Search Results (45)

To address the issue of persistent high-magnitude outlier interference affecting long-baseline (LBL) positioning systems in complex marine environments, this paper proposes a kinematic constraint-based Robust Interacting Multiple Model Kalman Filter algorithm. Combined with anchor point initialization and multi-step historical observations, the algorithm constructs a spatial Euclidean distance discriminant criterion. By further incorporating the maximum velocity constraint of the Autonomous Underwater Vehicle (AUV), dynamic decision thresholds are established, and final detection decisions are output to the positioning system. Within the Kalman Filter recursion process, a measurement mask matrix is introduced to instantly isolate measurement outliers, preventing abnormal data from participating in state updates and model probability evolution. Simulation results demonstrate that, compared with standard LBL positioning, conventional single outlier detection, and the conventional maximum correntropy criterion-based Kalman filter (MCC-KF) algorithm, the proposed approach enhances outlier identification and suppression—particularly under consecutive anomaly conditions—thereby improving the positioning accuracy of maneuvering targets in complex underwater scenarios. Full article

Urban low-altitude unmanned aerial vehicle (UAV) logistics networks face critical operational bottlenecks due to complex three-dimensional spatial blockages, continuous communication diffraction, and severe vulnerability to information-layer threats such as Automatic Dependent Surveillance—Broadcast (ADS-B) signal anomalies. To address these interconnected challenges, this paper proposes an event-driven, cloud–edge collaborative digital twin framework to guarantee continuous multi-link communication and flight safety. The architecture operates through a dual-tier “Teacher–Student” paradigm. Under secure conditions, a cloud digital twin acts as a high-capacity “Teacher,” employing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to partition heterogeneous user topologies. It then utilizes an energy-guided stochastic diffusion sampling (EGSDS) method to refine initial macroscopic routing, generating precise, outage-free global trajectories by systematically minimizing non-line-of-sight (NLoS) observation penalties and kinematic regularization costs. To counteract signal anomalies, a distributed Time Difference of Arrival (TDOA) anchor network continuously validates UAV coordinate integrity. If a threshold is breached, control authority is instantly transferred to the UAV’s edge digital twin. This resource-constrained edge tier relies on a localized “Student” network trained via progressive distillation. By compressing the computationally heavy iterative diffusion process into a rapid one-step inference model, the UAV autonomously generates a secure, short-range emergency path that strictly adheres to minimum communication thresholds. Once interference clears, the cloud seamlessly regains control to complete the logistics mission. Experimental results demonstrate that the proposed scheme significantly outperforms conventional heuristic routing methods in cloud-based scenarios. Furthermore, the edge-based distillation mechanism substantially improves the overall trajectory survival rate under signal anomalies, ensuring resilient and continuous logistics operations. Full article

Over the last decade, augmented reality (AR) has been widely adopted in architectural education, yet it is still often treated as a visualization add-on rather than as an operative design medium. This paper argues that AR becomes pedagogically meaningful when it is anchored to physical or graphic artefacts so that overlays function not as final images, but as reversible instruments for testing, adjustment, and spatial verification. Building on reflection-in-action as a model of situated design learning, the study examines two teaching experiences: one focused on the AR-based translation of complex two-dimensional graphic fields into three-dimensional hypotheses, and another centred on kinematic reasoning through equilibrium and iterative adjustment. The article proposes that error within AR-based workflows has a double pedagogical role: first, as corrective feedback, when mismatch reveals imprecision, insufficient legibility, or unstable alignment in the target; and second, as generative design feedback, when recalibration and reconfiguration trigger new spatial hypotheses or bidirectional transfers between physical and digital models. Evidence is based primarily on analytic observation of documented episodes and on visual documentation of process transformations, complemented by a background evaluative scaffold and supplementary student feedback where available. Results indicate that AR can (a) increase the material and graphic precision of the supporting artefact; (b) strengthen spatial and kinematic understanding by making intermediate states and inconsistencies visible; and (c) turn mismatch and recalibration into operative parts of the design process itself. The paper therefore reframes AR in architectural education not as a representational endpoint, but as a medium of verification, adjustment, and projective transformation. Full article

