Search Results (69)

Reliable localization and mapping are critical for intelligent robotic systems operating in dynamic indoor environments, where pedestrians and other moving objects can lead to erroneous feature associations, map contamination, and accumulated trajectory drift. To address these challenges, this study proposes the Boundary-Protected Semantic-Geometric Dynamic-Probability (Boundary-SGDP) framework, an enhanced red–green–blue-depth (RGB-D) visual simultaneous localization and mapping (SLAM) system based on boundary-protected semantic–geometric dynamic-probability estimation. The proposed method combines instance-level semantic priors generated by the YOLO26n-seg detector, a segmentation-oriented model in the You Only Look Once (YOLO) family, and the Segment Anything Model 2 (SAM2) with morphological region decomposition and RGB-D depth-edge detection. Potentially dynamic regions are further divided into dynamic interiors, semantic boundary protection bands, and geometrically informative depth-edge regions. Semantic and geometric cues are integrated to estimate a dynamic score for each feature, which is subsequently propagated to the MapPoint level as a dynamic probability. During pose optimization, these probabilities are used to adaptively adjust the weights of reprojection constraints, thereby reducing the influence of motion-contaminated observations while preserving geometrically valuable features around object boundaries and occlusion regions. Unlike conventional hard semantic masking strategies, Boundary-SGDP provides a soft and adaptive mechanism for handling dynamic observations. Experiments conducted on four dynamic walking sequences from the TUM RGB-D benchmark demonstrate that the proposed method achieves lower absolute and relative trajectory errors than the original ORB-SLAM3 system, while retaining substantially more boundary-related features. The results confirm the effectiveness of semantic–geometric fusion and boundary protection for robust visual localization and mapping in dynamic indoor scenes, and demonstrate the potential of the proposed framework for practical autonomous navigation and intelligent perception applications. Full article

The dual active bridge (DAB) converter is one of the most common types of isolated bidirectional power converters in modern spacecraft EPS owing to its galvanic isolation, bidirectionality, soft switching, and good controllability. However, the goal of power transfer maximization often clashes with real-world spacecraft EPS constraints, namely thermal compliance, reliability, and the accuracy of simplified models used for analysis. This paper proposes a physics-informed methodology to derive the practical operating range under single phase shift (SPS) control based on a rigorous piecewise time-domain representation. From this model, the steady-state initial condition, general closed-form RMS current expression, ZVS boundary condition, and ZVS-aware loss model linked to the junction-temperature estimate are derived. The validity domain of the fundamental harmonic approximation (FHA) is evaluated against the exact model across the full (φ, k) space, and a two-dimensional operating map superposing power contours, the ZVS limit, and the thermal limit is presented. For the baseline case study at k = 1.0, the thermal constraint limits the nominal feasible upper phase shift to approximately 35°, while the broader 15–45° range remains useful for design assessment and operation toward 45° requires lower effective resistance and/or improved thermal management. The normalized SPS power-transfer curve retains the same shape under variations in L and f_s, but RMS current, losses, and thermal feasibility must be reassessed for each converter design. The resulting closed-form framework provides a steady-state feasibility-evaluation tool for spacecraft EPS design and offers a computational basis for future supervisory constraint evaluation under varying voltage, load, and thermal conditions. Full article

Autonomous surface vessels (ASVs) operating in narrow and restricted waterways must follow a planned path while avoiding nearby static hazards and maintaining safe clearance from boundaries. This paper presents a LiDAR-based deep reinforcement learning framework for path following and static obstacle avoidance of an underactuated ASV. The vessel receives local pose information from a localization system and surrounding environment through a 2D LiDAR scan, which is converted into compact sector features using feasibility-inspired pooling method. A Soft Actor-Critic (SAC) policy is trained in simulation to output continuous rudder and propulsion commands, based on LiDAR features, estimated motion states, and path-relative errors. The policy is evaluated over 500 randomized simulation episodes ranging from 0–4 obstacles. The trained policy achieved an overall success rate of 95.0%, with an average cross-track error of 0.66 m. Obstacle and border collision rates are 3.80% and 1.20%, respectively; indicating that the policy can perform path tracking and collision avoidance in constrained layouts. A single field trial was then conducted in each of three fixed obstacle layouts using the model-scale Bluefin vessel. In these trials the policy executed on the physical platform and avoided static obstacles, with minimum obstacle clearances of 0.50–1.17 m. However, the field trajectories exhibit larger oscillations and longer path lengths than simulation, with an average RMS cross-track error of 1.14 m compared to 0.69 m in simulation. Full article

