Search Results (233)

The advancement of adaptable industrial robots in intelligent manufacturing is hindered by the inefficiency of traditional motion planning methods in high-dimensional spaces. Therefore, a Dempster–Shafer evidence theory-based hybrid motion planner is proposed, in which a probabilistic sampling network (PSNet) and an enhanced artificial potential field (EAPF) cooperate with each other to improve the planning performance. The PSNet architecture comprises two modules: a motion planning module (MPM) and a fusion sampling module (FSM). The MPM utilizes sensor data alongside the robot’s current and target configurations to recursively generate diverse multimodal distributions of the next configuration. Based on the distribution information, the FSM was used as a decision-maker to ultimately generate globally connectable paths. Moreover, the FSM is equipped to correct collision path points caused by network inaccuracies through Gaussian resampling. Simultaneously, an augmented artificial potential field with a dynamic rotational field is deployed to repair local paths when worst-case collision scenarios occur. This collaborative strategy harmoniously unites the complementary strengths of both components, thereby enhancing the overall resilience and adaptability of the motion planning system. Experiments were conducted in various environments. The results demonstrate that the proposed method can quickly find directly connectable paths in diverse environments while reliably avoiding sudden obstacles. Full article

The unprecedented increase in the number of objects orbiting the Earth necessitates a comprehensive characterisation of these objects to improve the effectiveness of Space Surveillance and Tracking (SST) operations. In particular, accurate knowledge of the attitude and physical properties of space objects has become critical for space debris mitigation measures, since these parameters directly influence major perturbation forces like atmospheric drag and solar radiation pressure. Characterising a space object beyond its orbital position improves the accuracy of SST activities such as collision risk assessment, atmospheric re-entry prediction, and the design of Active Debris Removal (ADR) and In-Orbit Servicing (IOS) missions. This study presents a novel approach for the simultaneous estimation of the attitude and optical reflective properties of uncontrolled space objects with known shape using light curves. The proposed method also accounts for atmospheric effects, particularly the Aerosol Optical Depth (AOD), a highly variable parameter that is difficult to determine through on-site measurements. The methodology integrates different estimation, optimisation, and data analysis techniques to achieve an accurate, robust, and computationally efficient solution. The performance of the method is demonstrated through the analysis of a simulated scenario representative of realistic operational conditions. Full article

Multi-view inverse synthetic aperture radar (ISAR) image sequences provide multi-dimensional observation information about space targets, enabling precise attitude estimation that is fundamental to both non-cooperative target monitoring and critical space operations including active debris removal and space collision avoidance. However, directly utilizing all images within an ISAR sequence for attitude estimation can result in a substantial data preprocessing workload and reduced algorithm efficiency. Given the inherent overlap and redundancy in the target information provided by these ISAR images, this paper proposes a novel space target attitude estimation method based on the selection of multi-view ISAR image sequences. The proposed method begins by establishing an ISAR imaging projection model, then characterizing the target information differences through variations in imaging plane normal, and proposing an image selection method based on the uniform sampling across elevation and azimuth angles of the imaging plane normal. On this basis, the method utilizes a high-resolution network (HRNet) to extract the feature points of typical components of the space target. This method enables simultaneous feature point extraction and matching association within ISAR images. The attitude estimation problem is subsequently modeled as an unconstrained optimization problem. Finally, the particle swarm optimization (PSO) algorithm is employed to solve this optimization problem, thereby achieving accurate attitude estimation of the space target. Experimental results demonstrate that the proposed methodology effectively filters image data, significantly reducing the number of images required while maintaining high attitude estimation accuracy. The method provides a more informative sequence than conventional selection strategies, and the tailored HRNet + PSO estimator resists performance degradation in sparse-data conditions, thereby ensuring robust overall performance. Full article

As robotic applications rapidly expand into increasingly complex and dynamic environments, greater emphasis is being placed on the intelligence and safety of human–robot collaboration at the task execution level. In shared human–robot workspaces, even the most precise motion planning cannot fully prevent collisions. To address this critical safety concern, we propose a variational Bayesian Kalman filtering-based external torque estimation algorithm that integrates the robot’s dynamic model while avoiding additional system complexity. We begin by reviewing the robot dynamics framework and the classical external torque estimation method based on generalized momentum. We then derive a Kalman filter-based approach for external torque estimation in robotic manipulators and analyze the adverse effects arising from mismatches in process noise covariance. Finally, we introduce a sliding window-based variational Bayesian Kalman filter, which dynamically estimates the current process noise covariance while simultaneously mitigating the accumulation of recursive errors. Full article

