Research on visual navigation algorithm of AGV used in the small agile warehouse

Aiming at the problems of poor flexibility, complicated path maintenance and poor positioning performance in the current guidance technology of AGV, this paper designs and implements a new navigation system of automated guided vehicle (AGV) based on AprilTags2 auxiliary positioning. The system uses the Robot Operating System (ROS) as a platform to develop AGV navigation functions. The navigation system comprises two parts: the hardware and software layers. Firstly, hardware selection is performed after considering the actual requirements, performance, cost, and other factors in the hardware layer. Simultaneously, the AGV chassis and single-steering wheel walking mechanism are built to provide a stable and flexible operating platform for the software layer. Secondly, the software layer design includes two parts, namely the ROS navigation planning end and AprilTags2 detection. The ROS planning navigation end performs the design of four functional modules (i.e., AGV map construction, autonomous positioning, path planning, and path tracking), while the AprilTags2 detection part obtains the visual positioning pose of AGV by setting the AprilTags2 on each site and using the Kinect1 camera to detect. A more accurate AGV pose can be obtained by merging the former pose with the kinematic estimated pose. Finally, the two groups of positioning errors of the system before and after adding the AprilTags2 auxiliary positioning are tested and compared. The experimental results show that the navigation accuracy of the proposed system with AprilTags2 auxiliary positioning is significantly improved.

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 (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.

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.

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.

Intelligent Logistics is one of most important strategic directions for development of modern logistics industry. Automated guided vehicle (AGV) is key equipment benchmark for development of Intelligent Logistics. This paper analyzes development history of AGV and their current application status, both at home and abroad. It also points out AGV’s new direction for development and advancement of China’s Intelligent Logistics where we should focus on research and promotion of AGV since they are artificial intelligence-oriented and based on needs of Intelligent Logistics. In future, combination of robot and autopilot technology will greatly enhance application and service levels of AGV to Intelligent Logistics, reduce logistics costs of trunk transportation and the last mile, improve efficiency of logistics transportation and distribution, and promote user’s experience.

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 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.

The paper presents the virtual prototype of the warehouse system based on automated guided vehicles (AGV) using metal-line following guidance and RFID tags navigation technology. The structure of the AGV virtual prototype with a lifting device and proposed navigation technology created in the MSC Adams software is shown. The process of automated placement of AGV and racks instances in a virtual storage space and control plant creation is described. An example of the implementation of a small warehouse system and some algorithms for its operation: prediction and avoiding collisions with other AGVs, dynamic restructuring of the route with a busy section of the route, transportation of a group of racks along optimal routes are considered. The results of the dynamic cosimulation are presented.

In this paper, we describe a vision sensor-based driving algorithm for indoor automatic guided vehicles (AGVs) that facilitates a path tracking task using two mono cameras for navigation. One camera is mounted on vehicle to observe the environment and to detect markers in front of the vehicle. The other camera is attached so the view is perpendicular to the floor, which compensates for the distance between the wheels and markers. The angle and distance from the center of the two wheels to the center of marker are also obtained using these two cameras. We propose five movement patterns for AGVs to guarantee smooth performance during path tracking: starting, moving straight, pre-turning, left/right turning, and stopping. This driving algorithm based on two vision sensors gives greater flexibility to AGVs, including easy layout change, autonomy, and even economy. The algorithm was validated in an experiment using a two-wheeled mobile robot.

As the need for efficient warehouse logistics has increased in manufacturing systems, the use of automated guided vehicles (AGVs) has also increased to reduce travel time. The AGVs are controlled by a system using laser sensors or floor-embedded wires to transport pallets and their loads. Because such control systems have only predefined palletizing strategies, AGVs may fail to engage incorrectly positioned pallets. In this study, we consider a vision sensor-based method to address this shortcoming by recognizing a pallet’s position. We propose a multi-task deep learning architecture that simultaneously predicts distances and rotation based on images obtained from a visionary sensor. These predictions complement each other in learning, allowing a multi-task model to learn and execute tasks impossible with single-task models. The proposed model can accurately predict the rotation and displacement of the pallets to derive information necessary for the control system. This information can be used to optimize a palletizing strategy. The superiority of the proposed model was verified by an experiment on images of stored pallets that were collected from a visionary sensor attached to an AGV.