Fish-like biomimetic robots increasingly combine compliant structures, soft and variable-stiffness actuation, distributed sensing, and autonomous control, but cross-study comparison remains difficult because biological inspiration, robotic embodiment, test boundaries, and mission definitions are heterogeneous. We synthesize the field through a mechanism-to-evidence framework that links biological mechanisms to measurable descriptors, robotic embodiment, controlled interventions, task-oriented evidence, and conditional design principles. Across the literature, the transferable unit is not external resemblance but a functional mechanism whose advantage remains measurable after robotic integration. Three conclusions recur: dynamic performance depends on matching stiffness, actuation frequency, damping, and fluid loading within the intended operating range; morphology, sensing, control, power, and payload must be co-designed; and component, free-swimming, controlled-task, and field studies support different scopes of inference. We translate these findings into nine evidence-informed conditional design principles with explicit applicability limits and discriminating validation tests. Major gaps remain in wet-state dynamic characterization, complete reporting of power boundaries and kinematics, uncertainty and failures, matched task-level comparisons, and long-duration field validation. The resulting framework shifts evaluation from peak metrics and taxonomic labels toward task-conditioned, evidence-bounded design decisions. Full article

Accurate and timely monitoring of terrestrial oil spills is vital for ecological conservation and safe oilfield operations. To address the challenges of segmenting terrestrial oil spills in UAV remote sensing imagery, including blurred boundaries, irregular shapes, and complex background interference, we propose Fluid-SegFormer, a fluid-aware semantic segmentation model based on the lightweight SegFormer architecture. Fluid-SegFormer employs a Mix Transformer (MiT-B0) encoder to extract hierarchical multi-scale features and integrates a hierarchical fluid-aware optimization framework. Specifically, the Local Noise Gating (LNG) module suppresses background noise, the Horizontal–Vertical Perception Attention (HVPA) module enhances the structural representation of irregular oil spill regions, and the Fluid Soft Boundary Refinement Decoder (FSBRD) recovers fine boundary details. Experiments on a newly constructed high-resolution UAV terrestrial oil spill dataset demonstrate that Fluid-SegFormer achieves an mIoU of 87.84%, an IoU of 77.56%, and a Precision of 91.42%, effectively balancing computational efficiency and segmentation accuracy. These results demonstrate the potential of Fluid-SegFormer for practical deployment in UAV-based oil spill monitoring on edge devices. Full article

The integration of large-scale distributed energy resources (DERs) into distribution networks (DNs) brings challenges to the effective control of DNs. In traditional approaches, mathematical or reinforcement learning (RL)-based solution algorithms are commonly used. However, the exponential increase in the number of DERs reduces the effectiveness of these strategies. Mathematical methods struggle to cope with the dynamic uncertainty caused by the high penetration of renewable energy, while RL algorithms relying on global data training may violate multi-agent privacy protocols. This paper proposes a DNs cooperative optimization method based on resource aggregation and RL. To reduce optimization dimensionality and ensure the privacy of resource data, a dynamic aggregation strategy is employed to aggregate a large number of distributed energy resources into aggregated entities, and the adjustable active–reactive power boundaries of each aggregated entity are derived. To fully exploit the regulation capability of DNs, data centers (DCs), as novel devices, are considered as flexible loads. To improve the convergence speed of model training and decision-making accuracy, evolution strategies (ES) and prioritized experience replay (PER) are integrated into the Soft Actor-Critic (SAC) algorithm, respectively. The proposed method is validated on the IEEE 33-bus and IEEE 123-bus systems. The results demonstrate the effectiveness and superiority of the proposed method in ensuring the secure operation of DNs. Full article

