Search Results (317)

The heterogeneous automatic guided vehicle (AGV) system, composed of several AGVs with different load capability and handling function, has good flexibility and agility to operational requirements. Accurate task travel time prediction (T3P) is vital for the efficient operation of heterogeneous AGV systems. However, T3P remains a challenging problem due to individual task correlations and dynamic changes in model input/output dimensions. To address these challenges, a biomimetics-inspired learning framework based on a radial basis function (RBF) neural network with an improved mayfly algorithm and a selective update strategy (IMA-SURBF) is proposed. Firstly, a T3P model is constructed by using travel-influencing factors as input and task travel time as output of the RBF neural network, where the input/output dimension is determined dynamically. Secondly, the improved mayfly algorithm (IMA), a biomimetic metaheuristic method, is adopted to optimize the initial parameters of the RBF neural network, while a selective update strategy is designed for parameter updates. Finally, simulation experiments on model design, parameter initialization, and comparison with deep learning-based models are conducted in a complex assembly line scenario to validate the accuracy and efficiency of the proposed method. Full article

The history of the rabies virus dates back four millennia, with the virus being considered by many to be the first known transmitted between animals and humans. In Brazil, rabies virus variants associated with terrestrial wild animals, marmosets, and different bat species have been identified. In this study, bat samples from different regions of São Paulo State, in Southeast Brazil, were analyzed to identify their genetic variability and patterns. A total of 51 samples were collected over ten years (1999–2009) and submitted to the immunofluorescent technique using monoclonal antibodies for antigenic profile detection (the diagnostic routine used in Latin American countries) and genetic evolution analysis through maximum likelihood approaches. Three antigenic profiles were detected: one related to the rabies virus maintained by hematophagous bat populations (AgV3), part of the monoclonal antibody panel used, and two other profiles not included in the panel (called NC1 and NC2). These antigenic profiles were genetically distributed in five groups. Group I was related to hematophagous bats (AgV3), Groups II and III were related to insectivorous bats (NC1) and Groups IV and V were also related to insectivorous bats (NC2). The results presented herein show that genetic lineages previously restricted to the northwest region of São Paulo State are now found in other state regions, highlighting the need for a comprehensive genetic study of bat rabies covering geographic and temporal space, through expanded genomic analysis using a standard genomic fragment. Full article

Traditional deep reinforcement learning methods suffer from slow convergence speeds and poor adaptability in complex environments and are prone to falling into local optima in AGV system applications. To address these issues, in this paper, an adaptive path planning algorithm with an improved Deep Q Network algorithm called the B-PER DQN algorithm is proposed. Firstly, a dynamic temperature adjustment mechanism is constructed, and the temperature parameters in the Boltzmann strategy are adaptively adjusted by analyzing the change trend of the recent reward window. Next, the Priority experience replay mechanism is introduced to improve the training efficiency and task diversity through experience grading sampling and random obstacle configuration. Then, a refined multi-objective reward function is designed, combined with direction guidance, step punishment, and end point reward, to effectively guide the agent in learning an efficient path. Our experimental results show that, compared with other algorithms, the improved algorithm proposed in this paper achieves a higher success rate and faster convergence in the same environment and represents an efficient and adaptive solution for reinforcement learning for path planning in complex environments. Full article

This work presents the development of a functional real-time SLAM system designed to enhance the perception capabilities of an Automated Guided Vehicle (AGV) using only a 2D LiDAR sensor. The proposal aims to address recurring gaps in the literature, such as the need for low-complexity solutions that are independent of auxiliary sensors and capable of operating on embedded platforms with limited computational resources. The system integrates scan alignment techniques based on the Iterative Closest Point (ICP) algorithm. Experimental validation in a controlled environment indicated better performance using Gauss–Newton optimization and the point-to-plane metric, achieving pose estimation accuracy of 99.42%, 99.6%, and 99.99% in the position ($x$, $y$) and orientation ($\theta$) components, respectively. Subsequently, the system was adapted for operation with data from the onboard sensor, integrating a lightweight graphical interface for real-time visualization of scans, estimated pose, and the evolving map. Despite the moderate update rate, the system proved effective for robotic applications, enabling coherent localization and progressive environment mapping. The modular architecture developed allows for future extensions such as trajectory planning and control. The proposed solution provides a robust and adaptable foundation for mobile platforms, with potential applications in industrial automation, academic research, and education in mobile robotics. Full article

