Search Results (6,914)

The deterioration of civil infrastructure poses a significant threat to public safety, yet conventional manual inspections remain subjective, labor-intensive, and constrained by accessibility. To address these challenges, this paper presents a real-time robotic inspection system that integrates deep learning perception and autonomous navigation. The proposed framework employs a two-stage neural network: a U-Net for initial segmentation followed by a Pix2Pix conditional generative adversarial network (GAN) that utilizes adversarial residual learning to refine boundary accuracy and suppress false positives. When deployed on an Unmanned Ground Vehicle (UGV) equipped with an RGB-D camera and LiDAR, this framework enables simultaneous automated crack detection and collision-free autonomous navigation. Evaluated on the CrackSeg9k dataset, the two-stage model achieved a mean Intersection over Union (mIoU) of 73.9 ± 0.6% and an F1-score of 76.4 ± 0.3%. Beyond benchmark testing, the robotic system was further validated through simulation, laboratory experiments, and real-world campus hallway tests, successfully detecting micro-cracks as narrow as 0.3 mm. Collectively, these results demonstrate the system’s potential for robust, autonomous, and field-deployable infrastructure inspection. Full article

Human–machine shared control has been widely adopted to enhance driving performance and facilitate smooth transitions between manual and fully autonomous driving. However, existing authority allocation strategies often neglect real-time assessment of scenario complexity and driver workload. To address this gap, we leverage non-invasive eye-tracking devices and the 3D virtual driving simulator Car Learning to Act (CARLA) to collect multimodal data—including physiological measures and vehicle dynamics—for the real-time classification of scenario complexity and cognitive workload. Feature importance is quantified using the SHAP (SHapley Additive exPlanations) values derived from Random Forest classifiers, enabling robust feature selection. Building upon a Hidden Markov Model (HMM) for workload inference and a Model Predictive Control (MPC) framework, we propose a novel human–machine shared control architecture with adaptive authority allocation. Human-in-the-loop validation experiments under both high- and low-workload conditions demonstrate that the proposed strategy significantly improves driving safety, stability, and overall performance. Notably, under high-workload scenarios, it achieves substantially greater reductions in Time to Collision (TTC) and Time to Lane Crossing (TLC) compared to low-workload conditions. Moreover, the adaptive approach yields lower controller load than alternative authority allocation methods, thereby minimizing human–machine conflict. Full article

To ensure the safety of vehicles and occupants under failures or functional limitations of ego vehicles, a minimum risk maneuver (MRM) has been proposed as a key automated driving system (ADS) function. However, executing an MRM may pose certain potential risks when sensor failures occur. This study proposed an MRM strategy designed to enhance highway-driving safety during MRM execution under multiple sensor-failure conditions. A hazard and operability study analysis, based on an ADS behavior model, is conducted to systematically identify hazards, determine potential hazardous events, and categorize the associated safety risks arising from sensor failures. Within the proposed strategy, virtual objects are generated to account for potential hazards and support risk assessments. Adaptive MRM behavior is determined in real time by analyzing surrounding objects and evaluating time-to-collision and time headway. The strategy is verified by using a MATLAB–CARLA co-simulation environment across three representative highway scenarios with combined sensor failures. The result demonstrates that the proposed MRM strategy can mitigate collision risk in hazardous scenarios while effectively leveraging the remaining functional sensors to guide the ego vehicle toward an appropriate minimum risk condition during MRM execution. Full article

We analysed data collected at the Innsbruck Alpenzoo (Tyrol, Austria) over 33 years (1988–2020). We examined data from 4542 wild birds of 137 species that were rescued in the increasingly urbanised and densely populated Inn Valley around Innsbruck and examined the outcome of hospital treatment (survival or death); for a subgroup of 3440 birds, we examined the length of stay at the zoo. The birds were divided into nine different groups, and the reasons for admission were divided into nine categories to analyse how the reasons for admission and membership of a bird group influences rehabilitation success and the duration of care required. Orphaned birds, birds that had become entangled in man-made structures, and birds with unknown reasons for admission had the best survival rates (60%), while birds with physical injuries, victims of collisions, and attacks by cats had the lowest survival rates (37%). Survival rates were highest among areal insectivores (66%) and waterbirds (62%), and lowest among small songbirds (45%) and woodpeckers (<39%), which suffered disproportionately from the consequences of window collisions. The overall survival rate of hospitalised birds (51%) was higher, and the duration of care required (median 11 days) was especially shorter at Innsbruck Alpenzoo than at most other rehabilitation centres. We attribute this mainly to the professional care and varied, group-specific diet provided to the patients, which we describe in detail. We also discuss the problems and limitations of wild bird care for zoo staff in addition to their daily tasks. Thereby, it became apparent that the retirement of experienced bird carers at the beginning of the study period and the subsequent steady changeover of staff members had a negative impact on success rates. Full article

