AGV Navigation Based on AprilTags2 Auxiliary Positioning

This paper presents the design and development of a mecanum wheel-based Automated Guided Vehicle (AGV), serving as a test platform for autonomous system research. The study highlights the essential elements of an Automated Guided Vehicle (AGV) and provides a thorough review of the literature. It explores the information, electrical, and mechanical actuator systems critical to the AGV's functioning. The AGV's navigation system is powered by a Jetson Nano minicomputer, with high-level software development implemented using the Robot Operating System (ROS). The software design focuses on modularity because it facilitates easier changes and maintenance, which is essential for research and rapid testing. The DC motors control subsystem controls AGV mobility by adjusting wheel speed and direction. These motors have incremental encoders that indicate wheel rotation. The Jetson Nano is used in the framework to process data from various sensors and monitor the AGV's performance. Experimental validation was carried out in both static and dynamic environments, where the AGV successfully executed autonomous navigation tasks with an average localization error, motion time, and path deviation. These results demonstrate the system's reliability, accuracy, and potential as a flexible test platform for developing and benchmarking autonomous navigation algorithms in research and educational contexts.

Automated Guided Vehicles (AGV) have received a lot of attention in recent years in terms of both hardware and software research. Nowadays, the AGV offers more adaptable and effective industrial and transportation system solutions. An AGV’s navigation technique is essential to its operation. The decision to use AGV navigation is not straightforward, even if it appears appropriate and sufficient. This paper surveys the navigation approaches applied to AGV in the past five years of published academic research. In doing so, this work responds to three related questions: 1) are the AGV’s classical navigation techniques still relevant to the current application area?; 2) are heuristic navigation techniques themselves able to optimize AGV movement in terms of guide and strategy?; and 3) is the use of artificial intelligence (AI) in AGV navigation techniques able to increase system performance? As a result, numerous techniques for AGV navigation have been developed globally. On the other hand, the most popular navigation approaches are provided below for more research.

Automated Guided Vehicles (AGV) with magnetic navigation and electromagnetic navigation have lower path flexibility due to the need to lay the path ahead of time. The Simultaneous Localization and Mapping (SLAM) is mature in technology theory, so that this technique is applied to the AGV navigation. The laser sensor is used to acquire the information in the location environment for immediate location and mapping, so that carry out the path planning and complete the AGV autonomous navigation. Extended Kalman Filter (EKF) is the most common solution for laser SLAM. Firstly, this paper analyzes the research results of EKF-SLAM at this stage and its limitations. Secondly, it discusses the application of EKF-SLAM in AGV and the development direction in the next stage. By comparative analysis shows that laser EKF-SLAM can meet the requirements of AGV navigation. The development of EKF-SLAM algorithm in positioning, mapping accuracy and the ability to solve interferences in dynamic environment has an important reference for the reliability of AGV navigation.

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.

The Automated Guided Vehicle (AGV) is a device that uses various sensor technologies, automation, and multiple control systems. AGVs have many navigation methods, of which the magnetic stripe navigation system is well developed. The magnetic stripe navigation AGV positioning is carried out precisely and at a lower cost level. However, the magnetic stripe is prone to breakage, and the AGV can only travel according to the magnetic stripe and cannot be intelligently avoided or the task changed in real-time by the control system. This paper draws on magnetic stripe navigation tracking principles, using grey-scale sensors for stable path tracking; distance sensors are combined for timely obstacle avoidance; Bluetooth is used to obtain approximate indoor locations for path planning. The navigation system simultaneously combines multi-sensor information for navigation, ultimately enabling the AGV to be quickly positioned accurately along the prescribed path and return to the route in time to avoid obstacles.

In this research, a localization system for a particular Automated Guided Vehicle (AGV), namely a Laser Guided Vehicle (LGV), which takes advantage of RSSI data, is described. The vehicle, its sensors and the wireless sensor network are virtualized, in order to provide a software replica of that available in a real factory. A Co-Simulator, composed by two simulation environments, has been implemented: from one hand, Robot Operating System (ROS), the RVIZ and GAZEBO for modeling and simulating the virtual system, and from the other hand, COOJA for reproducing the Wireless Sensor Network (WSN) behavior and RSSI signals. The virtualized LGV in ROS, together with its virtual sensors set, is integrated with the WSN in COOJA, so that localization can be performed by sensor fusion of odometric and RSSI data according to a Kalman Filter approach. The Co-simulator can be used to deploy path-tracking policies and test them before actual implementation in a Cyber Physical System (CPS) scenario.

Efficient path planning for Automated Guided Vehicles (AGVs) in warehouse automation is crucial yet challenging, particularly in environments with irregular structures and constrained spaces. This study addresses these challenges by focusing on AGVs without rotary platforms, which require the rotation of the entire robot-rack assembly for directional changes, demanding additional space and complex path planning. We have developed dedicated algorithms that integrate robotics and optimization principles to tackle these issues. Our methods take into account the spatial requirements for rack rotation, navigating through limited inter-rack clearance, and adapting to irregular warehouse layouts. Extensive simulations and real-world applications validate the proposed solutions, demonstrating significant improvements in traversal efficiency and collision risk mitigation. The results indicate that our algorithms effectively enhance the operational efficiency and reliability of AGV systems in complex warehouse environments. This research adapts AGV path planning by providing robust strategies to optimize navigation in challenging settings, ultimately improving warehouse productivity.

