Search Results (102)

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

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

With the ongoing advancements in automation technology, four-wheeled independent drive and steering (4WID-4WIS) automated guided vehicles (AGVs) are increasingly employed in intelligent logistics and warehousing systems. To enhance the performance of path tracking accuracy and cruising stability of AGVs, an automatic cruising methodology is proposed operating in complex environments. The approach integrates lateral control through model predictive control (MPC), which is optimized by a Long Short-Term Memory (LSTM) network, alongside fuzzy PID control for longitudinal management. By utilizing the LSTM network for trajectory prediction, the system can anticipate future vehicle states and outputs, thereby facilitating proactive adjustments that enhance the performance of the MPC lateral controller and improve both trajectory tracking accuracy and response speed. Concurrently, the fuzzy PID control strategy for longitudinal management increases the system’s adaptability to dynamic environments. The proposed methodology has been demonstrated in a physical prototype operating in real practical environments. Comparative results demonstrate that the LSTM-MPC significantly outperforms conventional MPC in lateral control accuracy. Additionally, the fuzzy PID controller yields superior longitudinal performance compared to traditional dual-PID and constant-speed strategies. This advantage is particularly evident in curved path segments, where the proposed fuzzy PID–LSTM–MPC framework achieves significantly higher lateral and longitudinal tracking accuracy compared to other control strategies. Full article

This study presents an on-line intelligent lubrication system utilizing specialty grease to address lubricant loss and uneven coating issues in traditional methods. Characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FT-IR), the specialty grease demonstrates superior tribological performance, achieving a 46.7% reduction in the average friction coefficient and 33.3% smaller wear scar diameter under a 392 N load compared to conventional lubricants. The system features an automatic control vehicle design integrating heating, grease supply, lubrication-scraping mechanisms, and a dual closed-loop intelligent control system combining PID-based temperature regulation with machine vision. Experiments identified 50 °C as the optimal heating temperature. Kinematic modeling and grease consumption analysis guided greasing parameters optimization, validated through simulations and practical tests. Evaluated on a 20 m long, 36.5 mm diameter wire rope, the system achieved full coverage within 60 s, forming a uniform lubricant layer of 0.3–1.0 mm thickness (±0.15 mm deviation). It realizes the innovative application of high-adhesion lubricating grease, adaptive process control, and real-time thickness feedback technology, significantly improving the lubrication effect, reducing maintenance costs, and extending the lifespan of the wire rope. This provides intelligent lubrication technology support for the reliable operation of wire ropes in industrial fields. Full article

With the development of artificial intelligence technology, automatic guided vehicle (AGV) path planning is widely used in many fields. Aiming at the problems of low convergence efficiency and easy to fall into local optimization of the traditional ant colony algorithm, this paper proposes an AGV path-planning method based on the artificial potential field-ant colony algorithm. The performance of the algorithm is improved by incorporating the artificial potential field attraction to construct the potential field heuristic function, dynamically adjusting the pheromone volatility coefficient, introducing multiple parameters to dynamically adjust the pheromone increment, and optimizing the path by using the pruning method and other improvement measures. The simulation experiments in 20 × 20 and 30 × 30 grid environments show that the improved algorithm already has significant advantages over the traditional algorithm and other improved ACO algorithms in terms of path length, convergence speed and the number of path inflection points, verifying its high efficiency and stability, and providing a better solution for AGV path planning. Full article

