Search Results (410)

This study investigates the operations in automated container terminals, in which scheduling decisions need to be generated automatically. Therefore, an integrated optimization model is established considering the two-way transportation process of containers between the berth and the yard with the assignment and scheduling of berths, quay cranes, and automated guided vehicles (AGVs). In particular, a buffer zone is added at the interface between the berth and the  GV routing area. The mathematical model is divided into two stages, in which the first stage deals  with the arrangement of vessels and quay cranes, while the second stage assigns container transport tasks to AGVs. Furthermore, a coordinated mechanism with an elite solution pool is introduced to coordinate the seaside scheduling and AGV transportation decisions. To solve the first stage, an adaptive large neighborhood search (ALNS) enhanced by reinforcement learning is proposed. The reinforcement learning strategy updates the selection rule for destroy-and-repair operator pairs. For the second stage, the AGV transportation environment is modeled as a directed graph, encoding the road network, the AGV interaction relationships, and the task states using a graph neural network (GNN). The AGVs are divided into several groups, mainly according to the initial locations, and each group serves nearby tasks. Therefore, multi-agent proximal policy optimization (MAPPO) is introduced with the GNN to arrange the AGV tasks. The proposed models and algorithms are evaluated through computational experiments with different problem scales, demonstrating better performance compared with the selected baseline methods under the tested settings. Full article

Automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) are among the key enabling technologies driving intelligent logistics and industrial automation. Despite their widespread adoption and rapid technological evolution, the literature often addresses AGV and AMR systems in a fragmented manner, lacking a structured methodological perspective that highlights their architectural foundations, levels of autonomy, and technological maturity. This paper presents a methodological survey of AGV and AMR technologies, focusing on system-level architectures and core functional components rather than isolated algorithms. The survey systematically analyzes key technological dimensions, including sensing and perception, localization and positioning strategies, navigation and path-planning approaches, communication infrastructures, and multi-robot coordination mechanisms. A clear distinction is drawn between classical AGV systems, which rely on fixed infrastructure and predefined routes, and AMR systems, which exhibit adaptive, perception-driven, and self-configuring behaviors enabled by artificial intelligence techniques. Rather than proposing new algorithms, this paper organizes existing approaches into a coherent framework that highlights technological transitions from infrastructure-dependent guidance to autonomous, data-driven navigation. Recent trends such as cloud–edge integration, learning-based navigation, scalable fleet management architectures, and cooperative multi-robot systems are reviewed and discussed from a methodological standpoint, emphasizing their role in increasing flexibility, robustness, and operational efficiency in industrial and logistics environments. The survey also addresses cross-cutting challenges, including system transparency, safety and certification, interoperability, and sustainability. Finally, this paper outlines research directions aligned with the principles of Industry 5.0, highlighting the need for human-centered, resilient, and scalable AMR and AGV systems capable of safe and explainable operation in complex industrial contexts. Full article

Real-time LiDAR semantic segmentation for industrial AGVs must distinguish repeated structures and weak glass returns while remaining robust to sensor-dependent intensity and tight edge computing budgets. We introduce DS-RangeNet, a lightweight range image network that processes geometry and material-sensitive intensity in separate streams. The geometry stream uses voxel-PCA descriptors, while the intensity stream uses normalized range, local intensity statistics, boundary strength, and intensity curvature. A lightweight convolutional attention block handles shallow fusion, whereas intensity–geometry cross-attention (IGCA) links deep features by estimating affinity within the guiding stream and routing values from the other stream. Centered kernel alignment (CKA) and normalized cross-covariance reveal weak similarity after separate encoding and progressively stronger alignment during fusion. On the site disjoint UBPC-9 test split, DS-RangeNet reaches 73.2% mIoU with 5.69 M parameters and 37 ms end-to-end latency on Jetson AGX Orin. A nine-fold leave-one-environment-out evaluation obtains 71.0% mIoU. The evaluation further spans SemanticPOSS and SemanticKITTI, five random seeds, 21 corruption conditions across seven families, standard cross-attention and convolution controls, a 60 min Jetson run, and cross-sensor transfer. Full article

