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
The path planning of autonomous mobile robots(PPoAMR)is a very complex multi-constraint problem.The main goal is to find the shortest collision-free path from the starting point to the target point.By the fact that th...The path planning of autonomous mobile robots(PPoAMR)is a very complex multi-constraint problem.The main goal is to find the shortest collision-free path from the starting point to the target point.By the fact that the PPoAMR problem has the prior knowledge that the straight path between the starting point and the target point is the optimum solution when obstacles are not considered.This paper proposes a new path planning algorithm based on the prior knowledge of PPoAMR,which includes the fitness value calculation method and the prior knowledge particle swarm optimization(PKPSO)algorithm.The new fitness calculation method can preserve the information carried by each individual as much as possible by adding an adaptive coefficient.The PKPSO algorithm modifies the particle velocity update method by adding a prior particle calculated from the prior knowledge of PPoAMR and also implemented an elite retention strategy,which improves the local optima evasion capability.In addition,the quintic polynomial trajectory optimization approach is devised to generate a smooth path.Finally,some experimental comparisons with those state-of-the-arts are carried out to demonstrate the effectiveness of the proposed path planning algorithm.展开更多
Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance.In commercial and domestic constructions,concrete,wood,and glass are typically used.Laser and visual map...Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance.In commercial and domestic constructions,concrete,wood,and glass are typically used.Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls.However,indoor and outdoor glass curtain walls may fail to perceive these transparent materials.In this study,a novel indoor glass recognition and map optimization method based on boundary guidance is proposed.First,the status of glass recognition techniques is analyzed comprehensively.Next,a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed.Finally,map optimization and path-planning tests are conducted and compared using different algorithms.The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls.Using the proposed method,the recognition accuracy of a public test set increases to 94.1%.After adding the planning map,incorrect coverage redundancies for two test scenes reduce by 59.84%and 55.7%.Herein,a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions.展开更多
This paper deals with a new approach based on Q-learning for solving the problem of mobile robot path planning in complex unknown static environments.As a computational approach to learning through interaction with th...This paper deals with a new approach based on Q-learning for solving the problem of mobile robot path planning in complex unknown static environments.As a computational approach to learning through interaction with the environment,reinforcement learning algorithms have been widely used for intelligent robot control,especially in the field of autonomous mobile robots.However,the learning process is slow and cumbersome.For practical applications,rapid rates of convergence are required.Aiming at the problem of slow convergence and long learning time for Q-learning based mobile robot path planning,a state-chain sequential feedback Q-learning algorithm is proposed for quickly searching for the optimal path of mobile robots in complex unknown static environments.The state chain is built during the searching process.After one action is chosen and the reward is received,the Q-values of the state-action pairs on the previously built state chain are sequentially updated with one-step Q-learning.With the increasing number of Q-values updated after one action,the number of actual steps for convergence decreases and thus,the learning time decreases,where a step is a state transition.Extensive simulations validate the efficiency of the newly proposed approach for mobile robot path planning in complex environments.The results show that the new approach has a high convergence speed and that the robot can find the collision-free optimal path in complex unknown static environments with much shorter time,compared with the one-step Q-learning algorithm and the Q(λ)-learning algorithm.展开更多
Given the difficulty in hand coding task schemes, an intellectualized architecture of the autonomous micro mobile robot based behavior for fault repair was presented. Integrating the reinforcement learning and the...Given the difficulty in hand coding task schemes, an intellectualized architecture of the autonomous micro mobile robot based behavior for fault repair was presented. Integrating the reinforcement learning and the group behavior evolution simulating the human's learning and evolution, the autonomous micro mobile robot will automatically generate the suited actions satisfied the environment. However, the designer only devises some basic behaviors, which decreases the workload of the designer and cognitive deficiency of the robot to the environment. The results of simulation have shown that the architecture endows micro robot with the ability of learning, adaptation and robustness, also with the ability of accomplishing the given task.展开更多
In the mobile robotic systems a precise estimate of the robot pose (Cartesian [x y] position plus orientation angle theta) with the intention of the path planning optimization is essential for the correct performance,...In the mobile robotic systems a precise estimate of the robot pose (Cartesian [x y] position plus orientation angle theta) with the intention of the path planning optimization is essential for the correct performance, on the part of the robots, for tasks that are destined to it, especially when intention is for mobile robot autonomous navigation. This work uses a ToF (Time-of-Flight) of the RF digital signal interacting with beacons for computational triangulation in the way to provide a pose estimative at bi-dimensional indoor environment, where GPS system is out of range. It’s a new technology utilization making good use of old ultrasonic ToF methodology that takes advantage of high performance multicore DSP processors to calculate ToF of the order about ns. Sensors data like odometry, compass and the result of triangulation Cartesian estimative, are fused in a Kalman filter in the way to perform optimal estimation and correct robot pose. A mobile robot platform with differential drive and nonholonomic constraints is used as base for state space, plants and measurements models that are used in the simulations and for validation the experiments.展开更多
