Online kinodynamic motion planning for omnidirectional automatic guided vehicles

Automated guided vehicles (AGVs) are essential components for the automation of fulfillment centers, a type of warehouse, where goods are stored on shelves and carried by AGVs. To increase the productivity in inventory management, a well-organized cooperative path control is required to transport goods to the designated picking stations. In this paper, we propose a QMIX-based scheme for the cooperative path control of multiple AGVs. Although QMIX is the one of popular cooperative multi-agent reinforcement learning algorithms, we find that QMIX alone was not enough to increase productivity in warehouse systems. So, we develop two novel techniques that can be used with QMIX: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sequential action masking</i> that eliminates all the collision cases and 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">additional local loss</i> that improves collaboration of individual AGVs. They help to encourage the AGVs to cooperate more for high productivity. By extensive simulations, we present the superiority of the proposed scheme on several layouts in fulfillment centers. The effect of cooperation among AGVs in the proposed scheme is verified through the comparison study with the existing algorithms. Additionally, we show the generalization performance by investigating the reusability of the model trained with the proposed scheme.

The initiative centers on the development and production of an Automated Guided Vehicle (AGV), designed to transform logistics across multiple industries, such as manufacturing, healthcare, and hospitality. As businesses increasingly aim to enhance their operational efficiency, automating transportation becomes vital for lowering labor expenses and minimizing the time delays associated with human involvement. The AGV presents considerable benefits in material handling, facilitating the effective and dependable movement of goods. Fitted with cutting edge sensors, the AGV can navigate autonomously within its surroundings, identifying and circumventing obstacles in real time. This feature guarantees safe functionality in dynamic environments, making it applicable in a variety of sectors. For example, in healthcare settings, the AGV can deliver medical supplies and equipment, reducing wait times and improving patient outcomes. In educational facilities, it can transport documents between offices, optimizing administrative workflows. The AGV’s adaptability also extends to shopping centers, hotels, banks, and corporate offices, where it can efficiently distribute items, thereby enhancing service delivery. Powered by a battery operated remote sensing system, the vehicle is designed to follow designated routes indicated by QR codes. This approach ensures precise navigation and simplifies the complexities often associated with traditional routing methods. By incorporating advanced technology, the AGV not only boosts operational effectiveness but also fosters a more organized and systematic logistics framework. In summary, this project is set to significantly influence various sectors by meeting the urgent demand for automation in material transport. As industries continue to progress and adopt technological innovations, the deployment of AGVs will be essential in optimizing operations, ensuring prompt deliveries, and ultimately enhancing overall productivity.

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.

A motion planning method that integrates global planning and local control is proposed in this paper, aiming to enable Automated Guided Vehicle (AGV) to navigate automatically in two-dimensional environment. The layered cost maps are utilized and divided into global cost map and local cost map to respectively provide environment information for the global planning and local control. Specifically, the global planning is based on the path planning algorithm to establish a global optimal path on the global cost map. Under the guidance of global path, local control generates an optimal collision free trajectory with temporal information on the local cost map, which can avoid contact with surrounding obstacles. The local control operates in real-time and consider temporal information, thereby it can directly generate commands for the underlying robot motion controller. Both the simulation and experimental results are presented in support of the feasibility and effectiveness of the proposed motion planning.

The developing wireless charging technology has great potential for application on Automated Guided Vehicles (AGV). However, due to the strict requirements for high-efficiency wireless charging, it is difficult to navigate towards the charging pile at one go, especially under large-scale semi-structured or unstructured scenes where there have not enough features to obtain precise enough global positioning. Furthermore, single-stage planning relies largely on AGV's control performance, which often leads to failed alignment. In this paper, we discuss the factors that impact wireless charging efficiency and determine the required positioning accuracy. Accordingly, a multi-stage perception, positioning and planning method composed of coarse positioning, dynamic adjustment, and end-to-end alignment is proposed. In the coarse positioning stage, the use of iterative closest point (ICP) and simultaneous localization and mapping (SLAM) allows the AGV to relocate and reach the designated area flexibly and quickly. In the dynamic adjustment stage, AprilTag and LiDAR inertial odometry (LIO) is fused to estimate AGV's pose more accurately by addressing problems with AprilTag exceeding camera's field of view (FOV) or large observation errors at long distance. Finally, high-precision positioning information is obtained in the end-to-end alignment stage with coupled monocular camera and 2D LiDAR measurements. In each stage, different path constraint and obstacle avoidance strategy for motion planning are adopted to achieve higher efficiency. We show that with the proposed method, automatic wireless charging tasks could be performed without the need of readjustment, even under unstructured environment.

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.

- 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.

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.

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.

This chapter describes innovative sensing technologies and control techniques that aim at improving the performance of groups of automated guided vehicles (AGVs) used for logistics operations in industrial environments. We explicitly consider the situation where the environment is shared among AGVs, manually driven vehicles, and human operators. In this situation, safety is a major issue that needs always to be guaranteed, while still maximizing the efficiency of the system. This paper describes some of the main achievements of the PAN-Robots European project.

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.

The automated guided vehicle (AGV) is widely used in industrial environments for goods transportation. However, issues such as mechanical wear, reduced battery life, navigation error accumulation, and decreased operational efficiency caused by frequent starts and stops need to be addressed. This paper proposes an improved Autonomous Emergency Braking (AEB) algorithm to tackle these problems. The algorithm employs a stepwise deceleration strategy, effectively reducing the frequency of sudden stops and enhancing the system's operational smoothness. The AEB algorithm not only considers straight-line driving scenarios but also optimizes deceleration strategies for turning scenarios, adjusting the deceleration detection range according to the turning trajectory. Additionally, a velocity smoothing algorithm is designed to ensure that speed changes during deceleration are gradual, avoiding abrupt speed variations that could impact the system. The feasibility of the AEB algorithm is validated through testing on actual equipment, and its performance is compared to that of a conventional emergency stop strategy. Experimental results show that the AEB algorithm significantly reduces the number of sudden stops, improves the AGV's operational smoothness and safety, and demonstrates excellent adaptability and robustness across different operational conditions.