Pavement cracks pose significant challenges to the driving stability and operational safety of automated guided vehicles (AGV) in industrial and logistics environments. The complex geometry of cracks and their nonlinear coupling with vehicle dynamics make conventional rule-based or single-modal approaches insufficient for reliable engineering decision-making. To address this issue, this study proposes an artificial intelligence–driven multi-modal perception and engineering analysis framework for AGV speed optimization under representative operating conditions. From the artificial intelligence perspective, an improved lightweight instance segmentation model based on YOLO11 is developed by integrating a dynamic upsampling strategy, a hybrid multi-scale feature representation module, and a large-kernel attention mechanism, enabling robust and fine-grained crack extraction in complex pavement scenes. In addition, a multi-modal learning strategy is adopted to fuse two-dimensional visual features with three-dimensional point cloud-derived geometric parameters, allowing accurate quantification of crack width, depth, and surface damage. From the engineering analysis perspective, the relationship between AI-extracted crack geometric characteristics and AGV dynamic responses is established to investigate the influence of pavement defects on vehicle vibration behavior. A triaxial vibration acquisition system is constructed under controlled experimental conditions, and the relationship between crack severity, vibration characteristics, and driving speed is quantitatively analyzed. Based on vibration constraints, a hierarchical speed optimization strategy is formulated for different crack levels. Experimental results demonstrate that the proposed method achieves accurate crack perception and effective geometric feature characterization, providing a quantitative basis for AGV speed adjustment under different pavement conditions. Full article

Automated Guided Vehicles (AGVs) are a key part of today’s industrial automation, especially for material handling and intralogistics operations. The conventional AGV designs, such as differential-drive vehicles and fixed-steering AGVs, tend to exhibit limited maneuverability in restricted spaces. Such behavior can be attributed to comparatively large turning radii in classic designs, which limit the possibility of efficient movement. Thus, the production of affordable AGVs with high motion flexibility and load stability remains a challenge in AGV development. To resolve this issue, PID-controlled reverse-phase steering method is suggested. Experimental evaluation with 12 trials demonstrated a decreased turning radius for the designed AGV from 1.5 ± 0.08 m (literature-reported value) to 0.84 ± 0.05 m (current study finding), corresponding to an approximately 46.7% reduction. Results demonstrate the proposed AGV’s improved cornering capabilities. In addition, the lateral deviation achieved from the designed AGV stands at an average of 3.1 ± 0.5 cm, while the Root Mean Square Error (RMSE) is 3.5 cm, resulting in an overall accuracy rate of 96% ± 1.2%. Obstacle avoidance tests confirm successful performance within an obstacle range of up to 80 cm. Overall, the developed AGV represents a scalable and economical system for intelligent material handling within the industrial environment. Full article

Urban low-altitude Unmanned Aerial Vehicles (UAVs) often require intermediate recharging during long-duration missions, and mobile Automated Guided Vehicles (AGVs) provide a flexible ground-side recharging solution in obstacle-constrained urban environments. Although AGV–UAV wireless energy coordination frameworks exist, appropriate matching cannot rely solely on nearest-distance or maximum-energy rules, as flight energy consumption, reachable safety margins, post-charge task feasibility, and low-altitude trajectory executability are tightly coupled. To address this issue, this paper proposes an energy-aware AGV matching method for efficient UAV recharging. For each candidate AGV, a feasible trajectory is generated under obstacle avoidance and altitude constraints, and the corresponding flight energy consumption, arrival energy, received charging energy, and post-charge available energy are quantitatively evaluated. A post-charge energy threshold is introduced to filter out AGVs that are reachable but unable to support subsequent mission execution after recharging. In addition, altitude regularization is employed to suppress excessive vertical maneuvers and improve the practicality of low-altitude trajectories. The proposed method is evaluated using three representative scenarios and three randomly generated scenarios against four baseline approaches. Results show that this method provides more reliable and mission-consistent decisions, avoids inefficient charging choices, and substantially reduces vertical motion. Full article

