Automated Guided Vehicles Research Articles

Fixed-time adaptive sliding mode-based active rollover prevention control of automated guided vehicles via improved super-twisting observer

As an important component of automated guided vehicle (AGV) active safety systems, the active rollover prevention (ARP) control has been widely studied. In this paper, a fixed-time adaptive sliding mode (FTASM) control strategy based on nested adaptive law (NAL) and improved super-twisting observer (ISTO) is developed for the AGV. Firstly, the lateral and roll motion dynamics of AGV are established, and the ARP problem is transformed into the yaw stability control with constraints. Subsequently, a faster fixed-time stable system-based FTASM composite controller is proposed to guarantee a fixed and faster convergence time of the yaw rate tracking error, such that the rollover stability will reach a region of permissible risk. The adoption of NAL enables the adaptive controller to automatically search the minimum switching gain satisfying the reaching condition of sliding mode, thus the conservative assumption constraint of the disturbance upper bound is released. In addition, an ISTO is developed to estimate the unknown disturbance to further improve the control robustness and attenuate the control chattering. The closed-loop stability analysis is rigorously given by Lyapunov theory. Finally, the performance of the proposed control strategy is validated by hardware-in-loop simulations with Adams and Matlab software and an actual steer-by-wire plant.

Dynamic Integrated Scheduling of Production Equipment and Automated Guided Vehicles in a Flexible Job Shop Based on Deep Reinforcement Learning

The high-quality development of the manufacturing industry necessitates accelerating its transformation towards high-end, intelligent, and green development. Considering logistics resource constraints, the impact of dynamic disturbance events on production, and the need for energy-efficient production, the integrated scheduling of production equipment and automated guided vehicles (AGVs) in a flexible job shop environment is investigated in this study. Firstly, a static model for the integrated scheduling of production equipment and AGVs (ISPEA) is developed based on mixed-integer programming, which aims to optimize the maximum completion time and total production energy consumption (EC). In recent years, reinforcement learning, including deep reinforcement learning (DRL), has demonstrated significant advantages in handling workshop scheduling issues with sequential decision-making characteristics, which can fully utilize the vast quantity of historical data accumulated in the workshop and adjust production plans in a timely manner based on changes in production conditions and demand. Accordingly, a DRL-based approach is introduced to address the common production disturbances in emergency order insertions. Combined with the characteristics of the ISPEA problem and an event-driven strategy for handling dynamic events, four types of agents, namely workpiece selection, machine selection, AGV selection, and target selection agents, are set up, which refine workshop production status characteristics as observation inputs and generate rules for selecting workpieces, machines, AGVs, and targets. These agents are trained offline using the QMIX multi-agent reinforcement learning framework, and the trained agents are utilized to solve the dynamic ISPEA problem. Finally, the effectiveness of the proposed model and method is validated through a comparison of the solution performance with other typical optimization algorithms for various cases.

Integration of an IoT sensor with angle-of-arrival-based angle measurement in AGV navigation: A reliability study

Automated guided vehicle (AGV), which was initially designed for indoor operations in industry, has been increasingly applied in outdoor heavy-duty logistics tasks. In typical navigation tasks, such as the autonomous tracking of a designated object or a person, relative angle and relative distance between AGV and the target is required. To obtain the necessary information, various on-board sensors are extensively integrated. In this paper, the reliability of measuring the relative angle with the Angle-of-Arrival (AoA) method with two different Internet of Things (IoT) sensor sets from Texas Instrument (TI) and u-blox, according to Bluetooth 5.1 was investigated. The performance of IoT sensors was validated with angle accuracy parameters and received signal strength indicator (RSSI). The better IoT sensor was then integrated into the AGV navigation system, and the information gathered from IoT sensor enabled the AGV to turn toward the direction of the target. The process of AGV turning to the targeted direction based on IoT sensor information was respectively tested in the simulation and actual environment and evaluated by the disparity between the real relative angle and the rotation angle of the AGV. The results showed that this disparity was within ±5° in both simulated and actual environments, and methods for higher accuracy were proposed. In this way, the reliability and performance of Angle of Arrival (AoA) sensors in measuring the relative angle, which remains unexplored by other researchers, was systematically assessed contributing to extending the usability of AoA sensors in complex, real-world applications.