Many transmission tower foundations in Northeast China are built in paddy fields, requiring construction during the wet season. Currently, no temporary bridge can be quickly installed and removed to transport people, materials, and machines without destroying the fields. To solve this problem, this study designs a prefabricated steel temporary bridge. The dynamic response of the bridge under a 25-ton moving crawler vehicle was studied using both field tests and numerical simulations. This work analyzes how the whole structure and its local parts respond to moving loads on soft paddy soil and calculates its maximum load capacity. The results indicate that: (1) empirical field measurements demonstrate that under the worst condition when the vehicle enters and leaves the bridge (end eccentric loading), the bridge does not lift or overturn; (2) numerical extrapolations indicate that when the 25 t vehicle passes at 1–10 m/s, the dynamic response is stable without resonance, and the dynamic stresses and displacements are always kept within safe limits; (3) the bridge exhibits reliable load capacity and resistance to deformation, with numerical extrapolations indicating that the theoretical ultimate bearing boundary of the system approaches 100 t. However, practical operational loads must incorporate appropriate safety factors and remain significantly below this extreme limit. This system allows heavy equipment to pass quickly and move frequently, while protecting the farmland from damage. Full article

To clarify the poorly understood soil-structure interactions flanking the pile cap, this study systematically investigates the sensitivity of the New Type Concrete Expanded-Plate (NT-CEP) pile system to variations in sub-cap silt profiles, specifically moisture content (12%~16%) and dry density (80%~90% compaction degree). Mechanical results indicate that the pile cap and expanded bearing plates operate via a robust synergistic load-sharing mechanism, with plastic failure zones localized beneath these components. Within conventional physical limits, fluctuations in moisture and density trigger less than a 4% variance in the ultimate compressive capacity, demonstrating the remarkable structural resilience of the internal compensatory load-transfer path. Based on the evaluated boundary conditions, a site-specific operational envelope featuring a minimum compaction degree of 80% and a critical moisture threshold below 14% is recommended as a preliminary reference. Nevertheless, explicit mechanical limitations must be rigorously addressed: these quantitative thresholds are strictly benchmarked against the scaled model testing utilizing a specific silt thickness and pile geometric stiffness ratio. Significant deviations in these parameters are expected under three distinct boundary constraints: (1) altered multi-axial stress paths inherent to complex interbedded geologies; (2) catastrophic matric suction loss and pore pressure accumulation driven by elevated groundwater tables; and (3) severe skin friction degradation common in thixotropic soft clays. Consequently, these indicators constitute a context-specific design envelope rather than a rigid universal standard, providing a mechanics-driven baseline for the gradient optimization of advanced NT-CEP foundations while delineating required calibration paths for future full-scale field instrumentation. Full article

Unsupervised domain adaptation (UDA) transfers knowledge from a labeled source domain to an unlabeled target domain and is particularly valuable in medical imaging, where dense annotations are costly and acquisition conditions vary. Cross-modality segmentation remains challenging because modality-dependent intensity and texture shifts alter image appearance, while teacher-generated pseudo-labels are often unreliable near anatomical boundaries. We propose a fractional texture-guided and boundary-aware perturbation-learning framework within a student–teacher scheme. On the source side, soft histogram transfer introduces target-related low-order intensity shifts. A multi-order fractional Gram discrepancy between shallow features of the intensity-transferred source and target images then provides a gradient signal for generating magnitude-normalized, range-clipped perturbations. This discrepancy is used as a perturbation cue rather than a direct alignment loss, exposing the student to target-relevant texture and edge-transition variation while preserving source annotations. On the target side, teacher logits are perturbed only within predicted boundary bands to model local contour uncertainty. Box-counting fractal boundary complexity guides the boundary-band width and logit perturbation scale and, together with predictive entropy, regulates pseudo-label supervision. Across five adaptation tasks, the proposed method achieves three-seed mean ± standard deviation Dice scores of 89.24 ± 0.12% and 82.01 ± 0.10% for cardiac MR→CT and CT→MR, 88.65 ± 0.29% and 90.43 ± 0.22% for abdominal MR→CT and CT→MR, and 84.76 ± 0.25% for bSSFP→LGE adaptation. Within the protocol-aware benchmark comparisons, the proposed method attains the highest average Dice score on four of the five tasks and is within 0.07 percentage points of the highest reported value on abdominal CT→MR. Ablation and operator-replacement studies further indicate that the source- and target-side pathways provide complementary benefits. Because all auxiliary perturbation and reliability-weighting modules are used only during adaptation, deployment requires only the adapted segmentation network, without additional inference-time modules or parameters. Full article

