Search Results (51)

Overhead cranes are underactuated systems with significant model uncertainties that pose major challenges for precise anti-swing control. These uncertainties, including unknown parameters and varying dynamics, severely limit the performance of conventional controllers. To address the control challenge of 7-degree-of-freedom (7-DOF) overhead cranes with variable cable length and double spherical pendulum dynamics, this paper proposes an adaptive sliding mode control method integrated with time-delay estimation. First, a comprehensive dynamic model that accounts for bridge movement, trolley travel, hoisting motion, and spherical swings of both the hook and the payload is established. Then, a sliding surface is constructed based on the coupling analysis between actuated and unactuated dynamics. The core innovation lies in the integration of time-delay estimation with adaptive sliding mode control, where the time-delay estimator provides accurate approximation of unknown system dynamics, while the adaptive mechanism compensates for estimation errors and parameter variations. This dual approach ensures robust performance despite model inaccuracies. Lyapunov stability analysis rigorously confirms the uniform ultimate boundedness of all closed-loop signals under model uncertainties. Experimental tests further show that the designed controller achieves accurate positioning and robust swing suppression, outperforming conventional controllers in challenging working conditions. Full article

Distributed hybrid flow-shop scheduling problems (DHFSPs) are widely encountered in manufacturing systems. Their complexity increases significantly when multiple overhead cranes are used for material handling. This paper investigates a distributed hybrid flow-shop scheduling problem with multiple overhead crane transportation (DHFSP-MCT), aiming to simultaneously minimize makespan and total energy consumption (including machining and transport). A hierarchical reinforcement learning-based bi-population collaborative metaheuristic algorithm (HRL-BCMA) is proposed. In HRL-BCMA, an iterated greedy strategy is first adopted to generate an initial population. Then, a two-level reinforcement learning framework is designed: a high-level agent decides when to release jobs to the shop floor, while a low-level agent based on a graph isomorphism network selects improvement operators. Furthermore, a bi-population co-evolutionary framework and a knowledge-informed strategy are introduced to enhance solution quality and diversity. Experimental evaluations on both randomly generated instances and a real-world-inspired aluminum manufacturing case show that HRL-BCMA reduces makespan by 8.6% and total energy consumption by 12.3% on average compared to the best existing algorithm (CBMA) while achieving superior Pareto front coverage. These results demonstrate the effectiveness of the proposed method for green scheduling problems with crane transport constraints. Full article

As typical underactuated systems, overhead cranes are widely utilized in heavy-load transportation. However, their strong nonlinear coupling and underactuated characteristics complicate precise positioning and payload swing suppression. Furthermore, model uncertainties and external disturbances in practical environments increase control complexity and degrade system performance. To address these issues, this paper develops a trajectory tracking control scheme based on time delay estimation (TDE). Specifically, some transformations are made for the dynamic model and the TDE mechanism is used to estimate unknown nonlinear dynamics and external disturbances. Then, a sliding mode trajectory tracking controller, along with the TDE mechanism, is proposed for the trajectory tracking control and uncertainties estimation of the overhead crane system. Rigorous mathematical analysis is provided to demonstrate the asymptotic stability of the closed-loop system. Finally, simulation results verify the effectiveness of the proposed method in comparison with the existing control methods. Full article

The lightweight design of box-type double-girder overhead crane main girders is important for improving load-carrying capacity, reducing energy consumption, and enhancing transportation efficiency. However, the structural optimization of crane main girders involves multiple constraints and strong nonlinearity, which often leads to slow convergence and premature stagnation when using traditional optimization methods. To address these issues, a multi-strategy improved dung beetle optimization algorithm (MSIDBO) is proposed for the lightweight design of overhead crane main girders. First, the search mechanism and inherent limitations of the standard dung beetle optimization (DBO) algorithm are analyzed. Subsequently, several enhancement strategies are introduced, including hybrid chaotic population initialization; reflective boundary handling; adaptive quantum jump updating; adaptive hybrid updating; and a staged control strategy for search intensity. These strategies are designed to enhance population diversity and achieve a better balance between global exploration and local exploitation. The performance of MSIDBO was evaluated on 29 CEC2017 benchmark functions. The results show that MSIDBO generally converges faster on 25 functions and reaches the global optimum on 24 functions among the compared algorithms. Finally, based on mechanical analysis and design specifications of overhead crane main girders, a constrained structural optimization model is established. The lightweight design optimization is carried out, and finite element simulations were conducted using ANSYS Workbench to verify the effectiveness and engineering feasibility of the optimized design. The results show that the proposed MSIDBO algorithm exhibits enhanced stability and convergence performance, achieving a weight reduction of 19.4% in the main girder under the specified design configuration, meeting satisfying strength and safety requirements. Full article