Research on Cooperative Driving Automation (CDA) has advanced to overcome the limited perception range of onboard sensors and the difficulty of inferring surrounding vehicles’ intentions by leveraging vehicle-to-everything (V2X) communications. This paper models how an autonomous vehicle receives cooperative sensing and cooperative maneuvering information generated at an edge roadside unit (edge RSU) that integrates roadside units (RSUs) with multi-access edge computing (MEC), and how the vehicle fuses this information with its onboard situational awareness and path-planning modules. We then analyze the performance gains of edge RSU-enabled services across diverse traffic environments. In a highway-merging scenario, simulations show that employing the edge RSU’s sensor sharing service (SSS) reduces collision risk relative to onboard-only baselines. For unsignalized intersections and roundabouts, we further propose a guidance-driven Hybrid Pairing Optimization (HPO) scheme in which the edge RSU aggregates CAV intents/trajectories, resolves spatiotemporal conflicts via lightweight pairing and time window allocation, and broadcasts maneuver guidance through MSCM. Unlike a first-come, first-served (FCFS) policy that serializes passage, HPO injects edge guidance as soft constraints while preserving arrival order fairness, enabling safe concurrent passage opportunities when feasible. Across intersections and roundabouts, HPO improves average speed by up to 192% and traffic throughput by up to 209% compared with FCFS under identical demand in our simulations. Full article

Miniature commercial trucks constitute a critical component of urban freight systems but face elevated crash risk due to distinctive driving patterns, frequent operation, and variable loads. This study quantifies how long-term and short-term driving behaviors jointly shape crash economic loss levels and identifies factors most strongly associated with severe claims. A driver-level dataset linking multi-source running behavior indicators, vehicle attributes, and insurance claims is constructed, and an enhanced Wasserstein generative adversarial network with Euclidean distance is employed to synthesize minority crash samples and alleviate class imbalance. Crash economic loss levels are modeled using a random-effects generalized ordinal logit specification, and model performance is compared with a generalized ordered logit benchmark. Marginal effects analysis is used to evaluate the influence of pre-collision driving states (straight, turning, reversing, rolling, following closely) and key behavioral indicators. Results indicate significant effects of inter-provincial duration and count ratios, morning and empty-trip frequencies, no-claim discount coefficients, and vehicle age on crash economic loss, with prolonged speeding duration and fatigued mileage associated with major losses, whereas frequent speeding and fatigue episodes are primarily linked to minor claims. These findings clarify causal patterns for miniature commercial truck crashes with different economic losses and provide an empirical basis for targeted safety interventions and refined insurance pricing. Full article

With the rapid advancement of science and technology, robotics is evolving towards more profound and extensive applications. Nevertheless, the inherent limitations of traditional industrial “caged” robots have significantly impeded the full utilization of their capabilities. Consequently, breaking free from these constraints and realizing human–robot collaboration has emerged as a new developmental trend in the robotics field. The collision-response mechanism, as a crucial safeguard for human–robot collaboration safety, has become a pivotal issue in enhancing the performance of human–robot interaction. To address this, an adaptive admittance control collision-response algorithm is proposed in this paper, grounded in the principle of admittance control. A collision simulation model of the AUBO-i5 collaborative robot is constructed. The effectiveness of the proposed algorithm is verified through simulation experiments focusing on both the end-effector collision and body collision of the robot, and by comparing it with existing admittance control algorithms. Furthermore, a collision-response experimental platform is established based on the AUBO-i5 collaborative robot. Experimental studies on end-effector and body collisions are conducted, providing practical validation of the reliability and utility of the proposed adaptive admittance control collision-response algorithm. Full article

