Multi-AGV Systems in Shared Industrial Environments: Advanced Sensing and Control Techniques for Enhanced Safety and Improved Efficiency

Automated guided vehicles (AGVs) have long been identified as a potential driver to improve system efficiency and lower labour costs in material handling systems. Accordingly, the reliability and availability of AGV systems is crucial to assure the stability and efficiency of these systems. However, the reliability issues and maintenance strategies of AGVs have not previously been studied sufficiently. This is even more marked in the case of multi-AGV systems that consist of fleets of AGVs. To fill this knowledge gap, research is conducted considering a multi-AGV system, consisting of three AGVs, in order to develop a scientific methodology for optimising the layout design, operation and maintenance of a multi-AGV system. Once an AGV is failed, it will be towed to the maintenance site for repair by a recycle vehicle to prevent deadlock and conflict. The efficiency of the recycling process of failed AGVs in a multi-AGV system, with respect to the change of location of the maintenance site, is analysed by the approach of coloured Petri nets (CPNs). A CPN model simulating the corrective and periodic preventive maintenance processes of failed AGVs is also developed in order to investigate the impact of different AGV maintenance strategies on the operation efficiency of the multi-AGV system. The simulation results obtained clearly show that the location of maintenance sites and maintenance strategies do have significant influence on the performance of a multi-AGV system, where corrective maintenance is an effective measure to maintain the long-term reliability and stability of the system.

This paper presents a new approach for kinodynamic online motion planning for Automated Guided Vehicles (AGVs) in industrial environments. AGVs normally transport large and heavy transport units such as Euro-pallets or mesh pallets. Nowadays just-in-time inventory management and lean production requires the transport of small transportation units. Thus a flexible material flow is needed that can not be fulfilled by continuous material handling devices like belt or roll conveyors. Our approach uses small holonomic AGVs that are equipped with safety laser range finders for our semi autonomous motion and path planning system. This system plans trajectories with respect to the kinodynamic constraints of the AGVs and the environment. The operator retains as much control as necessary and the vehicle can react online on course deviations and unforeseen changes in the environment.

Industry 4.0: Sustainable material handling processes in industrial environments

In this work we present an obstacle detection and tracking solution applied to Automated Guided Vehicles (AGVs) in industrial environments. The proposed method relies on information provided by an omnidirectional stereo vision system enabling 360 degree perception around the AGV. The stereo data is transformed into a classified digital elevation map (DEM). Based on this intermediate representation we are able to generate a set of obstacle hypotheses, each represented by a 3D cuboid and a free-form polygonal model. The cuboidal model is used for the classification of each hypothesis as “Pedestrian”, “AGV”, “Large Obstacle” or “Small Obstacle”, while the free-form polylines are used for object motion estimation relying on an Iterative Closest Point (ICP) method. The obtained measurements are subjected to a Kalman filter based tracking approach, in which the data association takes into account also the classification results.

This paper deals with a holistic approach to coordinate a fleet of automated guided vehicles (AGVs) in an industrial environment. We propose an ensemble approach based on a two layer control architecture and on an automatic algorithm for the definition of the roadmap. The roadmap is built by considering the path planning algorithm implemented on the hierarchical architecture and vice versa. In this way, we want to manage the coordination of the whole system in order to increase the flexibility and the global efficiency. Furthermore, the roadmap is computed in order to maximize the redundancy, the coverage and the connectivity. The architecture is composed of two layers. The low-level represents the roadmap itself. The high-level describes the topological relationship among different areas of the environment. The path planning algorithm works on both these levels and the subsequent coordination among AGVs is obtained exploiting shared resource (i.e., centralized information) and local negotiation (i.e., decentralized coordination). The proposed approach is validated by means of simulations and comparison using real plants.