Unmanned Aerial Vehicles (UAVs), as widely used tools, can achieve better efficiency when integrated into a multi-UAV system than individual, dispersed units. Obstacle avoidance and formation control are fundamental requirements for such systems. The Boids algorithm, a biomimetic model suitable for swarming, serves as the foundation for this study. This paper proposes a novel integrated algorithm based on Boids that can be applied to multi-UAV systems for obstacle avoidance and formation control. The algorithm enables the multi-UAV system to automatically form formations, autonomously avoid obstacles, and recover formations rapidly. In this algorithm, each UAV functions as an agent within the system that is capable of independently collecting and sharing information. Each agent can make independent decisions to enter either the formation mode or the obstacle avoidance mode based on external environmental factors. The formation mode utilizes the virtual structure method to guide UAVs to their virtual formation positions. In the obstacle avoidance mode, the artificial potential field method is employed to ensure that each UAV maintains a safe distance from other UAVs that pose collision risks and various complex obstacles, regardless of their number. Simulation experiments were conducted on the Unity platform, varying the number of UAVs and the formation shapes. The results verified that the algorithm operates correctly, stably, and in a timely manner, demonstrating good performance. Full article

With the development of Industry 4.0, discrete manufacturing systems are accelerating their transformation toward flexibility and intelligence to meet the market demand for various products and small-batch production. The flexible flow shop (FFS) paradigm enhances production flexibility, but existing studies often address FFS scheduling and automated guided vehicle (AGV) path planning separately, resulting in resource competition conflicts, such as equipment idle time and AGV congestion, which prolong the manufacturing cycle time and reduce system energy efficiency. To solve this problem, this study proposes an integrated production–transportation scheduling framework (FFSP-AGV). By using the adjacent sequence modeling idea, a mixed-integer linear programming (MILP) model is established, which takes into account the constraints of the production process and AGV transportation task conflicts with the aim of minimizing the makespan and improving overall operational efficiency. Systematic evaluations are carried out on multiple test instances of different scales using the CPLEX solver. The results show that, for small-scale instances (job count ≤10), the MILP model can generate optimal scheduling solutions within a practical computation time (several minutes). Moreover, it is found that there is a significant marginal diminishing effect between AGV quantity and makespan reduction. Once the number of AGVs exceeds 60% of the parallel equipment capacity, their incremental contribution to cycle time reduction becomes much smaller. However, the computational complexity of the model increases exponentially with the number of jobs, making it slightly impractical for large-scale problems (job count > 20). This research highlights the importance of integrated production–transportation scheduling for reducing manufacturing cycle time and reveals a threshold effect in AGV resource allocation, providing a theoretical basis for collaborative optimization in smart factories. Full article

Automatic navigation, as one of the modern technologies in farming automation, enables unmanned driving and operation of agricultural vehicles. In this research, the ZPTM (Zigzag Path Tracking Method) was proposed to reduce the complexity of path planning by using a point cloud consisting of a series of anchor points with spatial information, which are obtained from orthophotos taken by UAVs (Unmanned Aerial Vehicles) to represent the curved path in the zigzag. A local straight path was created by linking two adjacent anchor points, forming the local target path to be tracked, which simplified the navigation algorithm for zigzag path tracking. A nonlinear feedback function was established, using both lateral and heading errors as inputs for determining the desired heading angle of agricultural vehicles, which were guided along the local target path with minimal errors. A GUI (Graphic User Interface) was designed on the navigation terminal to visualize and monitor the working process of agricultural vehicles in automatic navigation, displaying interactive controls and components, including representations of the zigzag path and the agricultural vehicle using affine transformation. A high-clearance sprayer equipped with an automatic navigation system was utilized as the test platform to evaluate the proposed ZPTM. Zigzag navigation tests were conducted to explore the impact of path tracking parameters, including path curvature, moving speed, and spacing between anchor points, on zigzag navigation performance. Based on these tests, a regression model was established to optimize these parameters for achieving accurate and smooth movement. Field test results showed that the maximum error, average error, and RMS (Root Mean Square) error in the zigzag navigation were 3.30 cm, 2.04 cm, and 2.27 cm, respectively. These results indicate that the point cloud path-based ZPTM in this research demonstrates adequate stability, accuracy, and applicability in zigzag navigation. Full article