Under no-load conditions, the wet clutch of vehicles generates drag torque across a wide speed range, which increases power loss in the transmission system and significantly impacts its efficiency and reliability. To address the clutch drag issue over a wide speed range, this study first establishes a low-speed drag torque model that simultaneously considers the viscous friction effects in both the complete oil film region and the oil film rupture zone of the friction pair clearance. Subsequently, by solving the fluid-structure interaction dynamics model of the friction plates, the collision force between high-speed friction pairs and the resulting friction torque are determined, forming a method for calculating high-speed collision-induced drag torque. Building on this, a unified drag torque model for wet clutches across a wide speed range is developed, integrating both viscous and collision-induced drag torques. The validity of the wide-speed-range drag torque model is verified through experiments. The results indicate that as oil temperature and friction pair clearance increase, the drag torque decreases and the rotational speed corresponding to the peak drag torque is reduced, while the onset of collision phenomena occurs earlier. Conversely, with an increase in oil supply flow rate, the drag torque rises and the rotational speed corresponding to the peak drag torque increases, but the onset of collision phenomena is delayed. Finally, with the optimization objectives of minimizing the peak drag torque in the low-speed range and the total drag torque at the maximum speed in the high-speed range, an optimization design model for the surface grooves of the clutch friction plates is constructed. An optimized groove pattern is obtained, and its effectiveness in suppressing drag torque across a wide speed range is experimentally validated. Full article

The increasing use of UAV swarms for collaborative autonomous missions presents significant challenges in coordination, safety, and scalability, especially during dynamic formation reconfigurations. This study introduces the Magnetic Swarm Reconfiguration (MSR) protocol, a fully distributed navigation method that enables UAV swarms to transition smoothly and safely between geometric formations. MSR achieves this by combining two main components: first, it employs the Hungarian algorithm to compute an optimal assignment of UAVs to target positions within the new formation, thereby minimizing trajectory overlap and interference; second, it utilizes virtual magnetic attraction and repulsion forces for real-time navigation, drawing each UAV toward its assigned destination while dynamically repelling nearby agents to avoid collisions. To evaluate the performance of the MSR protocol, six representative formation transitions were simulated across swarm sizes of up to 100 UAVs. Results show that MSR reduces reconfiguration time significantly compared to existing methods, maintains strict safety standards by achieving minimal to zero collisions, and supports fully decentralized and simultaneous maneuvering. The scalability and robustness of the MSR protocol make it suitable for complex, large-scale swarm operations requiring rapid and reliable formation changes. Full article

In robotic teleoperation, the simultaneous realization of rapid tracking, compliance, and safe interaction presents a fundamental control challenge. This challenge stems from a critical trade-off: high-stiffness controllers achieve rapid tracking but compromise safety during physical interactions, whereas low-stiffness impedance controllers ensure compliant and safe interactions at the expense of responsiveness. To address this conflict, this study proposes a bio-inspired teleoperation control method (BITC) that integrates human withdrawal reflex mechanisms and the nonlinear stiffness characteristics of shear-thickening fluids. BITC features a dynamic force-feedback-driven collision reflex strategy, enabling rapid detection and disengagement from unintended contacts. Additionally, a nonlinear compliance control module is proposed to achieve both force fidelity during initial contact and adaptive stiffness modulation during progressively deeper contact in an emergency. By integrating full-state feedback tracking, the BITC teleoperation control framework is implemented to unify the performance of rapid tracking, compliance, and safety. Three experiments are conducted to demonstrate that the BITC method achieves accurate tracking performance, ensures compliant behavior during deep contact while maintaining force fidelity during initial contact, and enables safe reflexion for collision, respectively. The method is also validated to reduce peak contact forces by approximately 60% and minimizes contact duration to less than 120 ms, presenting comprehensive teleoperation performance. Full article

