In recent years,multiple-load automatic guided vehicle(AGV)is increasingly used in the logistics transportation fields,owing to the advantages of smaller fleet size and fewer occurrences of traffic congestion.However,...In recent years,multiple-load automatic guided vehicle(AGV)is increasingly used in the logistics transportation fields,owing to the advantages of smaller fleet size and fewer occurrences of traffic congestion.However,one main challenge lies in the deadlock-avoidance for the dispatching process of a multiple-load AGV system.To prevent the system from falling into a deadlock,a strategy of keeping the number of jobs in the system(NJIS)at a low level is adopted in most existing literatures.It is noteworthy that a low-level NJIS will make the processing machine easier to be starved,thereby reducing the system efficiency unavoidably.The motivation of the paper is to develop a deadlock-avoidance dispatching method for a multiple-load AGV system operating at a high NJIS level.Firstly,the deadlock-avoidance dispatching method is devised by incorporating a deadlock-avoidance strategy into a dispatching procedure that contains four sub-problems.In this strategy,critical tasks are recognized according to the status of workstation buffers,and then temporarily forbidden to avoid potential deadlocks.Secondly,three multiattribute dispatching rules are designed for system efficiency,where both the traveling distance and the buffer status are taken into account.Finally,a simulation system is developed to evaluate the performance of the proposed deadlock-avoidance strategy and dispatching rules at different NJIS levels.The experimental results demonstrate that our deadlock-avoidance dispatching method can improve the system efficiency at a high NJIS level and the adaptability to various system settings,while still avoiding potential deadlocks.展开更多
Multiple Automatic Guided Vehicle(multi-AGVs)management systems provide an effective solution to ensuring stable operations of multi-AGVs in the same scenario,such as flexible manufacturing systems,warehouses,containe...Multiple Automatic Guided Vehicle(multi-AGVs)management systems provide an effective solution to ensuring stable operations of multi-AGVs in the same scenario,such as flexible manufacturing systems,warehouses,container terminals,etc.This type of systems need to balance the relationship among the resources of the system and solve the problems existing in the operation to make the system in line with the requirement of the administrator.The multi-AGVs management problem is a multi-objective,multi-constraint combinatorial optimization problem,which depends on the types of application scenarios.This article classifies and compares the research papers on multi-AGVs management in detail.Firstly,according to the different dimensions of the problem,the multi-AGVs management system is analyzed from three perspectives,namely,1)task dimensiondispatch,2)spatial dimension-path,and 3)time dimension-scheduling.The detailed comparison between the three dimensions and their respective solutions are discussed in detail as well.Secondly,according to their utility,the multi-AGVs management problems are divided into three categories:1)cost reduction,resource-oriented,2)efficiency improvement,problem-oriented,3)personalized demand,goal-oriented.The algorithm and methods of the different utility-oriented are analyzed and discussed.The related literature is summarized and corresponds to the composition of the multi-AGVs management system and the multi-AGVs management problems.Finally,according to the literature review,suggestions are made for further research.展开更多
Intermediate charging and sudden failure of automatic guided vehicles(AGVs)interrupt and severely affect the stability and efficiency of scheduling.Therefore,an AGV scheduling approach considering equipment failure an...Intermediate charging and sudden failure of automatic guided vehicles(AGVs)interrupt and severely affect the stability and efficiency of scheduling.Therefore,an AGV scheduling approach considering equipment failure and power management is proposed for outfitting warehouses.First,a power consumption model is established for AGVs performing transportation tasks.The powers for departure and task consumption are used to calculate the AGV charging and return times.Second,an optimization model for AGV scheduling is established to minimize the total transportation time.Different conditions are defined for the overhaul and minor repair of AGVs,and a scheduling strategy for responding to sudden failure is proposed.Finally,an algorithm is developed to solve the optimization model for a case study.The method can be used to plan the charging time and perform rescheduling under sudden failure to improve the robustness and dynamic response capability of AGVs.展开更多