Automated guided vehicle (AGV) is widely used in transportation and distribution of materials. In the development of multi-AGV systems, how to handle deadlocks is a core issue. Most existing researches focus on open systems. Thus those results are not applicable in real-world systems. A few works have paid attention to closed systems recently, but the results are not flexible and generic enough. Therefore, an improved deadlock avoidance algorithm in closed guide-path system is proposed in this paper. The proposed algorithm is divided into an offline stage and an online stage. At the offline stage, the guide-path graph is processed to obtain useful information for the online stage. At the online stage, the algorithm assesses the safety of each resource allocation to ensure a deadlock-free operation. The computational complexity of the algorithm is shown to be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(|E|)$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula> is the set of the edges in the guide-path graph. Based on the algorithm, an overall control strategy is also developed, and then implemented and tested in real systems. It is also compared with Banker’s algorithm and its variants, the results of which show that the proposed algorithm has a better performance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Deadlock is a core issue in guide-path based multi-AGV systems. Once deadlock occurs, the whole system will collapse. This problem is more serious in closed systems, which is paid less attention to in literature. Current deadlock solutions are either too restrictive, resulting in inefficient resource utilization and poor flexibility, or computationally intensive and thus are unable to scale to large systems. To solve this problem, an improved deadlock avoidance algorithm with polynomial computational complexity is proposed in this paper, and it is more flexible than the Banker’s algorithm and its variants. Furthermore, an overall control framework (i.e., including task assignment, path planning and real-time scheduling) is developed based on the deadlock avoidance algorithm, which keeps persistent operation of the considered system while still guaranteeing deadlock-free. However, in this overall control framework, the choice of path is not optimized with respect to travel distance and makespan, which is the direction of future research.

In this paper, we propose to apply lane detection to the automated guided vehicle (AGV) platform in an industrial environment to realize automatic driving of AGV based on visual navigation. Similar to the lane detection of urban roads or highways, we present two methods for AGV navigation band detecting. The first method is based on traditional image processing which combines an adaptive threshold method with Sobel operator to detect the navigation band. The other method is based on deep learning, which implements end-to-end edge detection by LaneNet and it overcomes the problem of uneven illumination that the first method is struggling with. The RANSAC algorithm is finally used to fit the edge lines from the detected edge pixels in order to generate the AGV control commands to achieve robust real-time AGV automated driving.

Automated guided vehicles (AGVs) are widely used for transporting materials in industry and commerce. In this research, an intelligent AGV-based material-handling system was developed using a model- based systems engineering (MBSE) approach. The core of the AGV, the controller, was designed in the system modelling language environment using Visual Paradigm software, and then implemented in the hardware. As the result, the AGV’s complex tasks of material handling, navigation, and communication were successfully accomplished and tested in the real industrial environment. The developed AGV is capable of towing trolleys with a weight of up to 200kg at walking speed. The AGV can be incorporated into an intelligent material-handling system with multiple autonomous vehicles and work stations, thus providing flexibility and reconfigurability for the whole manufacturing system. Ergonomic and safety aspects were also considered in the design of the AGV. A comprehensive safety system that is compliant with industrial standards was implemented.

PurposeThis paper aims to propose and formulate a complicated routing/scheduling problem for multiple automated guided vehicles (AGVs) in a manufacturing system.Design/methodology/approachConsidering the due date of AGVs requiring for material handling among shops in a jobshop layout, their earliness and tardiness are significant in satisfying the expected cycle time and from an economic view point. Therefore, the authors propose a mathematical program to minimize the penalized earliness and tardiness for a conflict-free and just-in-time production.FindingsThe model considers a new concept of turning point for deadlock resolution. As the mathematical program is difficult to solve with a conventional method, an optimization method in two stages, namely, searching the solution space and finding optimal solutions are proposed. The performance of the proposed mathematical model is tested in a numerical example.Practical implicationsA case study in real industrial environment is conducted. The findings lead the decision-makers to develop a user interface decision support as a simulator to plan the AGVs’ movement through the manufacturing network and help AGVs to prevent deadlock trap or conflicts. The proposed decision support can easily be commercialized.Originality/valueThe benefits of such commercialization are increase in the quality of material handling, improve the delivery time and prevent delays, decrease the cost of traditional handling, capability of computerized planning and control, intelligent tracking and validation experiments in simulation environment.