Utilizing a coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) approach, this study constructs a comprehensive three-dimensional numerical model to simulate particle migration dynamics within rough artificial fractures subjected to the high-energy impact of water inrush. The model explicitly incorporates key governing factors, including intricate fracture wall geometry characterized by the joint roughness coefficient (JRC) and aperture variation, hydraulic pressure gradients representative of inrush events, and polydisperse sand particle sizes. Sophisticated simulations track the complete mobilization, subsequent acceleration, and sustained transport of sand particles driven by the powerful high-pressure flow. The results demonstrate that particle migration trajectories undergo a distinct three-phase kinetic evolution: initial acceleration, intermediate coordination, and final attenuation. This evolution is critically governed by the complex interplay of hydrodynamic shear stress exerted by the fluid flow, frictional resistance at the fracture walls, and dynamic interactions (collisions, contacts) between individual particles. Sensitivity analyses reveal that parameters like fracture roughness exert significant nonlinear control on transport efficiency, with an identified optimal JRC range (14–16) promoting the most effective particle transit. Hydraulic pressure and mean aperture size also exhibit strong, nonlinear regulatory influences. Particle transport manifests through characteristic collective migration patterns, including “overall bulk progression”, processes of “fragmentation followed by reaggregation”, and distinctive “center-stretch-edge-retention” formation. Simultaneously, specific behaviors for individual particles are categorized as navigating the “main shear channel”, experiencing “boundary-disturbance drift”, or becoming trapped as “wall-adhered obstructed” particles. Crucially, a robust multivariate regression model is formulated, integrating these key parameter effects, to quantitatively predict the critical migration time required for 80% of the total particle mass to transit the fracture. This investigation provides fundamental mechanistic insights into the particle–fluid dynamics underpinning hazardous water–sand inrush phenomena, offering valuable theoretical underpinnings for risk assessment and mitigation strategies in deep underground engineering operations. Full article

In maritime blurred image recognition, ship collision accidents frequently result from three primary blur types: (1) motion blur from vessel movement in complex sea conditions, (2) defocus blur due to water vapor refraction, and (3) scattering blur caused by sea fog interference. This paper proposes a dual-branch recognition method specifically designed for motion blur, which represents the most prevalent blur type in maritime scenarios. Conventional approaches exhibit constrained computational efficiency and limited adaptability across different modalities. To overcome these limitations, we propose a hybrid CNN–Transformer architecture: the CNN branch captures local blur characteristics, while the enhanced Transformer module models long-range dependencies via attention mechanisms. The CNN branch employs a lightweight ResNet variant, in which conventional residual blocks are substituted with Multi-Scale Gradient-Aware Residual Block (MSG-ARB). This architecture employs learnable gradient convolution for explicit local gradient feature extraction and utilizes gradient content gating to strengthen blur-sensitive region representation, significantly improving computational efficiency compared to conventional CNNs. The Transformer branch incorporates a Hierarchical Swin Transformer (HST) framework with Shifted Window-based Multi-head Self-Attention for global context modeling. The proposed method incorporates blur invariant Positional Encoding (PE) to enhance blur spectrum modeling capability, while employing DyT (Dynamic Tanh) module with learnable α parameters to replace traditional normalization layers. This architecture achieves a significant reduction in computational costs while preserving feature representation quality. Moreover, it efficiently computes long-range image dependencies using a compact 16 × 16 window configuration. The proposed feature fusion module synergistically integrates CNN-based local feature extraction with Transformer-enabled global representation learning, achieving comprehensive feature modeling across different scales. To evaluate the model’s performance and generalization ability, we conducted comprehensive experiments on four benchmark datasets: VAIS, GoPro, Mini-ImageNet, and Open Images V4. Experimental results show that our method achieves superior classification accuracy compared to state-of-the-art approaches, while simultaneously enhancing inference speed and reducing GPU memory consumption. Ablation studies confirm that the DyT module effectively suppresses outliers and improves computational efficiency, particularly when processing low-quality input data. Full article

Empty container depot (ECD) design significantly impacts maritime terminal efficiency, yet traditional evaluation approaches assess limited operational factors, constraining comprehensive performance optimization. This study develops an integrated discrete event simulation (DES) framework that simultaneously evaluates lifting equipment utilization, truck turnaround times, and potential collisions to support terminal decision-making. This study combines operational efficiency metrics with safety analytics for non-automated ECDs using Top Lifters and Reach Stackers. Additionally, a regression analysis examines efficiency metrics’ effect on safety risk. A case study at a Chilean multipurpose terminal reveals performance trade-offs between indicators under different operational scenarios, identifying substantial efficiency disparities between dry and refrigerated container operations. An analysis of four distinct collision zones with varying historical risk profiles showed the gate area had the highest potential collisions and a strong regression correlation with efficiency metrics. Similar models showed a poor fit in other conflict zones, evidencing the necessity for dedicated safety indicators complementing traditional measures. This integrated approach quantifies interdependencies between safety and efficiency metrics, helping terminal managers optimize layouts, expose traditional metric limitations, and reduce safety risks in space-constrained maritime terminals. Full article