Safe and efficient cargo handling is essential in modern port logistics, where overhead cranes are widely used to move containers, bulk materials, and heavy equipment. Accurate real-time measurement of wire rope inclination is critical for preventing collisions, reducing load sway, and enabling autonomous crane operation under challenging maritime conditions. This paper presents a You Only Look Once v9 (YOLOv9)-based camera–LiDAR fusion system for real-time estimation of the trolley–hook rope inclination angle in overhead cranes. A monocular industrial camera and a YOLOv9 detector provide semantic region-of-interest (ROI) masks for the trolley and hook, while a 3D LiDAR sensor, rigidly mounted and extrinsically calibrated to the camera, provides depth information. LiDAR points projected onto the image and filtered by YOLOv9 bounding boxes allow efficient extraction of safety-critical 3D geometry and reconstruction of the rope vector. Experimental results on an overhead crane testbed show that the proposed fusion estimator achieves an angle RMSE below 1 degree in dynamic swing and low-illumination scenarios, significantly outperforming a camera-only baseline (RMSE ≈ 2.11). These metrically validated results indicate that the proposed detection pipeline offers a robust foundation for intelligent crane monitoring and automation in maritime logistics and smart port operations. Full article

Modern interaction with smart devices is hindered by the “Midas Touch” problem, where sensors frequently misinterpret incidental physical movements as intentional commands due to a lack of human context. This research addresses this conflict by introducing the Multimodal Haptic Informatics (MHI) index within a novel Scene–Action–Trigger (SAT) framework. The goal is to contextualize mechanical movements as human intent by integrating physical, spatial, and cognitive data locally at the edge. The methodology employs an “Action-as-primary indexing” mechanism where the Action channel (IMU) serves as a temporal anchor t, triggering high-resolution Scene (computer vision) and Trigger (audio) processing only during critical haptic events. Validated through a complex origami crane task generating 29,408 data frames, the framework utilizes a three-stage informatics derivation process: single-modal scoring, score weighting, and hand state mapping. Results demonstrate that applying an adaptive “Speedometer” logic successfully reclassifies the “Transitional State”. While this state constitutes over half of the behavioral dataset (54.76% on average), it is effectively disambiguated into meaningful intent using a self-trained local Large Language Model (LLM) for semantic verification. Furthermore, the event-driven sampling of 93 keyframes reduces the processing overhead by 99.68% compared to linear annotation. This study contributes a low-latency, privacy-preserving “Protocol of Assent” that maintains user agency by providing intelligent system suggestions based on confirmed haptic intensity. Full article

Taking a metal structural part blanking workshop as the application background, this study addresses the challenges of high material variety, long crane feeding travel caused by heterogeneous line-side storage layouts, and frequent machine stoppages due to the limited feeding capacity of a single overhead crane. To this end, an integrated machine–crane dual-resource scheduling model is developed by explicitly considering line-side storage locations. The objective is to minimize the maximum waiting time among all machine tools. Under constraints of material assignment, processing sequence, and the crane’s single-task execution and travel requirements, the storage positions of materials in line-side buffers are jointly optimized. To solve the problem, a genetic algorithm with fitness-value-based crossover is proposed, and a simulated-annealing acceptance criterion is embedded to suppress premature convergence and enhance the ability to escape local optima. Comparative experiments on randomly generated instances show that the proposed algorithm can significantly reduce the maximum waiting time and yield more stable results for medium- and large-scale cases. Furthermore, a simulation based on real production data from an industrial enterprise verifies that, under limited feeding capacity, the proposed method effectively shortens material-waiting time, improves equipment utilization, and enhances production efficiency, demonstrating its effectiveness. Full article