Soft robots built from compliant materials and deformable structures are increasingly used in medical intervention, wearable assistance, delicate manipulation, and environmental exploration, where conventional rigid robots are limited by high mechanical impedance and poor morphological adaptability. However, their transition from laboratory prototypes to deployable systems remains constrained by coupled bottlenecks in materials, actuation, sensing, modeling, control, energy supply, and manufacturing. This review summarizes recent advances in soft robotics through an evaluative framework covering actuation and materials, modeling and simulation, control strategies, multimodal sensing, and representative applications. Instead of treating these topics as independent descriptions, we compare the underlying mechanisms, measurable performance indicators, strengths, limitations, and application boundaries. Three conclusions emerge. First, no single actuation strategy can simultaneously maximize output force, response speed, energy efficiency, durability, miniaturization, and untethered operation. Second, high-fidelity continuum models improve physical accuracy but remain difficult to use for real-time control, whereas reduced-order and data-driven models improve efficiency at the cost of generalization, interpretability, or contact fidelity. Third, practical soft robots will depend on system-level integration of embedded sensing, physics-informed learning, robust control, reliable materials, and scalable fabrication. Future progress should therefore prioritize standardized benchmarks, lifecycle reliability, energy-autonomous operation, and task-specific comparisons with rigid robotic systems. Full article

High penetrations of distributed energy resources make active distribution networks strongly time-varying, nonlinear, and spatially coupled, which limits the online applicability of centralized voltage/reactive-power optimization. This paper proposes a data–physics fusion dynamic partitioning method for fast power coordination. A physics-based rolling partition baseline is first developed by integrating node operating behavior, voltage/reactive sensitivity, electrical distance, and feeder topology, providing an interpretable and efficient partitioning scheme for normal operating conditions. For high-volatility and strongly coupled scenarios, a heterogeneous dynamic graph and a heterogeneous spatio-temporal graph attention network are introduced to learn control-oriented latent node embeddings. Physical regularization, boundary-coupling penalties, and temporal smoothing constraints are further embedded into soft clustering to reduce cross-partition coupling and partition fluctuation. Tests on the IEEE 33-bus, IEEE 123-bus, and practical Feeder Z systems show that the dynamic partition closely approximates global OPF results, achieving normalized costs of 1.00017 and 1.00099 on the two IEEE systems with 74.3% and 83.2% time reductions. It further reduces the Feeder Z fixed-partition cost gap by 88.0%, while HST-GAT lowers boundary P/Q exchanges by 1.55%/6.57% under volatile conditions. Full article

Decision-making problems often involve simultaneous sources of uncertainty: interval-valued hesitation in expert judgments, positive–neutral–negative assessments, parameter-dependent evaluations, and boundary-region indiscernibility. To address these combined forms of uncertainty, this paper develops an interval-valued spherical fuzzy soft rough set (IVSFSRS) framework. The model integrates interval-valued spherical fuzzy information, soft parameterization, and rough lower–upper approximations in a unified approximation space. In response to the need for a more explicit theoretical foundation, an IVSF soft relation is formally defined and its role in constructing the approximation operators is clarified. The paper also presents basic operations, Hamacher-type extensions for the lower and upper approximations, and proof sketches showing the validity of the proposed operators. Furthermore, an IVSFSRS-based multi-criterion group decision-making procedure is developed, and a TOPSIS-oriented formulation is outlined to improve practical applicability. A demonstrative location-selection example, comparative analysis with related models, and sensitivity discussion illustrate the expressive power, robustness, and limitations of the proposed approach. Full article