Integrating depth sensors into mobile robots enables automated gait monitoring with potential applications in neurological disorders. This pilot study aims to evaluate the preliminary feasibility of robot-assisted gait assessment using Azure Kinect against Vicon, including individuals with multiple sclerosis, while simultaneously examining between-system, within-system, and environmental effects. A total of 20 participants were recruited to complete the eight-meter straight-line and 32 m corridor walking tests in the laboratory on the same day. Following independent data acquisition by both systems, temporal alignment was achieved through foot-event anchoring and interval trimming. On a unified timeline, 8 joint kinematic signals and 26 descriptors were extracted. Generalized estimating equations were applied, with a Bonferroni correction implemented for the 26 parallel tests to control the family error rate. The results showed: The spatiotemporal gait metrics exhibited general stability between systems and environments. Vicon better revealed variations in hip and pelvic amplitudes and restricted extension phenotypes, while the robotic system demonstrated greater sensitivity to knee posture and relative swing amplitude. The corridor environment induced an increase in stride length and a reduced step time compared to the laboratory, accompanied by a greater peak of hip and knee flexion and a greater forward lean of the trunk, with a largely preserved temporal organization. Within the Vicon-referenced framework, Azure Kinect-based robotic assessment demonstrated preliminary feasibility for capturing gait-related characteristics in individuals with multiple sclerosis. However, due to the limited number of analyzed MS participants, these findings should be interpreted as exploratory rather than as definitive clinical validation. The two systems exhibit complementary kinematic advantages. We recommend adopting an evaluation protocol that combines laboratory baseline with corridor validation, supplemented by descriptor-level mapping for cross-system data integration when necessary. This approach may support future tiered assessment, disease progression monitoring, and efficacy evaluation, but larger clinical cohorts are required to confirm its applicability in individuals with multiple sclerosis. Full article

The study proposes and investigates a seismic retrofitting strategy based on an external reinforced concrete exoskeleton, grounded in the analysis of the actual force transfer mechanisms between the existing structure and the added system. The three-dimensional numerical model was developed in ETABS, employing linear response spectrum analysis in accordance with EN 1998-1 and P100-1/2013. The internal forces transmitted at the structural interface were determined using the Section Cut method, enabling the identification of integrated resultants and the prioritization of critical connections. Three types of connections are examined—slab-to-slab, column-to-wall, and beam-to-joint—while the distribution of stresses within the anchor groups is assessed based on an elastic model under combined axial force and bending action. The results indicate that the global structural response is governed by diaphragm coupling, whereas the vertical interfaces ensure kinematic compatibility and the redistribution of axial and bending effects. The proposed methodology provides a coherent framework for the rational design of interface connections in retrofit interventions carried out without interrupting building operation. Full article

Neck pain is increasingly associated with exposure-dependent dysfunction linked to digitally mediated behaviors, prolonged near-work, sustained postures, and reduced movement variability, whereas cervical assessment remains dominated by static imaging and brief in-clinic examination. This narrative review evaluates why current diagnostic approaches remain poorly suited to the dynamic nature of many contemporary cervical disorders and examines segmental cervical motion as a clinically relevant but insufficiently observed functional target. Evidence from static imaging, dynamic radiographic methods, laboratory motion analysis, wearable inertial sensing, markerless video, and digital measure validation frameworks is synthesized to assess both current capabilities and translational limitations. Dynamic radiographic methods can characterize intervertebral motion with high anatomical specificity, but they are not suitable for scalable longitudinal monitoring. By contrast, wearable and video-based approaches are more compatible with real-world assessment, yet they capture external head–neck kinematics rather than vertebral-level kinematics directly and remain constrained by indirect observability, soft-tissue artifact, and inference uncertainty. On this basis, the review proposes a four-layer framework for continuous non-invasive cervical functional assessment based on sensing, representation, inference, and clinical interpretation, in which segmental cervical behavior is treated as a latent segment-informed functional construct inferred from multimodal external signals and periodically anchored to sparse reference-grade imaging anchors. Segmental motion signatures are consequently positioned as candidate digital measures for longitudinal cervical monitoring, provided that their development is supported by rigorous analytical and clinical validation, explicit uncertainty reporting, and demonstrated incremental clinical value. Full article