This paper presents an original method for modelling the energy intensity of an overhead crane using MATLAB–Simulink and MSC Adams software. The analysis focused on an overhead crane used in warehouses handling bundled goods, which are placed on pallets. The study examined the energy intensity of the crane in two reference, predefined work cycles: goods reception and order picking. During the development phase, data from logistics centres and the FLEXSIM system were used to define the test cycles. The author’s experience in implementing and developing standards was also applied. Reference measurements of the crane, necessary for validating the computer model, were carried out in real operating conditions at a logistics centre. The integration of the author’s proprietary approach—combining computer-based energy intensity modelling with test cycles for the crane—helped overcome barriers in supporting the concept of “green warehouses” (passive or energy-positive), making it possible to estimate and compare the energy intensity of intralogistics facilities. A high level of agreement was achieved between the measured and modelled data using the author’s proprietary EPI. The described methodology was verified using a double-girder overhead crane handling bundled load units in a warehouse. The test results determined the potential for energy recovery within the crane’s drive system. Full article

Gantry cranes play a key role in modern industrial logistics. However, the traditional dynamic model based on the assumption of cable rigidity faces difficulty in accurately describing the complex swing characteristics of flexible cables, resulting in low load positioning accuracy and limited operation efficiency. To address this problem, this paper proposes a cable modeling method that considers the flexible deformation and nonlinear dynamic characteristics of the cable. Based on the theory of continuum mechanics, a flexible cable dynamic model that can accurately describe the flexible deformation and distributed mass characteristics of the cable is established. In order to solve the transportation time optimization and full-state constraint problems, a velocity trajectory optimization algorithm based on a discretization framework is proposed. Through inverse kinematics analysis and numerical integration technology, a reverse angle enumeration reasoning (RAER) method is proposed to suppress the swing of the load. Under the same constraints of distance, velocity, acceleration, cable swing angle, and residual swing angle, RAER requires a longer transportation time but achieves smaller peak swing and residual swing, making it the only algorithm that satisfies full-state constraints. Under the energy criterion, the proposed algorithm also requires the least amount of energy. Comprehensive comparisons through simulations and experiments show that the predicted swing angles of the flexible cable are highly consistent with the experimental results. Full article

This study deals with innovative friction-bolted joints. The innovative bolted joints were designed for load-bearing structures of light cranes with spans up to 18 m and lifting capacities up to 160 kN, or similar load-bearing structures. The practical suitability of these connectors for use in the screw connection nodes of headframes with girders in light overhead cranes was confirmed by the positive results of experimental verification and experimental testing on physical models of these nodes. Specific elements were created and used in these joints, and several models were examined. The analyzed friction-bolted joints were found to have an increased load capacity when compared with typical assemblies. If using screws of higher strength grades, shortening the preparation of those joints is possible. Full article

This research develops and validates a novel impact detection system for container monitoring using autoencoders embedded within an edge computing unit. This solution addresses common limitations in current container tracking systems, such as a lack of real-time processing and reliance on cloud connectivity, by enabling local, on-device anomaly detection. We compare the performance of Recurrent Autoencoders (RAEs) and Temporal Convolutional Autoencoders (TCAEs) using acceleration data collected during quay crane handling. Experimental results show that the RAE framework outperforms TCAEs, achieving a precision of 91.3%, a recall of 87.6%, and an F1-score of 89.4% for impact detection while also demonstrating lower reconstruction loss and improved detection of sequential anomalies. The system accurately identifies impact events with minimal computational overhead, proving its viability for real-time deployment in port environments. Our findings suggest that time-series autoencoder architectures, particularly RAEs, are effective for detecting mechanical impacts in resource-constrained edge devices, offering a robust alternative to traditional cloud-based solutions. Full article