With the booming development of logistics,manufacturing and warehousing fields,the autonomous navigation and intelligent obstacle avoidance technology of automated guided vehicles(AGVs)has become the focus of scientif...With the booming development of logistics,manufacturing and warehousing fields,the autonomous navigation and intelligent obstacle avoidance technology of automated guided vehicles(AGVs)has become the focus of scientific research.In this paper,an enhanced deep reinforcement learning(DRL)framework is proposed,aiming to empower AGVs with the ability of autonomous navigation and obstacle avoidance in the unknown and variable complex environment.To address the problems of time-consuming training and limited generalisation ability of traditional DRL,we refine the twin delayed deep deterministic policy gradient algorithm by integrating adaptive noise attenuation and dynamic delayed updating,optimising both training efficiency and model robustness.In order to further strengthen the AGV's ability to perceive and respond to changes of a dynamic environment,we introduce a distance-based obstacle penalty term in the designed composite reward function,which ensures that the AGV is capable of predicting and avoiding obstacles effectively in dynamic scenarios.Experiments indicate that the AGV model trained by this algorithm presents excellent autonomous navigation capability in both static and dynamic environments,with a high task completion rate,stable and reliable operation,which fully proves the high efficiency and robustness of this method and its practical value.展开更多
Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and flexibility.This paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufact...Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and flexibility.This paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufacturing workshops with undirected material flow,aiming to minimize both total task delay time and total task completion time.To address this LAGVSP,a mixed-integer linear programming model is built,and a nondominated sorting genetic algorithm II based on dual population co-evolution(NSGA-IIDPC)is proposed.In NSGA-IIDPC,a single population is divided into a common population and an elite population,and they adopt different evolutionary strategies during the evolution process.The dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two populations.In addition,to enhance the quality of initial population,a minimum cost function strategy based on load balancing is adopted.Multiple local search operators based on ideal point are proposed to find a better local solution.To improve the global exploration ability of the algorithm,a dual population restart mechanism is adopted.Experimental tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.展开更多
The advancements in intelligent manufacturing have made high-precision trajectory tracking technology crucial for improving the efficiency and safety of in-factory cargo transportation.This study addresses the limitat...The advancements in intelligent manufacturing have made high-precision trajectory tracking technology crucial for improving the efficiency and safety of in-factory cargo transportation.This study addresses the limitations of current forklift navigation systems in trajectory control accuracy and stability by proposing the Enhanced Stability and Safety Model Predictive Control(ESS-MPC)method.This approach includes a multi-constraint strategy for improved stability and safety.The kinematic model for a single front steeringwheel forklift vehicle is constructed with all known state quantities,including the steering angle,resulting in a more accurate model description and trajectory prediction.To ensure vehicle safety,the spatial safety boundary obtained from the trajectory planning module is established as a hard constraint for ESS-MPC tracking.The optimisation constraints are also updated with the key kinematic and dynamic parameters of the forklift.The ESSMPC method improved the position and pose accuracy and stability by 57.93%,37.83%,and 57.51%,respectively,as demonstrated through experimental validation using simulation and real-world environments.This study provides significant support for the development of autonomous navigation systems for industrial forklifts.展开更多
An artificial potential field method based on global path guidance(G-APF)is proposed for target unreachability and local minima problems of the conventional artificial potential field(APF)method in complex environment...An artificial potential field method based on global path guidance(G-APF)is proposed for target unreachability and local minima problems of the conventional artificial potential field(APF)method in complex environments with dynamic obstacles.First,for the target unreachability problem,the global path attraction is added to the APF;second,an obstacle detection optimisation method is proposed and the optimal virtual target point is selected by setting the evaluation function to improve the local minima problem;finally,based on the obstacle detection optimisation method,the gravitational and repulsive processes are improved so that the path can pass through the narrow channel smoothly and remain collision-free.Experiments show that the method optimises 40.8%of the total path corners,reduces 81.8%of the number of path oscillations,and shortens 4.3%of the path length in Map 1.It can be applied to the vehicle obstacle avoidance path planning problem in complex environments with dynamic obstacles.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52005427,61973154)the National Defense Basic Scientific Research Program of China(No.JCKY2018605C004)+1 种基金the Natural Science Research Project of Jiangsu Higher Education Institutions(Nos.19KJB510013,18KJA460009)the Foundation of Graduate Innovation Center in Nanjing University of Aeronautics and Astronautics(No.KFJJ20190516)。