Integrated Scheduling of Handling Equipment in Automated Container Terminal Considering Quay Crane Faults

Quay cranes (QCs) play a vital role in automated container terminals (ACTs), and once a QC malfunctions, it will seriously affect the operation efficiency of ships being loaded and unloaded by the QC. In this study, we investigate an integrated scheduling problem of quay cranes (QCs), yard cranes (YCs), and automated guided vehicles (AGVs) under QC faults, which is aimed at minimizing the loading and unloading time by determining the range of adjacent operational QCs of the faulty QCs and reallocating unfinished container handling tasks of QCs. A mixed integer programming model is formulated to dispatch QCs, YCs, and AGVs in ACTs. To solve the model, an adaptive two-stage NSGA-II algorithm is proposed. Numerical experiments show that the proposed algorithm can significantly reduce the impact of faulty QCs on productivity while maintaining its synchronous loading and unloading efficiency. The sensitivity analysis of ship scale, location, and number of faulty QCs indicates that the number of faulty QCs has a greater influence on the loading and unloading efficiency than their locations, and the impact of faulty QCs on the efficiency of small-scale ships is greater than that of large-scale ships.

Multiobjective design of automated order fulfillment systems: A case study for an existing surgical complex

We present a case study that introduces a systematic framework for designing an automated guided vehicle (AGV) order fulfillment system. The multi-stage framework integrated discrete event simulation (DES), super-efficiency data envelopment analysis (S-DEA) model, and Design of Experiments (DOE) methodologies for the design of an AGV system in an existing large-scale surgical complex within a comprehensive hospital in Singapore. Performance metrics derived from this model were consolidated into a single relative efficiency score using the S-DEA model. This efficiency score was then utilized in a DOE methodology, combined with a post-hoc model selection process, to robustly identify appropriate models for evaluating alternative system designs. The proposed framework optimizes AGV deployment by first identifying opportunities for AGVs to add value within the surgical system through a value-stream mapping approach. It then derives the optimal system design by balancing cost efficiency, manpower reallocation, and order fulfillment rates, facilitated by the S-DEA model augmented with the DOE approach and post-hoc model selection. Application of this framework to the surgical complex revealed several insights that can significantly enhance AGV efficiency prior to actual deployment. However, a key limitation of this case-study-based approach is its limited generalizability across different systems. Despite this, the methodology provides a robust foundation for AGV system design in similar healthcare environments.

CRTF-MoeICP: A robust coarse-to-fine reflector-based LiDAR indoor positioning algorithm

The reflector-based Light Detection and Ranging (LiDAR) positioning method is susceptible to environmental interferences, resulting in instability. This instability not only reduces movement accuracy but also poses safety hazards. To solve the above problems in the application of LiDAR sensors in the field of indoor positioning, we propose a Coarse Registration algorithm based on the Triangular Feature (CRTF) and a fine registration algorithm based on Multi-level outlier elimination and Iterative Closest Point (MoeICP) for the reflector-based LiDAR positioning. The proposed coarse-to-fine positioning algorithm CRTF-MoeICP addresses the issue of reflector-based LiDAR positioning failure arising from the improper selection of the initial transformation matrix and outlier interference in indoor structured industrial environments. The experiment results show that the CRTF-MoeICP algorithm can ensure the stable registration of the LiDAR point cloud and the reflector map by completely removing all outliers, greatly improving the indoor positioning stability of LiDAR sensors. Besides, the proposed algorithm can be realized by LiDARs with different performance, and improve the static positioning repeatability to ±3 mm. The high precision and stable positioning results improve the motion accuracy, ensuring that the Automatic Guided Vehicle (AGV) can accurately and stably complete the handling task.