A green scheduling problem is proposed in this work, where both constraints on intermediate storage capacity and job transportation requirements are simultaneously considered. An improved discrete pathfinder algorithm (IDPFA) with multi-neighborhood local search is proposed to minimize the maximum completion time and total energy consumption. The algorithm addresses the green flow shop scheduling problem with limited buffers and automated guided vehicle (GFSSP_LBAGV). Firstly, based on the machine speed constraints, the transportation time for moving jobs by the automated guided vehicle (AGV) is incorporated to establish a mathematical model. Secondly, the core idea of the pathfinder algorithm (PFA) is applied to the evolutionary process of the discrete PFA, where three different crossover operations are used to replace the exploration process of the pathfinder, the influence of the pathfinder on the followers, and the mutual learning among the followers. Then, a multi-neighborhood local search is employed to conduct a detailed exploration of high-quality solution spaces. Finally, extensive standard test sets are used to verify the effectiveness of the proposed IDPFA in solving GFSSP_LBAGV. Full article

For complex assembly workshops, optimizing AGV scheduling is critical to enhancing production efficiency and resource utilization. Traditional scheduling methods often rely on fixed priority rules and basic path planning algorithms, which are insufficient to accommodate the dynamic changes in resource availability and task demands. To overcome these limitations, this study proposes a DES-based optimization approach that dynamically adjusts AGV task allocation and path planning to improve scheduling performance in complex manufacturing environments. By integrating lean production principles with intelligent simulation technologies, a comprehensive simulation model was developed using the Plant Simulation platform. This model simulates the coordination between AGVs and workstations while optimizing workstation layout and material flow. Simulation results demonstrate that the proposed approach significantly improves AGV scheduling efficiency and overall production performance. Notably, workstation utilization increased from below 6% to over 28%, while the work-in-progress rate dropped from 94% to under 74%. This study offers a practical and effective AGV scheduling strategy for complex product assembly workshops, with strong potential for real-world implementation. Full article

This study introduces an advanced automated guided vehicle (AGV) specifically designed for application in recirculating aquaculture systems (RASs). The proposed AGV seamlessly integrates automated feeding, real-time monitoring, and an intelligent path-planning system to enhance operational efficiency. To achieve optimal and adaptive navigation, a hybrid algorithm is developed, incorporating Newton–Raphson-based optimisation (NRBO) alongside ant colony optimisation (ACO). Additionally, dueling deep Q-networks (dueling DQNs) dynamically optimise critical parameters, thereby improving the algorithm’s adaptability to the complexities of RAS environments. Both simulation-based and real-world experiments substantiate the system’s effectiveness, demonstrating superior convergence speed, path quality, and overall operational efficiency compared to traditional methods. The findings of this study highlight the potential of AGV to enhance precision and sustainability in recirculating aquaculture management. Full article

Traditional supervisory control methods for the nonblocking control of discrete event systems often suffer from exponential computational complexity. Reinforcement learning-based approaches mitigate state explosion by sampling many random sequences instead of computing the synchronous product of multiple modular supervisors, but they struggle with limited reachable state spaces. A primary novelty of this study is to use the K-means clustering method for online inference with the learned state-action values. The clustering method divides all events at a state into the good group and the bad group. The events in the good group are allowed by the supervisor. The obtained supervisor policy can ensure both system constraints and larger control freedom compared to conventional RL-based supervisors. The proposed framework is validated by two case studies: an industrial transfer line (TL) system and an automated guided vehicle (AGV) system. In the TL case study, nonblocking reachable states increase from 56 to 72, while in the AGV case study, a substantial expansion from 481 to 3558 states is observed. Our new method achieves a balance between computational efficiency and nonblocking supervisory control. Full article