Flexible job shop scheduling with multiple automated guided vehicles (FJSP-AGV) is a challenging production scheduling problem in intelligent manufacturing, where operation sequencing, machine assignment, AGV allocation, and transportation decisions are tightly coupled. Existing exact and meta-heuristic methods can obtain high-quality solutions, but they usually require considerable computational time for large-scale instances. Meanwhile, conventional dispatching rules can make fast decisions but often fail to capture the complex interactions among operations, machines, and AGVs. To address these challenges, this paper proposes an end-to-end deep reinforcement learning framework based on heterogeneous graph neural networks for solving FJSP-AGV. Specifically, a heterogeneous graph is constructed to represent the scheduling state, where operations, machines, and AGVs are modeled as different types of nodes, and their relationships are described by operation–machine and operation–AGV arcs. Based on this representation, a heterogeneous graph neural network is developed to extract scheduling information from different production resources. In particular, a meta-path aggregation mechanism is introduced to capture the complex interaction patterns among operations, machines, and AGVs. The proximal policy optimization algorithm is then employed to train the scheduling policy in an end-to-end manner. Experimental results on public benchmark instances and real-world cases demonstrate that the proposed method outperforms composite heuristic rules and achieves a favorable balance between solution quality and computational efficiency compared with existing state-of-the-art methods. These results indicate that the proposed HGNN-DRL framework is effective for fast and intelligent scheduling decision-making in FJSP-AGV environments. Full article

This paper proposes a structured methodology for constructing a digital twin (DT) of a planned flexible manufacturing system (FMS) for gear wheel production, integrating value stream mapping (VSM), discrete-event simulation (DES), production scheduling, and CAD/CAM modeling into a hierarchical, iterative design framework. The methodology was validated on an industrial case study involving a Polish manufacturer of gear transmissions undergoing modernization from conventional machining to a fully automated system incorporating CNC machining centers, industrial robots, automated guided vehicles (AGVs), and an automated storage and retrieval system (ASRS). Production scheduling was performed using eight algorithms including a random-search method and an ant colony optimization (ACO) algorithm, applied to a representative of 1072 parts across 10 gear wheel types. Simulation models of both conventional and automated systems were developed in FlexSim 2024. The best random instance achieved a makespan of 72,317 s in the automated model, compared to 75,634 s for the list rule—a 4.4% improvement that corresponds to approximately 55 min of production time per batch. AGV fleet sizing experiments identified four vehicles as the optimal configuration, beyond which marginal gains fall below 2%. The proposed digital twin framework enables virtual commissioning, continuous production planning, and what-if analysis prior to and during physical system implementation. Full article

Automated guided vehicles (AGVs) are key transportation resources in automated container terminals, where operational scheduling and charging decisions exhibit strong spatiotemporal coupling characteristics. When AGVs are assigned to transport “reefer” containers, interruptions in external power supply during transit may lead to temperature fluctuations, posing potential risks to cargo quality and transportation safety. To address this issue, this paper proposes a two-stage coordinated optimization framework for AGV operations and charging, considering reefer container temperature constraints. Specifically, an AGV transportation scheduling model is first developed to characterize quay-crane operations, yard allocation, AGV travel processes, battery dynamics, and reefer container transit-time limitations associated with temperature maintenance requirements. Subsequently, a port microgrid scheduling model integrating charging stations, photovoltaic generation, wind power, and energy storage systems is established to coordinate AGV charging strategies with energy system operations. Based on these models, a two-stage optimization framework is constructed, in which AGV task assignment and yard allocation are optimized in the first stage to improve operational efficiency, while energy scheduling is optimized in the second stage to minimize system operating costs under the operational decisions obtained in the first stage. Numerical results demonstrate that the proposed method effectively reduces the transportation time of reefer containers, alleviates temperature-related transportation risks, enhances the coordination between logistics operations and energy management, and improves terminal operational efficiency while ensuring the safety and quality of reefer container transportation. The proposed framework provides an effective solution for the integrated optimization of logistics and energy systems in automated container terminals. Full article