Research on AGV Path Planning Based on Improved Directed Weighted Graph Theory and ROS Fusion

This article addresses the common issues of insufficient computing power and path congestion for automated guided vehicles (AGVs) in real-world production environments, as well as the shortcomings of traditional path-planning algorithms that mainly consider the shortest path while ignoring vehicle turning time and stability. We propose a secondary path-planning method based on an improved directed weighted graph theory integrated with an ROS. Firstly, the production environment is modeled in detail to identify the initial position of the AGV. Secondly, the operational area is systematically divided, key nodes are selected and optimized, and a directed weighted graph is constructed with optimized weights. It is integrated with the ROS for path planning, using the Floyd algorithm to find the optimal path. The effectiveness and superiority of this method have been demonstrated through simulation verification and actual AGV operation testing. The path planning strategy and fusion algorithm proposed in this article that comprehensively considers distance and angle steering are simple and practical, effectively reducing production costs for enterprises. This method is suitable for logistics sorting and small transport AGVs with a shorter overall path-planning time, higher stability, and limited computing power, and it has reference significance and practical value.

Trajectory Tracking Control of Unmanned Vehicles via Front-Wheel Driving

Automated Guided Vehicles (AGVs) are the fastest commercially available application of unmanned driving technology, and the research significance of unmanned vehicle technology remains substantial. This paper investigates the driving mode of AGVs and proposes a method to extend the kinematic model of center-driven unmanned vehicles to front-wheel drive. This change in driving force enables unmanned vehicles to achieve faster tracking and higher consistency, solving the problems of long tracking time and insufficient accuracy in complex environments and reducing production costs. By analyzing the posture relationship of the unmanned vehicle system during movement, we established a posture error system to analyze the trajectory tracking problem. Utilizing Lyapunov stability theory and the concept of backstepping, we designed a control scheme that uses linear velocity and heading angular velocity as variables for the posture error system. This control scheme aims to stabilize the system and achieve synchronized trajectory tracking control of the unmanned vehicle. The impact of control parameters in the controller on tracking performance is also discussed. The final experimental simulation results show that the system error stabilizes, and the unmanned vehicle accurately follows the predetermined trajectory, verifying the feasibility of our proposed method and control scheme.

AGV Scheduling for Optimizing Irregular Air Cargo Containers Handling at Airport Transshipment Centers

Airport transshipment centers play a pivotal role in global logistics networks, enabling the swift and efficient transfer of cargo, which is essential for maintaining supply-chain continuity and reducing delivery times. The handling of irregularly shaped air cargo containers presents new constraints for automated guided vehicles (AGVs), as these shapes can complicate loading and unloading processes, directly impacting overall operational efficiency, turnaround times, and the reliability of cargo handling. This study focuses on optimizing the scheduling of AGVs to enhance cargo-handling efficiency at these hubs, particularly for managing irregular air cargo containers. A mixed-integer linear programming (MILP) model is developed, validated for feasibility with the Gurobi solver, and designed to handle large-scale operations. It incorporates a novel approach by integrating a simulated annealing optimized genetic algorithm (GA). The experimental results demonstrate that the designed algorithm can solve models of considerable size within 8 s, offering superior time efficiency compared to the solver, and an average solution quality improvement of 12.62% over the genetic algorithm, significantly enhancing both the model’s efficiency and scalability. The enhanced AGV scheduling not only boosts operational efficiency but also ensures better integration within the global logistics framework. This research provides a robust foundation for future advancements in logistics technology, offering both theoretical and practical insights into optimizing complex transportation networks.

Research on Automatic Recharging Technology for Automated Guided Vehicles Based on Multi-Sensor Fusion

Automated guided vehicles (AGVs) play a critical role in indoor environments, where battery endurance and reliable recharging are essential. This study proposes a multi-sensor fusion approach that integrates LiDAR, depth cameras, and infrared sensors to address challenges in autonomous navigation and automatic recharging. The proposed system overcomes the limitations of LiDAR’s blind spots in near-field detection and the restricted range of vision-based navigation. By combining LiDAR for precise long-distance measurements, depth cameras for enhanced close-range visual positioning, and infrared sensors for accurate docking, the AGV’s ability to locate and autonomously connect to charging stations is significantly improved. Experimental results show a 25% increase in docking success rate (from 70% with LiDAR-only to 95%) and a 70% decrease in docking error (from 10 cm to 3 cm). These improvements demonstrate the effectiveness of the proposed sensor fusion method, ensuring more reliable, efficient, and precise operations for AGVs in complex indoor environments.