This paper presents the development of fleet management software for mobile robots, including AGV and AMR technologies, within the scope of a case study from the GreenAuto project. The system was designed to integrate position and status data from different robots, unifying this information into a single map. To achieve this, a web-based platform was developed to allow the simultaneous, real-time visualization of all robots in operation. However, the main challenge of this research lies in the heterogeneity of the fleet, which comprises robots of different makes and models from various manufacturers, each using distinct data formats. The proposed approach addresses this by facilitating fleet monitoring and management, ensuring a greater efficiency and coordination in the robot movement. The results demonstrate that the platform improves the traceability and operational supervision, promoting the optimized management of mobile robots. It is concluded that the proposed solution contributes to industrial automation by providing an intuitive and centralized interface, enabling future expansions for new functionalities and the integration with other emerging technologies. The proposed system demonstrated efficiency in updating and supervising operations, with an average latency of 120 ms for task status updates and an interface refresh rate of less than 1 s, enabling near real-time supervision and facilitating operational decision-making. Full article

Agricultural factories utilize advanced facilities and technologies to cultivate crops in a controlled environment, enhancing operational yields and reducing reliance on natural resources. This is crucial for ensuring a stable supply of agricultural products year-round and plays a significant role in the transformation of agricultural modernization. Automated Guided Vehicles (AGVs) are commonly employed in agricultural factories due to their low ownership costs and high efficiency. However, small embedded devices on AGVs face significant challenges in managing multiple tasks while maintaining the required timeliness. Multi-task learning (MTL) is increasingly employed to enhance the efficiency and performance of detection models in joint detection tasks, such as lane-line detection, pedestrian detection, and obstacle detection. The YOLOP (You Only Look for Panoptic Driving Perception) model demonstrates strong performance in simultaneously addressing these tasks; detecting lane lines in changeable agricultural factory scenarios is yet a challenging task, limiting the subsequent accurate planning and control of AGVs. This paper proposes a feedback-based network for joint detection tasks (MTNet) that simultaneously detects pedestrians, automated guided vehicles (AGVs), and QR codes, while also performing lane-line segmentation. This approach addresses the challenge faced by using embedded devices mounted on AGVs, which are unable to run multiple models for different tasks in parallel due to limited computational resources. For lane-line detection tasks, we also propose an improved YOLOP lane-line detection algorithm based on feature shift aggregation. Homemade datasets were used for training and testing. Comparative experiments of our model with different models in the target-detection and lane-line detection tasks, respectively, show the progressiveness of our model. Surprisingly, we also obtained a significant improvement in the model’s processing speed. Furthermore, we conducted ablation experiments to assess the effectiveness of our improvements in lane-line detection, all of which outperformed the original detection model. Full article

This paper presents a novel method for centralized robotic swarm control that integrates path planning and task allocation subsystems. A swarm of agents is managed using various evaluation methods to assess performance. A feedforward neural network was developed to assign tasks to swarm agents in real time by predicting a suitability score. For centralized swarm planning, a hybrid algorithm combining Rapidly Exploring Random Tree (RRT) and Artificial Potential Field (APF) planners was implemented, incorporating a Multi-Agent Pathfinding (MAPF) solution to resolve simultaneous collisions at intersections. Additionally, experimental hardware using differential-drive, ArUco-tracked agents was developed to refine and demonstrate the proposed control solution. This paper specifically focuses on the swarm system design for applications in swarm reconfigurable manufacturing systems. Therefore, performance was evaluated on tasks that resemble such processes. Full article