文摘In recent years,multiple-load automatic guided vehicle(AGV)is increasingly used in the logistics transportation fields,owing to the advantages of smaller fleet size and fewer occurrences of traffic congestion.However,one main challenge lies in the deadlock-avoidance for the dispatching process of a multiple-load AGV system.To prevent the system from falling into a deadlock,a strategy of keeping the number of jobs in the system(NJIS)at a low level is adopted in most existing literatures.It is noteworthy that a low-level NJIS will make the processing machine easier to be starved,thereby reducing the system efficiency unavoidably.The motivation of the paper is to develop a deadlock-avoidance dispatching method for a multiple-load AGV system operating at a high NJIS level.Firstly,the deadlock-avoidance dispatching method is devised by incorporating a deadlock-avoidance strategy into a dispatching procedure that contains four sub-problems.In this strategy,critical tasks are recognized according to the status of workstation buffers,and then temporarily forbidden to avoid potential deadlocks.Secondly,three multiattribute dispatching rules are designed for system efficiency,where both the traveling distance and the buffer status are taken into account.Finally,a simulation system is developed to evaluate the performance of the proposed deadlock-avoidance strategy and dispatching rules at different NJIS levels.The experimental results demonstrate that our deadlock-avoidance dispatching method can improve the system efficiency at a high NJIS level and the adaptability to various system settings,while still avoiding potential deadlocks.
基金funded by the National Key Research and Development Program of China(No.2019YFB1310003)by the National Natural Science Foundation of China(Nos.U1913603,61803251 and 51775322)funded by the Shanghai Collaborative Innovation Center of Intelligent Manufacturing Robot Technology for Large Components(No.ZXZ20211101).
文摘Multiple Automatic Guided Vehicle(multi-AGVs)management systems provide an effective solution to ensuring stable operations of multi-AGVs in the same scenario,such as flexible manufacturing systems,warehouses,container terminals,etc.This type of systems need to balance the relationship among the resources of the system and solve the problems existing in the operation to make the system in line with the requirement of the administrator.The multi-AGVs management problem is a multi-objective,multi-constraint combinatorial optimization problem,which depends on the types of application scenarios.This article classifies and compares the research papers on multi-AGVs management in detail.Firstly,according to the different dimensions of the problem,the multi-AGVs management system is analyzed from three perspectives,namely,1)task dimensiondispatch,2)spatial dimension-path,and 3)time dimension-scheduling.The detailed comparison between the three dimensions and their respective solutions are discussed in detail as well.Secondly,according to their utility,the multi-AGVs management problems are divided into three categories:1)cost reduction,resource-oriented,2)efficiency improvement,problem-oriented,3)personalized demand,goal-oriented.The algorithm and methods of the different utility-oriented are analyzed and discussed.The related literature is summarized and corresponds to the composition of the multi-AGVs management system and the multi-AGVs management problems.Finally,according to the literature review,suggestions are made for further research.
基金Supported by the China High-Tech Ship Project of the Ministry of Industry and Information Technology under Grant No.[2019]360.
文摘Intermediate charging and sudden failure of automatic guided vehicles(AGVs)interrupt and severely affect the stability and efficiency of scheduling.Therefore,an AGV scheduling approach considering equipment failure and power management is proposed for outfitting warehouses.First,a power consumption model is established for AGVs performing transportation tasks.The powers for departure and task consumption are used to calculate the AGV charging and return times.Second,an optimization model for AGV scheduling is established to minimize the total transportation time.Different conditions are defined for the overhaul and minor repair of AGVs,and a scheduling strategy for responding to sudden failure is proposed.Finally,an algorithm is developed to solve the optimization model for a case study.The method can be used to plan the charging time and perform rescheduling under sudden failure to improve the robustness and dynamic response capability of AGVs.
