include three well-known schemes namely, augmented ideal proportional navigation guidance (AIPNG), modified AIPNG, angular acceleration guidance (AAG), and a recently introduced pursuer-centric scheme called anticipated trajectory-based proportional navigation guidance (ATPNG). Evader-centric schemes are classic evasion, random motion, optical-flow based evasion, Apollonius circle based evasion and another recently introduced evasion strategy called anticipated velocity based evasion. The performance of each of the pursuit methods was evaluated against five different evasion methods through hardware implementation. The performance was analyzed in terms of time of interception and the distance traveled by players. The working environment was obstacle-free and the maximum velocity of the pursuer was taken to be greater than that of the evader to conclude the game in finite time. It is concluded that ATPNG performs better than other PNG-based schemes, and the anticipated velocity based evasion scheme performs better than the other evasion schemes.