Location Privacy Protection for the Internet of Things with Edge Computing Based on Clustering K-Anonymity.

With the development of the Internet of Things (IoT) and edge computing, more and more devices, such as sensor nodes and intelligent automated guided vehicles (AGVs), can serve as edge devices to provide Location-Based Services (LBS) through the IoT. As the number of applications increases, there is an abundance of sensitive information in the communication process, pushing the focus of privacy protection towards the communication process and edge devices. The challenge lies in the fact that most traditional location privacy protection algorithms are not suited for the IoT with edge computing, as they primarily focus on the security of remote servers. To enhance the capability of location privacy protection, this paper proposes a novel K-anonymity algorithm based on clustering. This novel algorithm incorporates a scheme that flexibly combines real and virtual locations based on the requirements of applications. Simulation results demonstrate that the proposed algorithm significantly improves location privacy protection for the IoT with edge computing. When compared to traditional K-anonymity algorithms, the proposed algorithm further enhances the security of location privacy by expanding the potential region in which the real node may be located, thereby limiting the effectiveness of "narrow-region" attacks.

Hybrid quantum-classical computation for automatic guided vehicles scheduling

Motivated by recent efforts to develop quantum computing for practical, industrial-scale challenges, we demonstrate the effectiveness of state-of-the-art hybrid (not necessarily quantum) solvers in addressing the business-centric optimization problem of scheduling Automatic Guided Vehicles (AGVs). Some solvers can already leverage noisy intermediate-scale quantum (NISQ) devices. In our study, we utilize D-Wave hybrid solvers that implement classical heuristics with potential assistance from a quantum processing unit. This hybrid methodology performs comparably to existing classical solvers. However, due to the proprietary nature of the software, the precise contribution of quantum computation remains unclear. Our analysis focuses on a practical, business-oriented scenario: scheduling AGVs within a factory constrained by limited space, simulating a realistic production setting. Our approach maps a realistic AGVs problem onto one reminiscent of railway scheduling and demonstrates that the AGVs problem is better suited to quantum computing than its railway counterpart, the latter being denser in terms of the average number of constraints per variable. The main idea here is to highlight the potential usefulness of a hybrid approach for handling AGVs scheduling problems of practical sizes. We show that a scenario involving up to 21 AGVs, significant due to possible deadlocks, can be efficiently addressed by a hybrid solver in seconds.

Port environmental path planning based on key obstacles

This paper proposes an improved hybrid algorithm for automated guided vehicles (AGVs) in port environments based on the concept of key obstacles for the JPS and DWA algorithms. Given the complexity of the port environment and the abundance of obstacles, the traditional heuristic function of the JPS algorithm is improved by adding the key obstacle heuristic function. Simultaneously, improvements are made to the evaluation function of the traditional DWA algorithm, where the braking distance is segmented into key obstacle distance and non-key obstacle distance, utilizing the concept of key obstacles. Simulation experiments are conducted using Matlab to demonstrate the effectiveness of the improved algorithm. Moreover, the performance of the hybrid algorithm is compared with five mainstream algorithms in a real simulated port environment, and the final results show the significant enhancement of this paper’s algorithm in several key performance metrics. Thus, this study provides a feasible strategy for improved path planning efficiency for AGV in the port environment.

Hybrid Optimization Path Planning Method for AGV Based on KGWO.