The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accur...The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.展开更多
Good understanding of relationship between parameters of vehicle, terrain and interaction at the interface is required to develop effective navigation and motion control algorithms for autonomous wheeled mobile robots...Good understanding of relationship between parameters of vehicle, terrain and interaction at the interface is required to develop effective navigation and motion control algorithms for autonomous wheeled mobile robots （AWMR） in rough terrain. A model and analysis of relationship among wheel slippage （S）, rotation angle （0）, sinkage （z） and wheel radius （r） are presented. It is found that wheel rotation angle, sinkage and radius have some influence on wheel slippage. A multi-objective optimization problem with slippage as utility function was formulated and solved in MATLAB. The results reveal the optimal values of wheel-terrain parameters required to achieve optimum slippage on dry sandy terrain. A method of slippage estimation for a five-wheeled mobile robot was presented through comparing the odometric measurements of the powered wheels with those of the fifth non-powered wheel. The experimental result shows that this method is feasible and can be used for online slippage estimation in a sandy terrain.展开更多
Recently there has been great interest in the idea that evolvable system based on the principle of artifcial intelligence can be used to continuously and autonomously adapt the behaviour of physically embedded systems...Recently there has been great interest in the idea that evolvable system based on the principle of artifcial intelligence can be used to continuously and autonomously adapt the behaviour of physically embedded systems such as autonomous mobile robots and intelligent home devices. Meanwhile, we have seen the introduction of evolvable hardware(EHW): new integrated electronic circuits that are able to continuously evolve to adapt the chages in the environment implemented by evolutionary algorithms such as genetic algorithm(GA) and reinforcement learning. This paper concentrates on developing a robotic navigation system whose basic behaviours are obstacle avoidance and light source navigation. The results demonstrate that the intrinsic evolvable hardware system is able to create the stable robotiiuc behaviours as required in the real world instead of the traditional hardware systems.展开更多
Application of terrain-vehicle mechanics for determination and prediction of mobility performance of autonomous wheeled mobile robot （AWMR） in rough terrain is a new research area currently receiving much attention ...Application of terrain-vehicle mechanics for determination and prediction of mobility performance of autonomous wheeled mobile robot （AWMR） in rough terrain is a new research area currently receiving much attention for both terrestrial and planetary missions due to its significant role in design, evaluation, optimization, and motion control of AWMRs. In this paper, decoupled closed form terramechanics considering important wheel-terrain parameters is applied to model and predict traction. Numerical analysis of traction performance in terms of drawbar pull, tractive efficiency, and driving torque is carried out for wheels of different radii, widths, and lug heights, under different wheel slips. Effects of normal forces on wheels are analyzed. Results presented in figures are discussed and used to draw some conclusions. Furthermore, a multiobjective optimization （MOO） method for achieving optimal mobility is presented. The MOO problem is formulated based on five independent variables in- eluding wheel radius r, width b, lug height h, wheel slip s, and wheel rotation angle 0 with three objectives to maximize drawbar pull and tractive efficiency while minimizing the dynamic traction ratio. Genetic algorithm in MATLAB is used to obtain opti- mized wheel design and traction control parameters such as drawbar pull, tractive efficiency, and dynamic traction ratio required for good mobility performance. Comparison of MOO results with experimental results shows a good agreement. A method to apply the MOO results for online traction and mobility prediction and control is discussed.展开更多
In the present work, autonomous mobile robot（AMR） system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, n...In the present work, autonomous mobile robot（AMR） system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, neural network and adaptive neurofuzzy inference system（ANFIS） controller with safe boundary algorithm. In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach. The inputs to the controller are the signals from various sensors fixed at front face, left and right face of the AMR. The output signal from controller regulates the angular velocity of both front power wheels of the AMR. The shortest path is identified using fuzzy, neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation. The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.展开更多
This paper presents a novel vision-based obstacle avoidance approach for the Autonomous Mobile Robot (AMR) with a Pan-Tilt-Zoom (PTZ) camera as its only sensing modality. The approach combines the morphological closin...This paper presents a novel vision-based obstacle avoidance approach for the Autonomous Mobile Robot (AMR) with a Pan-Tilt-Zoom (PTZ) camera as its only sensing modality. The approach combines the morphological closing operation based on Sobel Edge Detection Operation and the (μ-kσ) thresholding technique to detect obstacles to soften the various lighting and ground floor effects. Both the morphology method and thresholding technique are computationally simple. The processing speed of the algorithm is fast enough to avoid some active obstacles. In addition, this approach takes into account the history obstacle effects on the current state. Fuzzy logic is used to control the behaviors of AMR as it navigates in the environment. All behaviors run concurrently and generate motor response solely based on vision perception. A priority based on subsumption coordinator selects the most appropriate response to direct the AMR away from obstacles. Validation of the proposed approach is done on a Pioneer 1 mobile robot.展开更多
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This work was supported by the National Key R&D Funding of China(No.2018YFB1403702)the Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars(No.LR22F030003).