This paper introduces a novel Human Machine Interface (HMI) that allows users to interact with a fleet of Automated Guided Vehicles (AGVs) used for logistics operations in industrial environments. The interface is developed for providing operators with information regarding the fleet of AGVs, and the status of the industrial environment. Information is provided in an intuitive manner, utilizing a three-dimensional representation of the elements in the environment. The HMI also allows operators to influence the behavior of the fleet of AGVs, manually inserting missions to be accomplished.

- Automated Guided Vehicles (AGVs) play a vital role in modern industries for efficient material handling and internal logistics. Conventional AGV systems rely on fixed infrastructure such as embedded wires, magnetic tapes, or predefined tracks, which limits flexibility and increases installation cost. This paper presents the design and implementation of an Intelligent Automated Guided Vehicle (IAGV) using machine vision and image-based visual serving. The proposed system utilizes image processing techniques to recognize directional signs and navigate autonomously in a dynamic shop-floor environment. The mechanical structure is designed using a three-wheel tadpole configuration to enhance maneuverability and reduce rolling resistance. Structural analysis using finite element analysis (FEA) validates the load-carrying capacity of the vehicle. Experimental results demonstrate that the proposed IAGV provides improved flexibility, safety, and adaptability compared to conventional AGV systems, making it suitable for modern industrial environments. Key Words: Automated Guided Vehicle (AGV), Machine Vision, Image Processing, Visual Servoing, Intelligent Systems.

The Fourth Industrial Revolution or Industry 4.0 is extremely relevant and important in manufacturing for several reasons. Failing to adopt the technology of the Fourth Industrial Revolution can cause organizations to fall behind, as their operations are not digitized enough in benchmarking competitors. A major industry as bicycle manufacturing needs to be transformed into a digital factory in order to keep up with the evolving technology. The objectives of this research are in designing a 3D digital mock up and Augmented Reality (AR) for bicycle frame production in the form of digital factory for increasing production capability by decreasing the break-even point whilst avoiding Covid-19 contraction using Tecnomatix Plant Simulation and Unity 3D programs. This research constructed three alternative digital factory layouts that are designed by using a 3D simulation program. The risk of Covid-19 contraction points and the three layouts of cost per unit are analyzed. The results show that the break-even point of the first to the third layouts with Automated Guided Vehicle (AGV) are 26,580, 26,322 and 25,354 units respectively. The result of the risk about Covid-19 contraction points from the first to the third layout with AGV are 21,272, 2,872 and 0 points respectively. This means the most appropriate layout for bicycle frame production is the third layout due to the best break-even point that can significantly avoid Covid-19 contraction. The benefit of this research is to integrate a 3D digital mock up, AR and Cobot including AGV to assist the resilience of operations with a dynamic industrial environment.

Path planning is one of the most important fields of research in the area of robotics. In this paper, a path planning method for a certain type of wheeled mobile robots called automated guided vehicles (AGVs) is proposed. The proposed model applies fuzzy control techniques to navigate the AGV in an unknown environment to reach a certain destination. In addition, a new method for escaping local minimums, which may be very critical issue faced by an AGV in an unknown environment, is introduced. Simulation results show satisfactory performance of the proposed method

A human-machine collaboration method based on real time action recognition is presented to achieve the collaboration between automated guided vehicles (AGVs) and human operators through the perception of tasks conducted by human operators. The process of action recognition is accomplished by a Kinect vision sensor and convolutional neural networks (CNN), and the methods of multiple features fusion and hierarchical cluster are applied in the process of action recognition. The utilization of action recognition technology can effectively shorten the expected waiting time, and can avoid the assembly line stagnation caused by human errors to a great extent. Finally, the experiment result indicates that the overall efficiency has been increased significantly after applying the method.

Automated Guided vehicle (AGV) is like a robot that can deliver the materials from the supply area to the technician automatically. This is faster and more efficient. The robot can be accessed wirelessly. A technician can directly control the robot to deliver the components rather than control it via a human operator (over phone, computer etc. who has to program the robot or ask a delivery person to make the delivery). The vehicle is automatically guided through its ways. To avoid collisions a proximity sensor is attached to the system. The sensor senses the signals of the obstacles and can stop the vehicle in the presence of obstacles. Thus vehicle can avoid accidents that can be very useful to the present industrial trend and material handling and equipment handling will be automated and easy time saving methodology.