Centralized UAV logistics controllers can achieve strong navigation performance in controlled settings, but they do not capture key deployment factors in crowdsourcing-enabled emergency logistics, where heterogeneous UAV owners participate with unreliability and dropout, and incentive expenditure and fairness must be accounted for. This paper presents a decentralized crowdsourcing multi-UAV emergency logistics framework on an edge-orchestrated architecture that (i) performs urgency-aware dispatch under distance/energy/payload constraints, (ii) tracks reliability and participation dynamics under stress (unreliable agents and dropout), and (iii) quantifies incentive feasibility via total payment and payment inequality (Gini). We adopt a hybrid decision design in which PPO/DQN policies provide real-time navigation/control, while GA/ACO act as planning-level route refinement modules (not reinforcement learning) to improve global candidate quality under safety constraints. We evaluate the framework in a controlled grid-world simulator and explicitly report stress-matched re-evaluation results under matched stress settings, where applicable. In the nominal comparison, centralized DQN attains high navigation-centric success (e.g., 0.970 ± 0.095) with short reach steps, but it omits incentives by construction, whereas the proposed crowdsourcing method reports measurable payment and fairness outcomes (e.g., payment and Gini) and remains evaluable under unreliability and dropout sweeps. We further provide a utility decomposition that attributes negative-utility regimes primarily to collision-related costs and secondarily to incentive expenditure, clarifying the operational trade-off between mission value, safety risk, and incentive cost. Overall, the results indicate that navigation-only baselines can appear strong when participation economics are ignored, while a deployable crowdsourcing system must explicitly expose incentive/fairness and robustness characteristics under stress. Full article

As global airspace operations grow increasingly complex, the risk of near-mid-air collisions (NMACs) poses a persistent and critical challenge to aviation safety. Traditional collision-avoidance systems, while effective in many scenarios, are limited by rule-based logic and reliance on transponder data, particularly in environments featuring diverse aircraft types, unmanned aerial systems (UAS), and evolving urban air mobility platforms. This paper introduces a novel, integrative machine learning framework designed to analyze NMAC incidents using the rich, contextual information contained within the NASA Aviation Safety Reporting System (ASRS) database. The methodology is structured around three pillars: (1) natural language processing (NLP) techniques are applied to extract latent topics and semantic features from pilot and crew incident narratives; (2) cluster analysis is conducted on both textual and structured incident features to empirically define distinct typologies of NMAC events; and (3) supervised machine learning models are developed to predict pilot decision outcomes (evasive action vs. no action) based on integrated data sources. The analysis reveals seven operationally coherent topics that reflect communication demands, pattern geometry, visibility challenges, airspace transitions, and advisory-driven interactions. A four-cluster solution further distinguishes incident contexts ranging from tower-directed approaches to general aviation pattern and cruise operations. The Random Forest model produces the strongest predictive performance, with topic-based indicators, miss distance, altitude, and operating rule emerging as influential features. The results show that narrative semantics provide measurable signals of coordination load and acquisition difficulty, and that integrating text with structured variables enhances the prediction of maneuvering decisions in NMAC situations. These findings highlight opportunities to strengthen radio practice, manage pattern spacing, improve mixed equipage awareness, and refine alerting in short-range airport area encounters. Full article

The Belgian constitution establishes that communities shall dispense neutral teaching that also respects both religious convictions and non-denominational philosophical choices. The application of this article has led to several conflicts with the religiosity of parents and students, among which one stands out eminently: the prohibition of the Islamic headscarf in schools in Flanders and Wallonia. It is precisely in the first of these communities, Flanders, where the collisions between the principle of neutrality and the religious freedom of Muslim women who intend to continue wearing this religious symbol continue to be reproduced, not only for reasons of religiosity, but also of identity. Signally, one of the main problems lies in the difficulties in delimiting the extension of the concept of neutrality as a limit to religious freedom, a task in which there does not seem to be agreement, neither among the main agents of the education system nor even among the courts of justice of the community. The best proof of this are the last two developments in the matter, the European Court of Human Right judgment in the Mykias case and the unsuccessful attempt to ban the Islamic veil in the province of Flanders. Full article

Public blockchains offer transparency and tamper resistance, but implementing national-scale lotteries directly on-chain is impractical because each bet would require a separate transaction, incurring substantial gas costs and facing throughput limitations. This paper presents an auditable lottery architecture designed to address these scalability challenges and eliminate the reliance on trusted third parties. The proposed approach decouples high-volume bet recording from on-chain enforcement. Bets are recorded off-chain in a transaction-positioned Merkle tree (TP-Merkle tree), while the service provider commits only the per-round root hash and summary metadata to an Ethereum smart contract. Each player receives a signed receipt and a compact Merkle proof (Slice), enabling independent inclusion checks and third-party audits. A programmable appeal mechanism allows any participant to submit receipts and cryptographic evidence to the contract; if misbehavior is proven, compensation is executed automatically from a pre-deposited margin. A proof-of-concept implementation demonstrates the system’s feasibility, and extensive experiments evaluate collision behavior, storage overhead, proof size, and gas consumption, demonstrating that the proposed design can support national-scale betting volumes (tens of millions of bets per round) while occupying only a small fraction of on-chain resources. Full article