文摘The path planning of autonomous mobile robots(PPoAMR)is a very complex multi-constraint problem.The main goal is to find the shortest collision-free path from the starting point to the target point.By the fact that the PPoAMR problem has the prior knowledge that the straight path between the starting point and the target point is the optimum solution when obstacles are not considered.This paper proposes a new path planning algorithm based on the prior knowledge of PPoAMR,which includes the fitness value calculation method and the prior knowledge particle swarm optimization(PKPSO)algorithm.The new fitness calculation method can preserve the information carried by each individual as much as possible by adding an adaptive coefficient.The PKPSO algorithm modifies the particle velocity update method by adding a prior particle calculated from the prior knowledge of PPoAMR and also implemented an elite retention strategy,which improves the local optima evasion capability.In addition,the quintic polynomial trajectory optimization approach is devised to generate a smooth path.Finally,some experimental comparisons with those state-of-the-arts are carried out to demonstrate the effectiveness of the proposed path planning algorithm.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFB4700400).
文摘Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance.In commercial and domestic constructions,concrete,wood,and glass are typically used.Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls.However,indoor and outdoor glass curtain walls may fail to perceive these transparent materials.In this study,a novel indoor glass recognition and map optimization method based on boundary guidance is proposed.First,the status of glass recognition techniques is analyzed comprehensively.Next,a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed.Finally,map optimization and path-planning tests are conducted and compared using different algorithms.The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls.Using the proposed method,the recognition accuracy of a public test set increases to 94.1%.After adding the planning map,incorrect coverage redundancies for two test scenes reduce by 59.84%and 55.7%.Herein,a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions.
基金Project supported by the National Natural Science Foundation of China (Nos. 61075091,61105100,and 61240052)the Natural Science Foundation of Shandong Province,China (No.ZR2012FM036)the Independent Innovation Foundation of Shandong University,China (Nos. 2011JC011 and 2012JC005)
文摘This paper deals with a new approach based on Q-learning for solving the problem of mobile robot path planning in complex unknown static environments.As a computational approach to learning through interaction with the environment,reinforcement learning algorithms have been widely used for intelligent robot control,especially in the field of autonomous mobile robots.However,the learning process is slow and cumbersome.For practical applications,rapid rates of convergence are required.Aiming at the problem of slow convergence and long learning time for Q-learning based mobile robot path planning,a state-chain sequential feedback Q-learning algorithm is proposed for quickly searching for the optimal path of mobile robots in complex unknown static environments.The state chain is built during the searching process.After one action is chosen and the reward is received,the Q-values of the state-action pairs on the previously built state chain are sequentially updated with one-step Q-learning.With the increasing number of Q-values updated after one action,the number of actual steps for convergence decreases and thus,the learning time decreases,where a step is a state transition.Extensive simulations validate the efficiency of the newly proposed approach for mobile robot path planning in complex environments.The results show that the new approach has a high convergence speed and that the robot can find the collision-free optimal path in complex unknown static environments with much shorter time,compared with the one-step Q-learning algorithm and the Q(λ)-learning algorithm.
文摘Given the difficulty in hand coding task schemes, an intellectualized architecture of the autonomous micro mobile robot based behavior for fault repair was presented. Integrating the reinforcement learning and the group behavior evolution simulating the human's learning and evolution, the autonomous micro mobile robot will automatically generate the suited actions satisfied the environment. However, the designer only devises some basic behaviors, which decreases the workload of the designer and cognitive deficiency of the robot to the environment. The results of simulation have shown that the architecture endows micro robot with the ability of learning, adaptation and robustness, also with the ability of accomplishing the given task.
文摘In the mobile robotic systems a precise estimate of the robot pose (Cartesian [x y] position plus orientation angle theta) with the intention of the path planning optimization is essential for the correct performance, on the part of the robots, for tasks that are destined to it, especially when intention is for mobile robot autonomous navigation. This work uses a ToF (Time-of-Flight) of the RF digital signal interacting with beacons for computational triangulation in the way to provide a pose estimative at bi-dimensional indoor environment, where GPS system is out of range. It’s a new technology utilization making good use of old ultrasonic ToF methodology that takes advantage of high performance multicore DSP processors to calculate ToF of the order about ns. Sensors data like odometry, compass and the result of triangulation Cartesian estimative, are fused in a Kalman filter in the way to perform optimal estimation and correct robot pose. A mobile robot platform with differential drive and nonholonomic constraints is used as base for state space, plants and measurements models that are used in the simulations and for validation the experiments.