The deployment of heterogeneous Automated Guided Vehicles (AGVs) in smart manufacturing requires control strategies that can accommodate distinct actuation characteristics and constraints. This paper proposes a Multi-Factor Coupled Parameter-Adaptive Model Predictive Control (MFCP-AMPC) framework. Unlike conventional approaches requiring vehicle-specific tuning, this framework unifies differential-drive, dual-steer, and mecanum-wheel platforms under a single parameter-varying state-space model that respects the specific actuation limits of each topology. A key contribution is the multi-factor coupling mechanism that dynamically adjusts the prediction horizon and weighting matrices based on path curvature, vehicle speed, and tracking error. Experiments on industrial AGV prototypes demonstrate that the framework achieves robust tracking precision under varying payloads. Crucially, by acknowledging physical limits, the framework achieves strict millimeter-level accuracy (RMSE < 7 mm) in quasi-static low-speed complex maneuvers (v≤0.3 m/s), and maintains highly competitive industrial precision (RMSE ≈ 15∼25 mm) under aggressive high-speed tracking (v≥1.0 m/s). Crucially, the proposed method significantly improves the control input smoothness (Smoothness Index > 0.75), thereby reducing mechanical wear and preventing actuator saturation. Real-time validation (12 ms average solve time on an Intel i7 IPC) confirms its suitability for resource-constrained industrial controllers.

Considering the influence of the dynamic characteristic of automatic guided vehicle (AGV) on trajectory tracking controlling, double closed loop control structure is proposed to realize the position/force cooperative control. The outer loop controlling uses backstepping to design corresponding position controller for kinematics model of AGV, while the inner control uses the integral sliding mode controlling. Self-adaptive controlling law is used to estimate the uncertain external interference in the driving force controller and stability of AGV trajectories tracking proof is proposed. In order to make the system achieve better control performance and prevent the occurrence of severe wobble, the hyperbolic tangent function in the control law of sliding mode control replaces the sign function to ensure a continuously smooth control input and states of the system. In the Matlab/simulink environment, tracking a given splayed trajectory generated by the S function to verify the double closed loop control structure and the effectiveness of the control algorithm proposed in this paper.

Automated guided vehicles and autonomous mobile robots for recognition and tracking in civil engineering

Automated Guided Vehicles (AGVs) are a promising solution to automation in the view of Industry 4.0. The amount of goods that can be automatically transported can be further increased by efficient path planning and tracking methods. The efficiency is always a trade off in terms of cost, accuracy and flexibility, but should never influence safety. This paper proposes a flexible path planning and tracking solution, aiming to be applicable to several application domains, and which is able to dynamically avoid an (unforeseen) obstacle by an overtake manoeuvre. The approach is based on Model Predictive Control (MPC), consisting of multi-domain objectives, applicable to multiple vehicle models and is fast in calculation time due to an adjusted multiple shooting approach, which guarantees constraint satisfaction over the entire time domain. Further, a dynamic maximum velocity approach is proposed, which adapts the maximum velocity constraint to the environmental circumstances, such that an emergency brake can be applied if a human would appear behind a corner or obstacle. These algorithms are implemented on an autonomous forklift. The overall system performance is measured by the time-of-arrival of an obstacle avoidance manoeuvre. To evaluate the influence of usage of a low-cost ultra wideband (UWB) localization technology, the same algorithms and platforms are used in combination with standard off-the-shelf laser based localization technology. The UWB technology does lead to a slightly larger spread in terms of time-of-arrival, but is on average very much comparable to the laser-based setup.