Long Range (LoRa) is one of the promising Low-Power Wide-Area Network technologies to achieve a strong anti-noise ability due to the modulation of the chirp spread spectrum in low-power and long-distance communications. However, LoRa suffers the problem of packet collisions. Hence, we propose QR−LoRa, a novel PHY-layer scheme that can transmit data in both amplitude and frequency dimensions simultaneously. For the amplitude modulation, we modulate the constant envelope of a LoRa chirp with a cyclic right-shifted ramp signal, where the cyclic right-shifted position carries the data of the amplitude modulation. We adopt the standard LoRa for frequency modulation. We prototype QR−LoRa on the software-defined radio platform USRP N210 and evaluate its performance via simulations and field experiments. The results show the bit rate gain of QR−LoRa is up to 2× compared with the standard LoRa device. Full article

Heating, ventilation, and air-conditioning systems account for 40–60% of the energy consumed in commercial buildings, and much of this load originates from sub-optimal piping layouts in chiller-plant rooms. This study presents an automated routing framework that couples Building Information Modeling (BIM) with an enhanced A* search to produce collision-free, low-resistance pipelines while simultaneously guiding component selection. The algorithm embeds protective buffer zones around equipment, reserves maintenance corridors through an attention-based cost term, and prioritizes 135° elbows to cut local losses. Generated paths are exported as Industry Foundation Classes (IFC) objects for validation in a BIM digital twin, where hydraulic feedback drives iterative reselection of high-efficiency devices—including magnetic-bearing chillers, cartridge filters and tilted-disc valves—until global pressure drop and life-cycle cost are minimized. In a full-scale shopping-mall retrofit, the method significantly reduces pipeline resistance and operating costs, confirming its effectiveness and replicability for sustainable chiller-plant design. Full article

Efficient material handling is crucial in the logistics operations of modern salt warehouses, where Rail Guided Vehicles (RGVs) and Air Sorting Robots (ASRs) are often deployed to manage inbound and outbound tasks. However, as the number of tasks increases within a given period, conflicts and deadlocks between simultaneously operating RGVs and ASRs become more frequent, reducing efficiency and increasing energy consumption during transportation. To address this, the research frames the inbound and outbound problem as a task allocation issue for the RGV/ASR system with a finite buffer, and proposes a collision avoidance strategy and a zero-wait strategy for loaded machines to reallocate tasks. To improve computational efficiency, we introduce an adaptive multi-neighborhood hybrid search (AMHS) algorithm, which integrates a dual-sequence coding scheme and an elite solution initialization strategy. A dedicated global search operator is designed to expand the search landscape, while an adaptive local search operator, inspired by biological hormone regulation mechanisms, along with a perturbation strategy, is used to refine the local search. In a case study on packaged salt storage, the proposed AMHS algorithm reduced the total makespan by 30.1% compared to the original task queue. Additionally, in 15 randomized test scenarios, AMHS demonstrated superior performance over three benchmark algorithms—Genetic Algorithm (GA), Discrete Imperialist Competitive Algorithm (DICA), and Improved Whale Optimization Algorithm (IWOA)—achieving an average makespan reduction of 12.6% relative to GA. 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

Dog clutches are superior to synchromesh units due to much less wear caused by friction but require an external torque source to synchronize the rotation speeds. The current trend in e-mobility to use the driving motor of an electric vehicle as this source just creates another problem, which is known as torque holes. In this work, we propose a novel double-sided dog clutch that synchronizes the speeds electromagnetically by itself avoiding mechanical contact between the parts. A shift sleeve, two coils placed coaxially in their stators, and two complementary rings form an electromagnetic reluctance actuator, which is integrated inside the gearbox between two gearwheels and represents the double-sided clutch. Thus, intermediate parts between the shift sleeve and the actuator are not required. Both actuator sides can produce axial force and electromagnetic torque. However, torques and forces are generated simultaneously on both sides. Therefore, a special control algorithm is developed to keep the resulting axial force approximately equal to zero while the torque is generated in the neutral gear position. After the synchronization, the axial force is applied on the corresponding side to shift the required gear engaging the shift sleeve teeth directly with the slots of the complementary ring mounted on the gearwheel. So, an axial contact of the teeth at an unaligned state, which can lead to unsuccessful shifting, is avoided. A testrig, which includes a clutch prototype and a testing two-speed gearbox, has been designed and built. The developed theoretical ideas have been verified during the experiments under different conditions. The experiments confirm that the actuator can reduce positive and negative speed differences on both sides and subsequently shift the gear without a shift sleeve collision at misaligned angular positions. Full article