To address the path planning problem for automated guided vehicles (AGVs) in challenging and complex industrial environments, a hybrid optimization approach is proposed, integrating a Kalman filter with grey wolf optimization (GWO), as well as incorporating partially matched crossover (PMX) mutation operations and roulette wheel selection. Paths are first optimized using GWO, then refined with Kalman filter corrections every ten iterations. Moreover, roulette wheel selection guides robust parent path selection, while an elite strategy and partially matched crossover (PMX) with mutation generate diverse offspring. Extensive simulations and experiments were carried out under a densely packed goods scenario and complex indoor layout scenario, within a fully automated warehouse environment. The results showed that this hybrid method not only enhanced the various optimization metrics but also ensured more predictable and collision-free navigation paths, particularly in environments with complex obstacles. These improvements lead to increased operational efficiency and safety, highlighting the method's potential in real-world applications.

Dynamic Scheduling Optimization of Automatic Guide Vehicle for Terminal Delivery under Uncertain Conditions

As an important part of urban terminal delivery, automated guided vehicles (AGVs) have been widely used in the field of takeout delivery. Due to the real-time generation of takeout orders, the delivery system is required to be extremely dynamic, so the AGV needs to be dynamically scheduled. At the same time, the uncertainty in the delivery process (such as the meal preparation time) further increases the complexity and difficulty of AGV scheduling. Considering the influence of these two factors, the method of embedding a stochastic programming model into a rolling mechanism is adopted to optimize the AGV delivery routing. Specifically, to handle real-time orders under dynamic demand, an optimization mechanism based on a rolling scheduling framework is proposed, which allows the AGV’s route to be continuously updated. Unlike most VRP models, an open chain structure is used to describe the dynamic delivery path of AGVs. In order to deal with the impact of uncertain meal preparation time on route planning, a stochastic programming model is formulated with the purpose of minimizing the expected order timeout rate and the total customer waiting time. In addition, an effective path merging strategy and after-effects strategy are also considered in the model. In order to solve the proposed mathematical programming model, a multi-objective optimization algorithm based on a NSGA-III framework is developed. Finally, a series of experimental results demonstrate the effectiveness and superiority of the proposed model and algorithm.

Effect of Layout Discretization on the Performance of Zone Control-Based Multi-AGV Traffic Management Systems

Automatic Guided Vehicles (AGVs) are widely used in flexible manufacturing systems for material handling inside the factory. Traffic management strategies, required to guarantee a conflict-free operation of the overall fleet, discretize the workspace of the AGVs and use the resulting graph model for route planning and execution. In zone control approaches, AGVs move from node to node on a permit basis, with limitations on the allowed number of AGVs at a time in each area of the graph to prevent and/or resolve deadlocks and conflicts. Hence, for an optimal implementation of traffic controllers in real manufacturing systems, it is essential to understand how the layout discretization influences the performance of the AGV network. This paper analyzes its effect in grid-like shaped workspaces by using a representative zone control algorithm and a recently developed improvement of it. Realistic numerical experiments on different layouts reveal that denser discretizations do not yield faster executions or increase in throughput, while lower control periods in the permit system entail significant performance uplifts.

Research on AGV Path Planning Integrating an Improved A* Algorithm and DWA Algorithm

With the rapid development of the economy and the continuous improvement of people’s living standards, the printing and packaging industry plays an increasingly important role in people’s lives. The traditional printing industry is a discrete manufacturing industry, relying on a large amount of manpower and manual operation, low production efficiency, higher labor costs, wasting of resources, and other issues, so the realization of printing factory intelligence to improve the competitiveness of the industry is an important initiative. Automatic guided vehicles (AGVs) are an important part of an intelligent factory, serving the function of automatic transportation of materials and products. To optimize the movement paths of AGVs, enhance safety, and improve transportation efficiency and productivity, this paper proposes an alternative implementation of the A* algorithm. The proposed algorithm improves search efficiency and path smoothness by incorporating the grid obstacle rate and enhancing the heuristic function within the A* algorithm’s evaluation function. This introduces the evaluation subfunction of the nearest distance between the AGV, the known obstacle, and the unknown obstacle in the global path in the dynamic window approach (DWA algorithm), and reduces the interference of obstacles with the AGV in global path planning. Finally, the two improved algorithms are combined into a new fusion algorithm. The experimental results show that the search efficiency of the fusion algorithm significantly improved and the transportation time shortened. The path smoothness significantly improved, and the closest distance to obstacles increased, reducing the risk of collision. It can thus effectively improve the productivity of an intelligent printing factory and enhance its flexibility.