Agriculture 5.0 represents a breakthrough transformation from Agriculture 4.0, addressing the needs for safety, sustainability, and resilience in human–robot collaboration in agriculture. By integrating technologies such as artificial intelligence, digital twins, and big data, it achieves breakthroughs in three areas: perception, cognition, and execution. As carriers of agricultural technology, agricultural automated guided vehicles (AGVs) are an indispensable part of agricultural activities and play a crucial role in agricultural production. In the actual operation of AGVs, intelligent perception is a core technological prerequisite for enabling the communication and interaction among machines, humans, and the environment. However, due to the complexity and variability of agricultural environments, intelligent perception technology remains a highly challenging task. While existing reviews have focused on isolated aspects of agricultural automation, a comprehensive synthesis of intelligent perception technologies for agricultural AGVs within the holistic, human-centric framework of Agriculture 5.0 is notably lacking. This review bridges this gap by systematically analyzing and comprehensively reviewing recent advances in intelligent perception for agricultural AGVs, covering multi-sensor technologies, visual perception and target recognition, positioning and navigation algorithms, as well as applications such as path planning, multi-robot coordination, and human–robot collaboration. Furthermore, this paper delves into the potential challenges and future development trends of intelligent perception technology in the context of Agriculture 5.0, highlighting the transformative potential of these technologies in promoting multimodal fusion and addressing safety issues in human–robot collaboration. In the context of Agriculture 5.0, with the continuous advancement of intelligent perception technologies and the ongoing improvement of agricultural intelligent equipment, a human-centered, highly sustainable, resilient, data-driven agricultural production system will ultimately be formed in the future. Full article

Cube-shaped automated storage and retrieval systems (AS/RS) enhance storage density by organizing inventory in a three-dimensional grid. However, they face two operational bottlenecks: (1) digging—the temporary removal and restacking of upper bins to access a target bin—and (2) inefficient idle staging and return policies in multi-AGV operations. We proposed a demand-based digging and bin-placement strategy and a waiting-point (staging) selection policy that considers AGV positions and remaining task times. These control policies are implemented in both rule-based and multi-agent reinforcement learning (MARL) variants. Their performance is evaluated using a Discrete Event System Specification (DEVS) simulation framework. In a 30 × 30 × 4 grid, three experiments demonstrated that deploying five AGVs achieved the best performance within the tested configuration; the demand-based digging and placement strategy achieved a 6.2% reduction in makespan, and the rule-based and MARL staging policies achieved additional reductions of 2.5% and 1.1%, respectively. These results highlight the benefits of jointly optimizing digging and multi-AGV staging and provide practical guidance for control-policy design in cube-shaped AS/RS. Full article

Over the last decades, the rapid growth of e-commerce has increased the scale and operational intensity of warehouse systems. Consequently, sustainability in warehouse operations has become increasingly important due to rising energy consumption and environmental concerns. As an answer to this growth, Automated Guided Vehicles (AGVs) are utilized more, directly affecting energy usage and operational efficiency. This study develops a sustainability-oriented optimization framework for AGV-assisted order picking in warehouses with random storage and multiple products per location. The framework includes five mathematical models prioritizing one or more environmental (distance minimization), economic (AGV utilization), and social (workload balancing) sustainability perspectives. A generalized recursive matheuristic algorithm based on iterative cut generation is developed for the first model. A real-life dataset is used to evaluate the proposed approaches. Results reveal substantially different outcomes from different sustainability perspectives. The “Compact Clustering Model” achieved the best environmental performance, reducing total travel distance and normalized energy consumption by approximately 27–29% compared with the baseline formulation. From an economic sustainability perspective, several formulations achieved 92–100% AGV utilization, while social sustainability indicators showed load variability ranging from approximately 5.1 to 13.1. Overall, the findings demonstrate that different formulations provide distinct sustainability advantages in AGV-assisted warehouse systems. Full article

Efficient AGV/AMR path planning is essential for intelligent warehouse logistics, where regular shelves, narrow aisles, local bottlenecks, and heterogeneous obstacles strongly affect roadmap quality. This study proposes LE-HG-PRM, a structure-aware extension of heuristic-guided probabilistic roadmap planning. The method embeds warehouse geometric priors into probability-field sampling, region-adaptive neighborhood connection, and cache-accelerated progressive path refinement. Compared with the preliminary conference version, the journal version introduces a redesigned warehouse-oriented planning framework and substantially expands the experimental validation. Four experimental campaigns are conducted, covering static-complexity progression, corridor-width sensitivity, parameter sensitivity, and map-scale expansion, with A*, JPS, PRM, RRT, RRT*, and HG-PRM as baselines. Each scenario uses 50 paired start–goal tasks, and sampling-based methods are repeated with 12 independent random seeds. The results show that LE-HG-PRM provides competitive path quality and structurally regular paths in representative warehouse layouts. Statistical tests further confirm that its path-length advantage is scenario-dependent but significant in several structured and bottleneck-constrained settings. The findings suggest that incorporating explicit warehouse-structure priors can improve roadmap-based global planning for intelligent logistics, while future work should validate the method in Gazebo and physical AGV/AMR platforms. Full article