文摘The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.
基金Project(60775060) supported by the National Natural Science Foundation of ChinaProject(F200801) supported by the Natural Science Foundation of Heilongjiang Province,China+1 种基金Project(200802171053,20102304110006) supported by the Specialized Research Fund for the Doctoral Program of Higher Education of ChinaProject(2012RFXXG059) supported by Harbin Science and Technology Innovation Talents Special Fund,China
文摘Good understanding of relationship between parameters of vehicle, terrain and interaction at the interface is required to develop effective navigation and motion control algorithms for autonomous wheeled mobile robots （AWMR） in rough terrain. A model and analysis of relationship among wheel slippage （S）, rotation angle （0）, sinkage （z） and wheel radius （r） are presented. It is found that wheel rotation angle, sinkage and radius have some influence on wheel slippage. A multi-objective optimization problem with slippage as utility function was formulated and solved in MATLAB. The results reveal the optimal values of wheel-terrain parameters required to achieve optimum slippage on dry sandy terrain. A method of slippage estimation for a five-wheeled mobile robot was presented through comparing the odometric measurements of the powered wheels with those of the fifth non-powered wheel. The experimental result shows that this method is feasible and can be used for online slippage estimation in a sandy terrain.
文摘Recently there has been great interest in the idea that evolvable system based on the principle of artifcial intelligence can be used to continuously and autonomously adapt the behaviour of physically embedded systems such as autonomous mobile robots and intelligent home devices. Meanwhile, we have seen the introduction of evolvable hardware(EHW): new integrated electronic circuits that are able to continuously evolve to adapt the chages in the environment implemented by evolutionary algorithms such as genetic algorithm(GA) and reinforcement learning. This paper concentrates on developing a robotic navigation system whose basic behaviours are obstacle avoidance and light source navigation. The results demonstrate that the intrinsic evolvable hardware system is able to create the stable robotiiuc behaviours as required in the real world instead of the traditional hardware systems.
基金Project supported by the National Natural Science Foundation of China(No. 60775060)the Natural Science Foundation of Heilongjiang Province of China (No. F200801)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education (Nos. 200802171053 and 20102304110006)the Harbin Science and Technology Innovation Talents Special Fund (No. 2012RFXXG059),China
文摘Application of terrain-vehicle mechanics for determination and prediction of mobility performance of autonomous wheeled mobile robot （AWMR） in rough terrain is a new research area currently receiving much attention for both terrestrial and planetary missions due to its significant role in design, evaluation, optimization, and motion control of AWMRs. In this paper, decoupled closed form terramechanics considering important wheel-terrain parameters is applied to model and predict traction. Numerical analysis of traction performance in terms of drawbar pull, tractive efficiency, and driving torque is carried out for wheels of different radii, widths, and lug heights, under different wheel slips. Effects of normal forces on wheels are analyzed. Results presented in figures are discussed and used to draw some conclusions. Furthermore, a multiobjective optimization （MOO） method for achieving optimal mobility is presented. The MOO problem is formulated based on five independent variables in- eluding wheel radius r, width b, lug height h, wheel slip s, and wheel rotation angle 0 with three objectives to maximize drawbar pull and tractive efficiency while minimizing the dynamic traction ratio. Genetic algorithm in MATLAB is used to obtain opti- mized wheel design and traction control parameters such as drawbar pull, tractive efficiency, and dynamic traction ratio required for good mobility performance. Comparison of MOO results with experimental results shows a good agreement. A method to apply the MOO results for online traction and mobility prediction and control is discussed.
文摘In the present work, autonomous mobile robot（AMR） system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, neural network and adaptive neurofuzzy inference system（ANFIS） controller with safe boundary algorithm. In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach. The inputs to the controller are the signals from various sensors fixed at front face, left and right face of the AMR. The output signal from controller regulates the angular velocity of both front power wheels of the AMR. The shortest path is identified using fuzzy, neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation. The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.
基金TheNationalNaturalSienceFoundationofChina (No .6 2 385 2 )
文摘This paper presents a novel vision-based obstacle avoidance approach for the Autonomous Mobile Robot (AMR) with a Pan-Tilt-Zoom (PTZ) camera as its only sensing modality. The approach combines the morphological closing operation based on Sobel Edge Detection Operation and the (μ-kσ) thresholding technique to detect obstacles to soften the various lighting and ground floor effects. Both the morphology method and thresholding technique are computationally simple. The processing speed of the algorithm is fast enough to avoid some active obstacles. In addition, this approach takes into account the history obstacle effects on the current state. Fuzzy logic is used to control the behaviors of AMR as it navigates in the environment. All behaviors run concurrently and generate motor response solely based on vision perception. A priority based on subsumption coordinator selects the most appropriate response to direct the AMR away from obstacles. Validation of the proposed approach is done on a Pioneer 1 mobile robot.