Research on Energy Saving Effect of Parallel and Perpendicular Yard Layouts under Different Proportions of Transshipment at the Automated Container Terminal

The proportions of container transshipment is the key factor in determining the proportion of automated guided vehicle (AGV) and external container truck operations. In terms of parallel and perpendicular layouts of automated container terminals (ACTs), varying proportions of container transshipment result in different proportions of AGVs and external container truck operations, subsequently leading to distinct impacts on energy consumption (EC) for each ACT layout. This paper deemed EC as the primary evaluation criterion, established an EC model encompassing yard cranes (YCs) and container trucks, and investigated the EC of parallel and perpendicular layouts at different proportions of container transshipment. The results indicate that when the proportions of container transshipment were less than 17%, the parallel layout had lower EC; when it was between 17% and 21%, there was not much difference between the two layouts; when it was greater than 21%, the perpendicular layout had lower EC. This conclusion was based on an ideal environment established in this paper. When making decisions, decision makers should use this model as a starting point and adapt it flexibly to the actual situation of the port, in order to arrive at a reasonable and feasible plan.

Application of Technological Procedure Automated Guided Vehicles in the Production Hall of a Company Due to Increasing the Automation—Case Study

This paper discusses the possibility of deploying automated guided vehicle (AGV) technology in a company’s production hall. Chronometric measurements of material handling and people handling were performed. The handling processes were divided into five sub-processes, which were analysed in terms of production line operation, distance, and carrying capacity of the automated guided vehicles. Based on the measurements, we found that it was necessary to use four vehicles for handling in the foam production hall. The company is applying this automation project to foams, and after successful testing, plans to continue with automation for leather upholstery. To ensure the correct chronometric results, we measured the distances between each workstation on the production floor at the company. Based on an analysis of material handling on the production lines, we evaluated that there is a possibility of applying AGVs on the entire production floor. We analysed the volumes of material transfer and the possibilities of interconnection within the existing material handling in the enterprise. Complications arise when deploying AGVs in the plant because the handling to the production lines is not only provided by the parent company, but also by the component suppliers themselves. Based on this observation, we have identified potential collision points within the operation between automated guided vehicles and other material handling equipment or people. For this reason, we deployed one more AGV on the handling routes to prevent unwanted collisions. This means that a human would be able to perform even more processes than a machine and can circulate 80 times, where a machine under the same conditions would only perform the entire circulation 57 times. There is room here to use humans for other processes and to automate the handling of goods, although it introduces delays that can be eliminated by deploying additional automated guided vehicles. This opens room for further research questions on how these vehicles can be deployed, even in a small space, and how existing material handling can be automated.

A simulation-based Digital Twin for smart warehouse: Towards standardization

Cyber–physical systems are developed to meet the need to improve process flexibility, optimality, and transparency through Digital Twin (DT). Unfortunately, the application of DT is still limited in practice, and there is no standard way to achieve integration. An Asset Administration Shell (AAS) appears as a promising concept for realizing DT in a standard manner. A literature review shows that most studies only reached static DT and only considered a few specific assets, especially unmovable ones. This study contributes to DT development by proposing 3D-based computer simulation technology as dynamic DT based on the AAS Framework. The proposed concept enables DT to conduct dynamic monitoring, optimization, and direct controlling. A smart warehouse in Taiwan is used to verify the proposed concept. Assets considered include Automated Guided Vehicles (AGVs), Operator, Conveyor, Forklift, and Storage Rack. AAS structure, simulation model built in FlexSim software, and system integration architecture have been constructed. Two advantages of using simulation for DT are demonstrated: multi-objective simulation–optimization for AGV capacity planning and creating dynamic dashboards. Based on the proposed concept, industry 4.0 scenarios can be integrated comprehensively.