The localization performance of visual–inertial simultaneous localization and mapping (VI-SLAM) strongly depends on front-end feature matching. In degraded scenes with low illumination, repetitive textures, and weak textures, traditional geometric front ends often suffer from sparse features and mismatches, resulting in unstable state estimation. To address this issue, this paper proposes Area-to-Point Matching Visual–Inertial SLAM (A2PM-VINS), a visual–inertial SLAM method based on Area-to-Point matching. The method introduces Area-to-Point hierarchical matching and a kinematic temporal inheritance mechanism to improve matching reliability and track continuity, and further designs an Anchor–Explorer feature selection strategy to retain features with higher geometric value for back-end optimization. In addition, a Sub-Window Consistency (SWC) weighting strategy is incorporated into the back end to suppress geometrically deceptive observations with poor temporal continuity and geometric consistency. Experiments on the European Robotics Challenge Micro Aerial Vehicle (EuRoC MAV) dataset show that A2PM-VINS achieves superior or competitive localization accuracy on multiple challenging sequences. The absolute trajectory errors on MH_04 and MH_05 are 0.0983 m and 0.1191 m, respectively, and stable tracking is maintained on V2_02, where VINS-Fusion fails. These results show that the proposed method effectively improves the robustness of visual–inertial state estimation in complex degraded environments. Full article

Neurological injuries and disorders affecting hand motor control can severely impair the ability to perform activities of daily living and substantially reduce quality of life. Technologies such as virtual reality (VR) are increasingly used to address fundamental challenges in therapy, including motivation and engagement; further, programmable features of digital interfaces offer additional opportunities to personalize and optimize motor training. In this proof-of-concept study, we developed and evaluated a novel VR-based training framework to support improved dexterity and hand function using physiological (sensory-driven) and cognitive (memory) cues designed to promote greater task-relevant neural engagement. The proposed approach leverages the integration of augmented sensory feedback (ASF) with memory-anchored cues for motor learning of target hand gestures. Using a within-subjects design, thirteen neurotypical adults completed four training conditions: (1) control (baseline gesture-matching in VR), (2) visual ASF (enhanced visualization and feedback of gesture accuracy), (3) memory-anchored cues (associating gestures with semantically meaningful entities, loosely analogous to American Sign Language), and (4) hybrid multimodal (visual ASF + memory-anchored cues). Training with the hybrid condition produced the fastest skill acquisition (9.3 trials to reach an 80% accuracy threshold) and the steepest initial learning slope (1.86 ± 0.12%/trial), with all conditions differing significantly in initial slope (all p < 0.002). Post-training assessment showed that the hybrid condition achieved the highest gesture accuracy (95.2%), greatest normalized post-training accuracy gain (14.3% above baseline), fastest execution time to target gesture (1.14 s), and lowest variability in gestural kinematics (SD = 3.9%). Both ASF and memory-anchored cue conditions each also independently outperformed the control condition on gesture accuracy (both p ≤ 0.002), with omnibus ANOVAs indicating significant condition effects across metrics. Together, these findings suggest that pairing ASF cues with memory-based cognitive scaffolding can yield additive benefits for motor skill acquisition and stability. Pending validation in clinical populations, such approaches may inform the design of VR-based motor training frameworks for rehabilitation. Full article

The integration of vertical greenery systems (VGSs) into existing reinforced concrete (RC) buildings raises questions regarding interface kinematics and the permanent displacement of soil-retaining elements under seismic excitation. This study experimentally investigates the residual displacement of façade-mounted living walls and rooftop planter pods anchored to a deficient RC frame under shake table excitation. A 1:3 scale reinforced concrete frame was tested in two distinct phases: initially as a deficient, unretrofitted structure (Phase A), and subsequently as a retrofitted system integrated with vertical greenery elements (Phase B). High-precision terrestrial laser scanning (TLS) was employed before and after successive seismic excitation stages to generate dense three-dimensional point clouds. Cloud-to-cloud comparison techniques were used to quantify global structural displacement and local kinematic behavior of greenery components, while results were validated against conventional displacement sensors. The RC frame exhibited millimeter-scale permanent displacements consistent with draw-wire measurements. In contrast, planter pods demonstrated configuration-dependent behavior, including up to 8 cm translational sliding and rotational responses reaching 13° under repeated excitation, whereas living wall panels remained stable. Notably, a 95% reduction in point cloud density reproduced global deformation patterns with an RMSE of 3.03 mm and quantified peak displacements with only ~2% deviation from full-resolution results. The findings demonstrate the capability of TLS-based monitoring to detect differential kinematic behavior of integrated VGSs, while highlighting the variability in performance of friction-based rooftop anchorage utilizing different robust planter pod fixing systems. Full article