基金supported by the Guang-dong Basic and Applied Basic Research Foundation(2024A1515011768)the National Natural Science Foundation of China(62073088)+1 种基金Open Research Project of the State Key Laboratory of Industrial Control Technology(ICT2024B43)Zhejiang University.
文摘With the booming development of logistics,manufacturing and warehousing fields,the autonomous navigation and intelligent obstacle avoidance technology of automated guided vehicles(AGVs)has become the focus of scientific research.In this paper,an enhanced deep reinforcement learning(DRL)framework is proposed,aiming to empower AGVs with the ability of autonomous navigation and obstacle avoidance in the unknown and variable complex environment.To address the problems of time-consuming training and limited generalisation ability of traditional DRL,we refine the twin delayed deep deterministic policy gradient algorithm by integrating adaptive noise attenuation and dynamic delayed updating,optimising both training efficiency and model robustness.In order to further strengthen the AGV's ability to perceive and respond to changes of a dynamic environment,we introduce a distance-based obstacle penalty term in the designed composite reward function,which ensures that the AGV is capable of predicting and avoiding obstacles effectively in dynamic scenarios.Experiments indicate that the AGV model trained by this algorithm presents excellent autonomous navigation capability in both static and dynamic environments,with a high task completion rate,stable and reliable operation,which fully proves the high efficiency and robustness of this method and its practical value.
基金supported by the National Natural Science Foundation of China(No.62076095)National Key Research and Development Program(No.2022YFB4602104).
文摘Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and flexibility.This paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufacturing workshops with undirected material flow,aiming to minimize both total task delay time and total task completion time.To address this LAGVSP,a mixed-integer linear programming model is built,and a nondominated sorting genetic algorithm II based on dual population co-evolution(NSGA-IIDPC)is proposed.In NSGA-IIDPC,a single population is divided into a common population and an elite population,and they adopt different evolutionary strategies during the evolution process.The dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two populations.In addition,to enhance the quality of initial population,a minimum cost function strategy based on load balancing is adopted.Multiple local search operators based on ideal point are proposed to find a better local solution.To improve the global exploration ability of the algorithm,a dual population restart mechanism is adopted.Experimental tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.
基金Natural Science Foundation of Shenyang Municipality,Grant/Award Number:22-315-6‐-02111 Project,Grant/Award Number:D23005。
文摘The advancements in intelligent manufacturing have made high-precision trajectory tracking technology crucial for improving the efficiency and safety of in-factory cargo transportation.This study addresses the limitations of current forklift navigation systems in trajectory control accuracy and stability by proposing the Enhanced Stability and Safety Model Predictive Control(ESS-MPC)method.This approach includes a multi-constraint strategy for improved stability and safety.The kinematic model for a single front steeringwheel forklift vehicle is constructed with all known state quantities,including the steering angle,resulting in a more accurate model description and trajectory prediction.To ensure vehicle safety,the spatial safety boundary obtained from the trajectory planning module is established as a hard constraint for ESS-MPC tracking.The optimisation constraints are also updated with the key kinematic and dynamic parameters of the forklift.The ESSMPC method improved the position and pose accuracy and stability by 57.93%,37.83%,and 57.51%,respectively,as demonstrated through experimental validation using simulation and real-world environments.This study provides significant support for the development of autonomous navigation systems for industrial forklifts.
基金supported by the Zhejiang Province New Young Talent Plan Project in 2022 under Grant No.2022R431B021。
文摘An artificial potential field method based on global path guidance(G-APF)is proposed for target unreachability and local minima problems of the conventional artificial potential field(APF)method in complex environments with dynamic obstacles.First,for the target unreachability problem,the global path attraction is added to the APF;second,an obstacle detection optimisation method is proposed and the optimal virtual target point is selected by setting the evaluation function to improve the local minima problem;finally,based on the obstacle detection optimisation method,the gravitational and repulsive processes are improved so that the path can pass through the narrow channel smoothly and remain collision-free.Experiments show that the method optimises 40.8%of the total path corners,reduces 81.8%of the number of path oscillations,and shortens 4.3%of the path length in Map 1.It can be applied to the vehicle obstacle avoidance path planning problem in complex environments with dynamic obstacles.