Reliable indoor localization is required for automated guided vehicles (AGVs), robot validation, and industrial digital-twin applications, but ultrasonic positioning can degrade sharply when acoustic visibility changes. This paper evaluates Marvelmind Super-Beacon localization in controlled laboratory experiments involving both AGV tracking and UR10 robot-arm positioning. The non-inverse architecture (NIA) and inverse architecture (IA) configurations are included as parallel validation scenarios to assess the robustness of the proposed mitigation framework across different Marvelmind deployment modes. The baseline analysis identifies the dominant acoustic failure modes, including multipath-induced scatter, crossover-zone handover jumps, update-rate degradation, complete non-line-of-sight (NLoS) outages, and height-dependent 3D jitter. To mitigate these effects, an embedded ultrasonic–inertial pipeline is implemented on an ESP32-S3-WROOM-1 module. The system combines UART packet validation, interrupt-driven ICM-20948 inertial acquisition at 500 Hz, sliding-window kinematic outlier rejection, and a 15-state error-state Kalman filter (ESKF). The embedded estimator logic is designed to maintain motion continuity during intermittent or corrupted acoustic positioning while reintroducing validated ultrasonic absolute corrections. Using recorded AGV and UR10 datasets, mitigation performance was quantitatively assessed through a firmware-consistent replay of the recorded measurements, using the same gating, inertial propagation, and measurement-update logic as the real-time ESP32-S3 implementation. Across ten trials per configuration, the replay-based trial-mean RMSE in the 2D AGV scenarios decreased from 101.2–104.1 mm for raw ultrasonic data to 47.2–48.7 mm after fusion, while peak failure-interval errors were reduced by 64.2–65.7%. In the 3D UR10 scenarios, replay-based trial-mean RMSE decreased from 157.6–158.4 mm to 80.2–80.5 mm, and peak height-sensitive 3D errors were reduced by 58.8–60.0%. The results demonstrate the feasibility of embedded ultrasonic–inertial robustness enhancement for localization in controlled laboratory AGV and robot-arm scenarios. While the proposed approach shows promising performance under the investigated conditions, further validation is required before extending the conclusions to larger-scale and dynamically changing industrial environments. Full closed-loop online robot localization and control based directly on the fused localization output remain subjects for future investigation. Full article

Multi-target path planning for Automated Guided Vehicle (AGV) in complex logistics environments requires balancing planning efficiency, obstacle avoidance capability, and trajectory smoothness. To address these challenges, this paper proposes a bi-level collaborative framework integrating Particle Swarm Optimization (PSO) with the Bidirectional Rapidly Exploring Random Tree (Bi-RRT). The framework unifies adaptive sampling, online parameter optimization, and trajectory smoothing within a single planning architecture. Specifically, the framework constructs a five-dimensional particle encoding that includes the expansion step size and multi-level strategy switching thresholds. During the Bi-RRT expansion process, an expansion-failure-driven adaptive sampling mechanism is introduced to enhance search performance in cluttered environments, while local-density-based suppression and directional dispersion are employed to reduce redundant exploration. In addition, a lightweight PSO-based monitoring mechanism enables online adaptive parameter adjustment. For multi-target scheduling, a greedy heuristic based on a hybrid weighted graph determines the visitation sequence. Trajectory smoothness is further improved using cubic B-spline interpolation combined with bounded perturbation optimization. Experimental results demonstrate that the proposed framework improves planning efficiency while maintaining stable performance across environments with different obstacle densities. These results demonstrate the effectiveness of the proposed framework for multi-target AGV path planning in complex warehouse environments. Full article