In the complex operational environment of coal mine shafts, trajectory tracking control of crawler hydraulic drill-anchor robots is susceptible to track slippage and internal–external uncertain disturbances, leading to low tracking accuracy. This issue hinders the implementation of efficient and precise coal mine roadway support operations. To address these challenges, enhance the automation level of coal mine roadway support, and improve operational safety and reliability, research on high-precision trajectory tracking control for crawler hydraulic drill-anchor robots is imperative. Therefore, this paper takes crawler hydraulic drill-anchor robots as the research object and focuses on the trajectory tracking control of such robots. First, a kinematic model incorporating track slippage was established for the crawler hydraulic drill-anchor robot. Second, a cascade predictive control strategy is proposed. The upper-layer trajectory tracking control adopts an adaptive model predictive controller, which adjusts controller weights according to tracking error variations and provides reference rotational speeds for the lower-layer predictive controller. Simulation results of linear and sinusoidal trajectory tracking show that the proposed strategy can effectively compensate for the effects of track slippage and improve trajectory tracking accuracy. Finally, a friction-compensated predictive control method was designed to regulate the rotational speeds of the left and right track drive wheels, and the proposed method achieves optimal control performance with a minimum MEAE of 0.12292 rpm, SDAE of 0.44366 rpm, ITAE of 4.9168, MEACI of 3.0607 mA, SDACI of 1.2497 mA, and ITACI of 122.4283. This performance is significantly superior to that of the conventional PID, ADRC, and MPC methods, thereby realizing high-precision track speed control. Full article

Traditional control strategies for Cable-Driven Parallel Robots (CDPRs) rely heavily on global kinematic modeling and precise calibration, severely limiting their adaptability in unstructured or dynamic environments. This study addresses the challenge of rapid deployment without geometric priors by proposing a reconfigurable CDPR system composed of modular units. A novel heuristic control strategy based on “4+2+1” local rules is introduced, comprising translational, attitude correction, and tension maintenance logic. By utilizing local feedback—including cable tension, attitude, and anchor orientation—this method generates control commands without requiring boundary condition calibration, thereby supporting real-time reconfiguration. Numerical simulations of a façade cleaning scenario demonstrate that the system maintains stability across varying topologies, including anchor position changes and unit failures. Compared to a benchmark kinematic method, the proposed strategy reduces trajectory tracking error by approximately 50.5% and suppresses the pitch Root Mean Square Error (RMSE) from a divergent 42.75° (traditional) to 1.52°, effectively preventing the attitude failure typical of uncalibrated model-based control. These findings confirm that the proposed rule-based approach significantly enhances robustness and adaptability, offering a practical solution for deploying CDPRs in complex environments without pre-existing maps. Full article

Compared to purely serial robots or cable-driven parallel robots (CDPRs), cable-driven hybrid robots (CDHRs) combine the advantages of both, addressing their limitations and enabling the execution of complex tasks. The series-parallel coupling structure increases the complexity of the system, complicating modeling, calibration, and force-closure workspace (FCW) analysis. This study develops a CDHR system equipped with various sensors and proposes methods for series-parallel coupling modeling, workspace analysis, and self-calibration of complex systems. First, the modular design requirements for the CDHR are analyzed, comprising an 8-cable parallel drive and a 4-degree-of-freedom serial manipulator. Second, a kinematic model of the CDHR with series-parallel coupling was derived, and the positions of the dynamic anchor seats were optimized using an optimization algorithm. Based on these optimized results, a modeling and analysis method for the statics and FCW is proposed. Furthermore, considering the complex and interdependent structural parameters of the system, a method for the self-calibration of the system parameters and trajectory planning for the CDHR is presented. Finally, experimental validation on both simulations and a physical prototype confirmed the effectiveness of the proposed methods. The developed prototype and the proposed method provide a basis for high-precision operations in large spaces, operations in dangerous/extreme environments, and automated operations in logistics/warehousing. Full article