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

In order to solve the path planning problem of Automated Guided Vehicle (AGV) transporting goods and packages in warehouse logistics, this paper studies the Algorithm of the path planning problem, an improved adaptive genetic algorithm is presented to solve the path optimization problem. In order to avoid falling into local optimum, an improved self-adaptive crossover method is proposed. In order to avoid the conflict of AGV in the process of path planning, the concept of Congestion Coefficient is introduced into the design of fitness function, reduce the AGV in the path optimization process of conflict. The mathematical model of AGV to accomplish multi-task is established, and the comparative experiment is set up. The experimental results show that the improved adaptive genetic algorithm can improve the optimization performance by 4.2% in solving the optimal path problem, and improve the convergence speed by 4.2%, the improved algorithm has obvious advantages.

To address the issues of insufficient safety distance and unsmooth paths in AGV path planning for narrow road scenarios, this paper proposes a method that integrates Voronoi-skeleton-based custom layers with traditional cost maps. First, key nodes of the Voronoi skeleton are extracted to generate a custom layer, which is then combined with static, obstacle, and expansion layers to form a new four-layer network cost map. This approach accurately distinguishes obstacle influences and enhances algorithm robustness. The A* algorithm based on this new map guides the automated guided vehicle (AGV) to travel safely along the road center. Second, an improved A* algorithm is employed for global planning to ensure safe navigation. Finally, B-spline smoothing is applied to the global path to enhance the AGV's efficiency and stability in complex environments. The experimental results show that in narrow road scenarios, the proposed algorithm improves AGV path planning safety by 82%, reduces the number of spatial turning points by 55.85%, and shortens planning time by 48.98%. Overall, this algorithm significantly enhances the robustness and real-time performance of path planning in narrow roads, ensuring the AGV moves safely in an optimal manner.

Intelligent logistics is an important part of intelligent manufacturing, and Automated Guided vehicle (AGV), as a part of seamless enterprise production system and storage system, is an important technical equipment to achieve intelligent logistics, and has been widely used in factories, warehousing and logistics environment such as automatic handling scenarios. Since a single AGV in the same site can no longer meet the trend of automated factory, it is important to effectively schedule and plan paths for multiple AGVs in limited space. Aiming at multi-AGV system, this paper analyzes and organizes the common path planning algorithms in the market, in order to improve the effectiveness and safety of AGVs. The A* algorithm and the ant colony algorithm's principles will be analyzed in the first part of the paper. Then describe the research status of the two respectively and summarize the advantages and defects respectively. Finally, prospect the development progress of AGV coordinated scheduling and path planning.

AGV (Automated Guided Vehicle) technology has attracted increasing attention. Precise control of AGV position and attitude information in complex operating environment is a key part of smart factories. With outdoor AGV as a platform, this study uses BDS/INS combined navigation system combining Beidou positioning system and inertial navigation system and takes the velocity and position difference between BDS and INS as a model. An integrated navigation method is proposed to improve bee colony algorithm and optimize the BP neural network-assisted Kalman filtering to achieve accurate positioning. Moreover, the optimization of BP neural network navigation using INS navigation, network-assisted navigation and bee colony algorithm is simulated. Results demonstrate that the integrated navigation algorithm has effectiveness and feasibility, and can solve the problems of BDS misalignment and large INS navigation error in complex environments.

With the development of logistics industry, the logistics automated warehouse is ahead in the race toward the direction of multi-user, multi-variety and flexibility automated warehouse. Traditional AGVs (automatic guided vehicle) are difficult to meet the new demands of logistics warehouse. This paper presents a method to extract the deviation of AGV's navigation path based on threshold segmentation of chromatic aberration according to the actual needs. The paper realized high precision path tracking of robot by the path tracking controller adopting the idea of beforehand closed loop control. Finally, the logistics warehouse environment was simulated, and the navigation performance of the designed AGV was fully tested. The experimental results show that the visual navigation AGV features of flexible navigation, high precision and smooth operation and can satisfy the demand of flexible storage.

To address the challenges of path conflicts and communication in cooperative operation of dual Automated Guided Vehicles (AGVs) in industrial logistics, a low-cost intelligent dual-AGV calling system based on STM32 microcontroller and ZigBee wireless communication technology is proposed. The system employs the STM32F103C8T6 as the core controller, integrating TB6612 motor driver, TCRT5000 infrared tracking, and HC-SR04 ultrasonic ranging modules to achieve autonomous navigation and dynamic obstacle avoidance for AGVs. A star communication network is established via ZigBee for transmitting instructions from the host computer and receiving AGV status information. The core innovation lies in a dynamic avoidance strategy based on random priority decision-making: when the ultrasonic sensors detect an inter-AGV distance less than 40cm, a random number generation mechanism (seeded by ultrasonic ranging data) is triggered. Decision information is exchanged via ZigBee, and the AGV with the higher number gains priority passage. Experiments demonstrate that the system stably realizes dual-AGV cooperative calling, path tracking, and intersection conflict resolution, with an average response time of approximately 185ms. ZigBee communication exhibits a delay below 50ms and a packet loss rate less than 0.5%, providing a highly reliable and low-cost solution for flexible production line logistics.

The most fundamental requirements for achieving accurate navigation of mobile robots are localization, map building and path planning. Currently, the mainstream methods include magnetic peg navigation, laser navigation, and 2D code navigation, etc. SLAM technology has the advantages of automation, intelligence, environmental adaptability, and low cost, and AGVs using this algorithm have been widely used in indoor applications and logistics transportation. Therefore, the SLAM (Simultaneous Localization and Mapping) algorithm based Automated Guided Vehicle (AGV) has become a hot research topic at home and abroad. In this paper, we investigate the SLAM algorithm in AGV system, and build indoor and logistics simulation environment maps based on 2D laser SLAM technology of Hector, Cartographer, Gmapping and Karto algorithms, and compare them by building accuracy index. The results show that the Hector algorithm has faster map building speed and accuracy, and is more suitable for indoor and logistics AGV systems.