Seismic requirements play a crucial role in the design of mechanical systems for infrastructures located in earthquake-prone regions. This process becomes significantly more complex when non-linearities are present, making system-specific analyses necessary. The evaluation of earthquake effects, as mandated by national regulations, is typically based on linear response spectra, which describe the peak response of a harmonic oscillator with a given natural frequency to external vibrations. However, for non-linear systems, computationally intensive transient simulations are required. Developing simplified methods to extend design loads without relying on such complex simulations would be highly beneficial, particularly for commonly encountered non-linear systems. One such system is a hoisted load manipulated by an overhead crane. Strong earthquakes can induce oscillations that cause periodic slack rope conditions—where the rope loses tension and the load temporarily enters free fall—resulting in peak accelerations that exceed those predicted by linear models. This study focuses on quantifying these amplified accelerations in hoisted loads subjected to non-linear dynamics. Using a Monte Carlo approach, it establishes intensification factors—expressed as a function of key physical parameters—relative to a given design response spectrum. Full article

In the aluminum electrolysis production workshop, heavy-load overhead cranes equipped with multi-functional operation terminals are responsible for critical tasks such as anode replacement, shell breaking, slag removal, and material feeding. The real-time monitoring of these four types of operation terminals is of the utmost importance for ensuring production safety. High-resolution cameras are used to capture dynamic scenes of operation. However, the terminals undergo morphological changes and rotations in three-dimensional space according to task requirements during operations, lacking rotational invariance. This factor complicates the detection and recognition of multi-form targets in 3D environment. Additionally, operations like striking and material feeding generate significant dust, often visually obscuring the terminal targets. The challenge of real-time multi-form object detection in high-resolution images affected by smoke and dust environments demands detection and dehazing algorithms. To address these issues, we propose the YOLOv8n-Al-Dehazing method, which achieves the precise detection of multi-functional material handling terminals in aluminum electrolysis workshops. To overcome the heavy computational costs associated with processing high-resolution images by using YOLOv8n, our method refines YOLOv8n through component substitution and integrates real-time dehazing preprocessing for high-resolution images, thereby reducing the image processing time. We collected on-site data to construct a dataset for experimental validation. Compared with the YOLOv8n method, our method approach increases inference speed by 15.54%, achieving 120.4 frames per second, which meets the requirements for real-time detection on site. Furthermore, compared with state-of-the-art detection methods and variants of YOLO, YOLOv8n-Al-Dehazing demonstrates superior performance, attaining an accuracy rate of 91.0%. Full article

To facilitate the automation of crane operations for grabbing coil stacks in port storage areas, thereby streamlining the processes of warehousing, stacking, and transshipment for enhanced operational efficiency, this paper utilizes algorithms related to 3D point clouds for the pose estimation of coil workpieces. To overcome the limitations of the traditional point pair feature (PPF) algorithm, a novel point cloud registration algorithm is introduced. This algorithm harnesses the advantages of the PPF algorithm in describing local features and integrates it with the Generalized Iterative Closest Point (GICP) algorithm to enhance the robustness and applicability of registration. Finally, comparative experiments demonstrate that the proposed algorithm delivers superior performance. The average pose estimation errors for one, two, and three coils are 1.1%, 1.1%, and 1.2% of the coil size, respectively, with total processing times of 3.6 s, 3.4 s, and 4.7 s, meeting the practical application requirements in terms of accuracy and timing. Full article

Overhead cranes are widely used for transportation in factories. They move slowly by manual operation to prevent the payload from swinging sharply or colliding with sudden obstacles. To address these issues and enhance work efficiency, this paper proposes a couple anti-swing obstacle avoidance control method for 5-DOF overhead cranes. Time polynomial fitting is employed for trajectory planning to achieve obstacle avoidance. To achieve anti-swing of the payloads, a coupled variable incorporating both actuated and underactuated states is defined, alongside a boundary for dynamic performance. Finally, MATLAB simulation and hardware experiments are carried out to verify the reliability and compared with some existing control methods. Full article