High-precision autonomous localization remains a critical challenge for intelligent mining vehicles in GNSS-denied and unstructured coal mine roadways, where traditional odometry-based methods suffer from severe cumulative drift and perceptual aliasing. Inspired by the synergy between mammalian visual cues and cognitive neural mechanisms, this paper proposes a robust biomimetic localization framework that integrates multi-source perception with a prior cognitive map. The core contributions are three-fold: First, a semantic-enhanced biomimetic localization method is developed, leveraging roadway sign data as absolute spatial anchors to suppress long-distance cumulative errors. Second, an optimized head direction (HD) cell model is formulated by incorporating a speed balance factor, kinematic constraints, and a drift correction influence factor, significantly improving the precision of angular perception. Third, boundary-adaptive and sign-based semantic constraint terms are integrated into a continuous attractor network (CAN)-based path integration model, effectively preventing trajectory deviation into non-navigable regions. Comprehensive evaluations conducted in large-scale underground scenarios demonstrate that the proposed framework consistently outperforms conventional IMU-odometry fusion, representative 3D SLAM solutions, and baseline biomimetic algorithms. By effectively integrating semantic landmarks as spatial anchors, the system exhibits superior resilience against cumulative drift, maintaining high localization precision where standard methods typically diverge. The results confirm that our approach significantly enhances both trajectory consistency and heading stability across extensive distances, validating its robustness and scalability in handling the inherent complexities of unstructured coal mine environments for enhanced intrinsic safety. Full article

Soil compaction in grassland and agricultural soils reduces water infiltration, root growth and ecosystem services. Conventional deep tillage and coring can alleviate compaction but are energy intensive and strongly disturb the turf. This study proposes an earthworm-inspired subsurface robot as a low-disturbance loosening tool for compacted grassland soils. Design principles are abstracted from earthworm body segmentation, anchoring–propulsion peristaltic locomotion and corrugated body surface, and mapped onto a robotic body with anterior and posterior telescopic units, a flexible mid-body segment, a corrugated outer shell and a brace-wire steering mechanism. Kinematic simulations evaluate the peristaltic actuation mechanism and predict a forward displacement of approximately 15 mm/cycle. Using the finite element method and a Modified Cam–Clay soil model, different linkage layouts and outer-shell geometries are compared in terms of radial soil displacement and drag force in cohesive loam. The optimised corrugated outer shell combining circumferential and longitudinal waves lowers drag by up to 20.1% compared with a smooth cylinder. A 3D-printed prototype demonstrates peristaltic locomotion and steering in bench-top tests. The results indicate the potential of earthworm-inspired subsurface robots to provide low-disturbance loosening in conservation agriculture and grassland management, and highlight the need for field experiments to validate performance in real soils. Full article

Suction embedded plate anchors (SEPLAs) are widely used in offshore engineering, but their keying process is often accompanied by embedment loss, which reduces the holding capacity. To minimize embedment loss, inwardly rotating keying flaps have been introduced, though their kinematics and effects on embedment loss remain insufficiently understood. In this study, transparent soil model tests, combined with particle image velocimetry (PIV), were conducted to directly visualize the keying behavior of SEPLAs with inwardly rotating keying flaps. Five anchor configurations were tested, including a reference model without flaps and four models with flap lengths ranging from 0.2 to 0.5 times the anchor breadth. The results show that inwardly rotating keying flaps significantly reduce embedment loss, with the configuration featuring a flap length of 0.4 times the anchor breadth exhibiting the optimal performance. The findings provide valuable insight into the influence of flap length on SEPLA keying behavior and embedment loss, offering practical guidance for optimizing flap design. Full article