Open Experimental AGV Platform for Dynamic Obstacle Avoidance in Narrow Corridors

Demanding for warehousing automation and intelligence is critical with the rapid development of information and big data teleology. In most storehouses, cargos are mainly carried by Automatic Guided Vehicles (AGVs) or even manpower. Most AGVs follow predefined paths that are normally paved by some marks detected by AGVs. It largely limits the application of AGVs. Some AGVs relies on highly accurate Light Laser Detection and Ranging (LiDAR) devices for navigation. The paper presents a low-cost localization approach for indoor AGVs with the localization accuracy up to centimetres. The proposed method not only allows an AGV to move precisely without any predefined paths, but also reduces the cost largely. The Ultra Wide Band (UWB) technology is used in our approach. In the paper, a gradient decent method cooperated with a least square method is developed to deal with the nonlinearity of UWB ranging data. An optimal localization result is achieved within a small amount of iterations. The positioning results are also compared to a high-accuracy Real-Time Kinematic (RTK) satellite system. Meanwhile, the approach is able to diagnose the original UWB data and will discard any data corrupted by non-ignorable noises. Thus, the robustness is guaranteed.

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.

Two-tier MPC architecture for AGVs navigation assisted by edge computing in an industrial scenario

This paper describes a design effort in a smart tracking prototype, in an attempt to provide real-time, mobile and seamlessly indoor/outdoor location tracking for medical staff, patient or instruments in hospital environment, by integrating GPS (Global Position System) with UWB (Ultra Wideband). Ultra Wideband (UWB) technology can locate a person or asset to the accuracy of centimeters. While UWB technology is becoming popular for indoor position tracking, GPS technology has been around for some time for use in outdoor position tracking with an accuracy of 1 to 100 meters depending on the type of equipment used. It is envisaged that it is possible to combine GPS with UWB to give accurate position regardless of where the person or object is in indoor or outdoor. In view of the strong need for indoor-outdoor tracking in many environments such as hospitals, we looked into the possibility of integrating UWB and GPS to provide the target with seamless indoor-outdoor navigation capability and have developed a prototype. The system structure and experimental results will be illustrated in this paper. To our knowledge, no same system is reported yet.

Ultra-wideband (UWB) technology offers a promising solution to the RF spectrum drought by allowing new services to coexist with current radio systems with minimal or no interference. This coexistence brings the advantage of avoiding the expensive spectrum licensing fees that providers of all other radio services must pay. In Ultra Wide Band technology, the conventional analog waveform is a simple pulse which in general is directly radiated to the air. These short pulses have typical widths of less than 1 nanosecond and thus bandwidth of over 1 GHz. Due to this wide bandwidth, Federal Communication Commission(FCC) has imposed certain restrictions on the emission limits of UWB systems. Inorder to fit well to the FCC mask, the basic pulse shapes have to be modified. So pulse shaping is very much relevant in UWB domain. The immediate objective of this paper is to design an optimal pulse shaping technique. This paper proposes a hybrid approach for UWB pulse shaping which utilises ideas from existing two techniques.

This paper presents a path planning and tracking framework for autonomous mining articulated vehicles (AVs). The relation space method is proposed for path planning. In this method, a self-organising, competitive neural network is adopted to identify the space around the vehicle. Then, the vehicle's optimal driving direction is determined by using the spatial geometric relationships of the identified space. A proportional-integral-derivative (PID) controller is used to control the vehicle speed, whereas a model predictive control (MPC) is designed for steering control, where the path tracking error model is used for MPC development. In addition, the AV's dynamics model is built in Adams. The effect and sensitivity of the path-tracking controller are validated through the Matlab-Adams co-simulation. The test results show the satisfactory path-tracking performance.

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.

Intelligent warehouse becomes a key component of logistics process automation, which essentially promotes the productivity and cost reduction. This paper presents a novel design solution of an Automated Guided Vehicles (AGVs) system for intelligent warehouse. An improved version of classical Dijkstra shortest-path algorithm is proposed for efficient global path planning. In the case of multi-AGV, the time windows method is used to address the issue of conflict and deadlock. In addition, the local path planning and auto-localization is addressed by using a heuristics-based algorithm and Monte Carlo Localization algorithm respectively. Extensive numerical experiments based on Player/Stage simulator are carried out to assess the suggested algorithms, for a range of scenarios and the result well validates its effectiveness. Currently the proposed design solution is adopted in developing the prototype of AGVs to be deployed in practice.

High-accuracy localization systems for non-line-of-sight (NLOS) environments have been a popular focus of research on indoor positioning. In particular, the use of ultra-wideband (UWB) technology for ranging is emerging as an effective method of indoor localization due to its high accuracy. To improve the overall performance of UWB systems, combining UWB technology with an inertial navigation system (INS) is a commonly used indoor positioning approach. However, UWB measurements are affected by outliers under the NLOS propagation of radio signals. In addition, INS tend to suffer from error accumulation without constraints imposed by observations from other sensors. To address these problems, an adaptive robust extended Kalman filter algorithm based on tightly coupled UWB/INS/floor map integration is proposed, with which the noise in the UWB observations can be adaptively adjusted. The integrated system performs NLOS identification based on a map line segment matching algorithm; then, abnormal observations are abandoned, or their weights are reduced to prevent the error accumulation of the INS from being incorrectly constrained. Finally, an evaluation based on experimental data shows the significant enhancement achieved with the proposed algorithm based on the integration of UWB technology, an INS and a floor map, which can achieve a 2-D positioning accuracy that corresponds to a root mean square error of 0.27 m. Thus, substantial improvements can be achieved for long-term operation in NLOS environments using the proposed integration scheme.

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 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.

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.

Ultra-wideband (UWB) technology is characterized by transmitting extremely short duration of radio impulses. To improve its multiple access capability, UWB technology can be combined with traditional spread-spectrum techniques. In this paper, we present the multiple access performance of direct-sequence (DS) UWB system with diversity reception. Numerical results show that the multiple access performance of a DS-UWB system can be improved significantly by increasing the number of antenna array elements and/or by adding more paths at the receiver

The inter-vehicles communication is becoming increasingly mandatory to improve the road safety and to allow a better transport management. The concept behind an intelligent vehicle consists in equipping this vehicle with an optimum number of sensors, in order to perceive the environment surrounding it and to ensure an automatic guidance control and the communication with the infrastructure and other vehicles. Among the most often used tools for this objective, we can cite the embedded radar. Moreover, the Ultra Wide Band (UWB) communication systems are under fast development and undergo progressive implementation. The principle of UWB technology is to transmit rather low power, carrier-less, signals occupying a very broad spectrum [1]. The spectral concentration of radiated power is thus extremely low, so that the other systems perceive UWB ones like background noise. In this article, a road radar based on UWB technology is studied. The proposed device allows, in a road environment, good resolution and increased precision for detection and localization of obstacles.