Balancing safety with computational speed is a persistent challenge in autonomous navigation. While optimal pathfinders like A* are efficient, they fail to define the navigable “buffer” zone required for safe motion. Existing corridor generation methods attempt to bridge this gap but often suffer from heavy computational overhead or geometric instability. This paper introduces the Manhattan d-corridor, a framework that constructs strictly bounded, collision-free regions around a reference path. By combining systematic expansion with topological pruning, the algorithm guarantees structural minimality without sacrificing coverage. Experiments confirmed that the method is over two orders of magnitude faster than standard baselines. Crucially, while traditional methods suffered geometric collapse at high resolutions and dropped to unsafe collision ratios, the d-corridor maintained invariant safety (1.0) across all tests. This establishes the framework as a highly robust, real-time solution for resource-constrained robotics. Full article

Large density fluctuations near the QCD critical point can be probed via intermittency analysis, which involves measuring scaled factorial moments (SFMs) of multiplicity distributions in relativistic heavy-ion collisions. Intermittency reflects the emergence of scale invariance and self-similar structures, which are closely related to symmetry principles and their breaking near a second-order phase transition. We present a systematic model study of intermittency for charged hadrons in Au+Au collisions at $\sqrt{s_{\mathrm{NN}}}$ = 7.7, 11.5, 19.6, 27, 39, 62.4, and 200 $\mathrm{GeV}$. Using the cascade UrQMD model, we demonstrate that non-critical background effects can produce sizable SFMs and a large scaling exponent if they are not properly removed using the mixed-event subtraction method. To estimate the possible critical intermittency signal in experimental data, we employ a hybrid UrQMD+CMC model, in which fractal critical fluctuations are embedded into the UrQMD background. A direct comparison of the second-order SFM between the model and STAR experimental data suggests that a critical intermittency signal on the order of approximately $1.8\%$ could be present in the most central Au+Au collisions at RHIC energies. This study provides practical guidance for evaluating background contributions in intermittency measurements and offers a quantitative estimate for the critical signal fraction present in the STAR data. Full article

In dense Wireless Body Area Network (WBAN) environments, inter-network interference significantly degrades the reliability of medical data transmission. This paper proposes a novel MAC layer interference mitigation strategy that integrates interference-priority-weighted K-means++ clustering with graph-coloring-based time slot allocation. Unlike traditional coexistence schemes, our two-phase approach first partitions the network using a weighted metric combining physical distance and Interference Signal Strength (ISS), ensuring a balanced distribution of high-priority WBANs. Subsequently, we employ an enhanced Priority-Weighted Welch–Powell algorithm to assign collision-free time slots within each cluster. Simulation results demonstrate that the proposed strategy outperforms IEEE 802.15.4, CSMA/CA, and random coloring benchmarks. It reduces inter-network interference by 26.7%, improves priority node distribution balance by 65.7%, and maintains a transmission success rate above 80% under high-load conditions. The proposed method offers a scalable and low-complexity solution for reliable vital sign monitoring in crowded healthcare scenarios. Full article

To address the problems of low success rate, excessively long obstacle avoidance paths, and a large number of invalid nodes in path planning for robotic arms in complex environments, this paper proposes an obstacle avoidance path planning method based on the Cauchy Chaotic African Vulture Optimization Algorithm (CC-AVOA). By introducing a Cauchy perturbation term, the algorithm retains a certain degree of randomness in the later stages of the search, which helps to escape local optima. Furthermore, the introduction of a logical chaotic mapping increases the diversity of the initial vulture population, thereby improving the overall search efficiency of the algorithm. This paper compares the performance of the CC-AVOA algorithm with the standard African Vulture Optimization Algorithm (AVOA), the Rapid Exploratory Random Tree (RRT) algorithm, and the A* algorithm through simulation experiments in MATLAB R2024a under two-dimensional, three-dimensional, and robotic arm space environments. The results show that the CC-AVOA algorithm can generate paths with fewer nodes and shorter paths. Finally, the CC-AVOA algorithm is validated on both the RoboGuide industrial simulation platform and a physical FANUC robotic arm. The planned trajectories can be accurately executed without collisions, further confirming the feasibility and reliability of the proposed method in real industrial scenarios. Full article