With the widespread use of autonomous guided vehicles (AGVs), avoiding collisions has become a challenging problem. Addressing the issue is not straightforward since production efficiency, collision avoidance, and energy consumption are conflicting factors. This paper proposes a novel edge computing method based on vehicle edge intelligence to solve the energy-efficient collision-free machine/AGV scheduling problem. First, a vehicle edge intelligence architecture was built, and the corresponding state transition diagrams for collision-free scheduling were developed. Second, the energy-efficient collision-free machine/AGV scheduling problem was modeled as a multi-objective function with electric capacity constraints, where production efficiency, collision prevention, and energy conservation were comprehensively considered. Third, an artificial plant community algorithm was explored based on the edge intelligence of AGVs. The proposed method utilizes a heuristic search and the swarm intelligence of multiple AGVs to realize energy-efficient collision-free scheduling and is suitable for deploying on embedded platforms for edge computing. Finally, a benchmark dataset was developed, and some benchmark experiments were conducted, where the results revealed that the proposed heuristic method could effectively instruct multiple automatic guided vehicles to avoid collisions with high energy efficiency. Full article

The accurate counting of passengers in public transport systems is crucial for optimizing operations, improving service quality, and planning infrastructure. It can also contribute to reducing the number of public transport lines where a high number of vehicles is not needed in certain periods during the year, but also by increasing the number of lines where the need is increased. This paper provides a comprehensive review of current methodologies and technologies used for passenger counting, without the actual implementation of the automatic passenger counting system (APC), but with a proposal based on image processing and machine learning techniques and concepts, since it represents one of the most used approaches. The research explores various technologies and algorithms, like card swiping, infrared, weight and ultrasonic sensors, RFID, Wi-Fi, Bluetooth, LiDAR, thermos cameras, including CCTV cameras and traditional computer vision methods, and advanced deep learning approaches, highlighting their strengths and limitations. By analyzing recent advancements and case studies, this review aims to offer insights into the effectiveness, scalability, and practicality of different passenger counting solutions and offers a solution proposal. The research also analyzed the current General Data Protection Regulation (GDPR) that applies to the European Union and how it affects the use of systems like this. Future research directions and potential areas for technological innovation are also discussed to guide further developments in this field. Full article

The traditional A* algorithm faces the challenges of low search efficiency and large node extension range in the field of path planning. These directly restrict the overall performance of the algorithm. In this study, we aimed to improve the search efficiency and path planning quality of the A* algorithm in complex and large-scale environments through a series of optimisation measures, including the innovation of weight design, flexible adjustment of the search neighbourhood, improvement of the heuristic function, and optimisation of the node selection strategy. Specifically, this study innovatively introduces the local obstacle rate as the core index of weight design, and it dynamically adjusts the weights according to the change of the obstacle rate during the node movement process, which effectively reduces the search space and significantly improves the search speed. At the same time, according to the real-time change of the local obstacle rate, this study dynamically adjusts the range of the search neighbourhood, so that the algorithm can choose the optimal search strategy according to different environmental information. In terms of the improvement of the heuristic function, this study adopted the diagonal distance as the benchmark for cost estimation, and it innovatively introduces the angle coefficient to reflect the complexity of path turning, thus providing the algorithm with a more accurate guidance for the search direction. In addition, this study optimises the node selection method by drawing on the idea of simulated annealing, which eliminates the need to calculate and compare all possible surrogate values during the node selection process, thus significantly reducing the running time of the algorithm. The results of the simulation experiments fully verify the effectiveness and practicality of the improved algorithm. Compared with the traditional A* algorithm, the improved algorithm achieved significant optimisation in terms of the average running time, the number of expansion nodes, and the path length, with the average running time shortened by 84%, the number of expansion nodes reduced by 94%, and the path length also shortened by 2.3%. Full article