After a severe outage occurs, restoring a distribution network can take from several hours to days, making the secure and stable operation of restoration areas (RAs) critical. During a post-disaster partitioned operation, asymmetric controllable distributed generator (CDG) regulation capacity, non-controllable distributed generator (NDG) fluctuation risks, and concentrated high-value loads cause significant inter-area power imbalances. Soft open points bridge this resource gap by integrating electric vehicle charging directly into soft open points via vehicle-to-grid (V2G) technology; the resulting electric vehicle-embedded soft open points (EV-SOPs) acquire storage-like energy transfer capability. This paper proposes a boundary–node coordinated optimization strategy for post-disaster RA operation, which integrates CDGs, NDGs, smart switches, and EV-SOPs. Firstly, the boundary dynamic updating model with a multi-homogeneity indicator—load importance, NDG fluctuation risk, and CDG flexibility—enables adaptive resource allocation. Secondly, the optimal operational model of RA is formulated considering the various characteristics of facilities and topology constraints. Thirdly, EV-SOP uncertainties in response reliability, discharge power, and energy capacity are characterized by Bernoulli, log-normal, and truncated normal distributions, reformulated into a tractable mixed-integer quadratically constrained programming via chance-constraint interval linear transformation, and solved by a sequential weight-based priority search with hot-start strategy. Case studies on the IEEE 123-bus system verify the effectiveness of the proposed method. Specifically, the dynamic boundary strategy reduces the comprehensive weighted index by up to 29.10%; physical feasibility truncation reduces EV-driven load loss from 3.2073 MW to 3.1038 MW; and the sequential weight-based priority search with hot-start strategy achieves a cone constraint satisfaction measure of 9.3175 × 10⁻⁷, confirming robust convergence. Full article

Natural gas hydrates are regarded as a vital strategic energy resource for the future owing to their high energy density and clean combustion characteristics. To facilitate research into the physical and mechanical properties of pressure-maintained hydrate samples, this paper presents an integrated multi-parameter online analysis system capable of rapidly measuring the P-wave velocity, electrical resistivity, thermal conductivity, and shear strength of core samples under pressure-maintaining conditions. The system comprises hardware acquisition boards based on ZYNQ and ARM platforms, specialized measurement probes, and comprehensive data acquisition and analysis software. To mitigate the susceptibility of P-wave signals to noise interference, an improved denoising algorithm combining Empirical Mode Decomposition (EMD) and wavelet thresholding is proposed. By employing autocorrelation function analysis, the algorithm identifies the transition boundary between noise-dominated and signal-dominated Intrinsic Mode Functions (IMFs), subsequently applying wavelet soft-thresholding to the noise-dominant components. Experimental results demonstrate that the proposed algorithm achieves a superior signal-to-noise ratio (SNR) compared to traditional EMD methods, particularly under low SNR conditions. System validation indicates measurement accuracies of 3.2% for P-wave velocity at 20 °C, 1.76% for electrical resistivity at 25 °C, and within 7% for both thermal conductivity and shear strength. Furthermore, sea trials conducted aboard the “HAIYANG SHIYOU 708” drilling vessel confirm that the system operates stably and effectively fulfills the requirements for deep-sea core parameter characterization. Full article

The increasing deployment of off-grid photovoltaic–battery energy storage systems (PV–BESSs) has intensified operational demands on battery energy storage, particularly when second-life lithium-ion batteries are employed. Due to aging-induced increases in internal resistance and reduced thermal margins, second-life batteries are more vulnerable to high-current operation at a low state-of-charge (SOC), which aggravates heat generation and accelerates degradation. In this study, an SOC-dependent soft current limiting strategy is proposed that reshapes the discharge current reference under low-SOC conditions while maintaining fixed SOC limits, thereby targeting current-domain protection rather than SOC-boundary adaptation for reliable off-grid operation. The proposed method introduces two SOC thresholds to gradually derate the allowable discharge current, preventing abrupt current changes near the lower SOC bound. A unified MATLAB/Simulink-based framework is developed for a 24 h representative off-grid PV–BESS scenario using a second-order equivalent circuit model coupled with a lumped thermal model. Simulation results show that the proposed current shaping reduces low-SOC current stress and associated Joule heating, leading to moderated temperature rise, while only slightly affecting the unmet load under the tested conditions. These findings indicate that SOC-dependent current shaping can provide a control-oriented means to reduce low-SOC electro-thermal stress in second-life batteries within the studied off-grid PV–BESS framework. Full article