With the advancement of logistics technology, Automated Guided Vehicles (AGVs) have been widely adopted in manufacturing enterprises due to their high flexibility and stability, particularly in flexible and discrete manufacturing domains such as tire production and electronic assembly. However, existing studies seldom systematically consider practical constraints such as limited AGV transport resources, AGV charging requirements, and charging station capacity limitations. To address this gap, this paper proposes a flexible job shop production-logistics collaborative scheduling model that incorporates transport and charging constraints, aiming to minimize the maximum makespan. To solve this problem, an improved PPO algorithm—CRGPPO-TKL—has been developed, which integrates candidate probability ratio calculations and a dynamic clipping mechanism based on target KL divergence to enhance the exploration capability and stability during policy updates. Experimental results demonstrate that the proposed method outperforms composite dispatching rules and mainstream DRL methods across multiple scheduling scenarios, achieving an average improvement of 8.2% and 10.5% in makespan, respectively. Finally, sensitivity analysis verifies the robustness of the proposed method with respect to parameter combinations. Full article

This paper proposes a comprehensive Mixed-Integer Linear Programming (MILP) formulation for the simultaneous scheduling of machines and Automated Guided Vehicles (AGVs) within a partitioned Flexible Manufacturing System (FMS). The main objective is to numerically optimize the simultaneous scheduling of machines and AGVs while considering various workshop layouts and operational constraints. Three different workshop layouts are analyzed, with varying numbers of machines in partitioned workshop areas A and B, to evaluate the performance and effectiveness of the proposed model. The model is tested in multiple scenarios that combine different layouts with varying numbers of workpieces, followed by an extension to consider dynamic initial conditions in a more generalized MILP framework. Results demonstrate that the proposed MILP formulation efficiently generates globally optimal solutions and consistently outperforms a greedy algorithm enhanced by A*-inspired heuristics. Although computationally intensive for large scenarios, the MILP’s optimal results serve as an exact benchmark for evaluating faster heuristic methods. In addition, the study provides practical insight into the integration of AGVs in modern manufacturing systems, paving the way for more flexible and efficient production planning. The findings of this research are expected to contribute to the development of advanced scheduling strategies in automated manufacturing systems. Full article

In order to address the Distributed Displacement Flow Shop Scheduling Problem (DPFSP) with uncertain processing times in real production environments, Plant Simulation is employed to construct a simulation model for the MSRDPFSP. The model conducts quantitative analyses of workshop layout, assembly line design, worker status, operating status of robotic arms and AGV vehicles, and production system failure rates. A hybrid NEH-DDQN algorithm is integrated into the simulation model via a COM interface and DLL, where the NEH algorithm ensures the model maintains optimal performance during the early training phase. Four scheduling strategies are designed for workpiece allocation across different workshops. A deep neural network replaces the traditional Q-table for greedy selection among these four scheduling strategies, using each workshop’s completion time as a simplified state variable. This approach reduces algorithm training complexity by abstracting away intricate workpiece allocation details. Experimental comparisons show that for the data of 500 workpieces, the NEH algorithm in 3 s demonstrates equivalent quality to that produced by the GA algorithm in 300 s. After 2000 iterations, the DDQN algorithm achieves a 15% reduction in makespan with only a 2.5% increase in computational time compared to random search, this joint simulation system offers an efficient and stable solution for the modeling and optimization of the MSRDPFSP issue. Full article

This study presents the development, simulation, and hardware implementation of a 48 V, 1 kW mid-range wireless power transfer (WPT) platform for autonomous guided vehicle (AGV) charging in industrial applications. The system uses an LCC-S compensation topology, selected for its ability to maintain a constant output voltage and deliver high efficiency even under load variations at a typical coil distance of 15 cm. It can also operate at different distances by adjusting the compensator circuit. A proportional–integral (PI) controller is implemented for current regulation, offering a practical, low-cost solution well suited to industrial embedded systems. Compared to advanced control strategies, the PI controller provides sufficient accuracy with minimal computational demand, enabling reliable operation in real-world environments. Current adjustment can be dynamically carried out in response to real-time changes and continuously monitored based on the AGV battery’s state of charge (SOC). Simulation and experimental results validate the system’s performance, achieving over 80% efficiency and demonstrating its feasibility for scalable, robust AGV charging in Industry 4.0 Manufacturing Settings. Full article