Automated guided vehicles (AGVs) play a critical role in indoor environments, where battery endurance and reliable recharging are essential. This study proposes a multi-sensor fusion approach that integrates LiDAR, depth cameras, and infrared sensors to address challenges in autonomous navigation and automatic recharging. The proposed system overcomes the limitations of LiDAR’s blind spots in near-field detection and the restricted range of vision-based navigation. By combining LiDAR for precise long-distance measurements, depth cameras for enhanced close-range visual positioning, and infrared sensors for accurate docking, the AGV’s ability to locate and autonomously connect to charging stations is significantly improved. Experimental results show a 25% increase in docking success rate (from 70% with LiDAR-only to 95%) and a 70% decrease in docking error (from 10 cm to 3 cm). These improvements demonstrate the effectiveness of the proposed sensor fusion method, ensuring more reliable, efficient, and precise operations for AGVs in complex indoor environments. Full article

As an important part of urban terminal delivery, automated guided vehicles (AGVs) have been widely used in the field of takeout delivery. Due to the real-time generation of takeout orders, the delivery system is required to be extremely dynamic, so the AGV needs to be dynamically scheduled. At the same time, the uncertainty in the delivery process (such as the meal preparation time) further increases the complexity and difficulty of AGV scheduling. Considering the influence of these two factors, the method of embedding a stochastic programming model into a rolling mechanism is adopted to optimize the AGV delivery routing. Specifically, to handle real-time orders under dynamic demand, an optimization mechanism based on a rolling scheduling framework is proposed, which allows the AGV’s route to be continuously updated. Unlike most VRP models, an open chain structure is used to describe the dynamic delivery path of AGVs. In order to deal with the impact of uncertain meal preparation time on route planning, a stochastic programming model is formulated with the purpose of minimizing the expected order timeout rate and the total customer waiting time. In addition, an effective path merging strategy and after-effects strategy are also considered in the model. In order to solve the proposed mathematical programming model, a multi-objective optimization algorithm based on a NSGA-III framework is developed. Finally, a series of experimental results demonstrate the effectiveness and superiority of the proposed model and algorithm. Full article

Automatic Guided Vehicles (AGVs) are widely used in flexible manufacturing systems for material handling inside the factory. Traffic management strategies, required to guarantee a conflict-free operation of the overall fleet, discretize the workspace of the AGVs and use the resulting graph model for route planning and execution. In zone control approaches, AGVs move from node to node on a permit basis, with limitations on the allowed number of AGVs at a time in each area of the graph to prevent and/or resolve deadlocks and conflicts. Hence, for an optimal implementation of traffic controllers in real manufacturing systems, it is essential to understand how the layout discretization influences the performance of the AGV network. This paper analyzes its effect in grid-like shaped workspaces by using a representative zone control algorithm and a recently developed improvement of it. Realistic numerical experiments on different layouts reveal that denser discretizations do not yield faster executions or increase in throughput, while lower control periods in the permit system entail significant performance uplifts. Full article

With the rapid development of the economy and the continuous improvement of people’s living standards, the printing and packaging industry plays an increasingly important role in people’s lives. The traditional printing industry is a discrete manufacturing industry, relying on a large amount of manpower and manual operation, low production efficiency, higher labor costs, wasting of resources, and other issues, so the realization of printing factory intelligence to improve the competitiveness of the industry is an important initiative. Automatic guided vehicles (AGVs) are an important part of an intelligent factory, serving the function of automatic transportation of materials and products. To optimize the movement paths of AGVs, enhance safety, and improve transportation efficiency and productivity, this paper proposes an alternative implementation of the A* algorithm. The proposed algorithm improves search efficiency and path smoothness by incorporating the grid obstacle rate and enhancing the heuristic function within the A* algorithm’s evaluation function. This introduces the evaluation subfunction of the nearest distance between the AGV, the known obstacle, and the unknown obstacle in the global path in the dynamic window approach (DWA algorithm), and reduces the interference of obstacles with the AGV in global path planning. Finally, the two improved algorithms are combined into a new fusion algorithm. The experimental results show that the search efficiency of the fusion algorithm significantly improved and the transportation time shortened. The path smoothness significantly improved, and the closest distance to obstacles increased, reducing the risk of collision. It can thus effectively improve the productivity of an intelligent printing factory and enhance its flexibility. Full article