Search Results (192)

Gas-insulated substations (GISs) have been widely adopted in modern power systems due to their compact design and high reliability. However, the potential generation of toxic byproducts poses significant risks to manual operations such as gas pressure adjustments, highlighting the necessity for robotic deployments. This paper proposes a Robot Operating System (ROS)-based robotic framework with human–robot collaborative autonomy for GIS operations. The framework employs a 6-degree-of-freedom (6-DOF) robotic arm, integrated with a motion control system for trajectory execution, a visual perception system enhanced by the coordinate attention (CA) mechanism for small-scale detection and localization, and a communication system for data exchange. The framework improves the accuracy of component perception and enables fine manipulation in complex environments, reducing reliance on manual intervention while facilitating a safe collaborative autonomous workflow through dynamic adjustment of autonomy level and control authority. Experimental results on the representative gas pressure adjustment task demonstrate an autonomous operational success rate exceeding 90% under the proposed configuration in dynamic scenarios. By enhancing safety and precision, this study advances robotic solutions for hazardous operations and lays a foundation for broader infrastructure applications. Full article

Inland waters such as lakes, reservoirs, rivers, fish ponds, and confined hydraulic structures impose turbidity, shallow-water acoustic multipath, dense boundaries, dynamic biological interference, and limited communication on underwater robots. These coupled constraints can simultaneously degrade sensing, localization, mapping, planning, and control. This structured narrative review synthesizes peer-reviewed English-language evidence on underwater navigation, with emphasis on multisensor fusion localization and the transfer of localization uncertainty into risk-aware planning. Visual, acoustic, inertial, velocity, depth, and external positioning measurements are compared by complementarity, observability, degradation modes, and integration cost. Filtering, sliding-window optimization, factor graphs, estimator switching, and hybrid model- and data-driven approaches are evaluated according to accuracy, real-time performance, robustness, and localization credibility. The review examines how covariance, sensing quality, collision probability, energy, platform dynamics, and task requirements affect maps, path costs, safety margins, trajectory tracking, and degraded operation. Evidence from simulation, public datasets, hardware-in-the-loop tests, tanks, and real waters is synthesized conditionally because study platforms, environments, ground truth, and metrics are heterogeneous. Reliable inland-water autonomy requires diagnosable heterogeneous sensing, integrity-aware localization, and coordinated feedback among localization, planning, and control. Mission-level evaluation should consider data validity, fault recovery, and safe completion rather than average localization error or shortest path alone. Full article

Automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) are among the key enabling technologies driving intelligent logistics and industrial automation. Despite their widespread adoption and rapid technological evolution, the literature often addresses AGV and AMR systems in a fragmented manner, lacking a structured methodological perspective that highlights their architectural foundations, levels of autonomy, and technological maturity. This paper presents a methodological survey of AGV and AMR technologies, focusing on system-level architectures and core functional components rather than isolated algorithms. The survey systematically analyzes key technological dimensions, including sensing and perception, localization and positioning strategies, navigation and path-planning approaches, communication infrastructures, and multi-robot coordination mechanisms. A clear distinction is drawn between classical AGV systems, which rely on fixed infrastructure and predefined routes, and AMR systems, which exhibit adaptive, perception-driven, and self-configuring behaviors enabled by artificial intelligence techniques. Rather than proposing new algorithms, this paper organizes existing approaches into a coherent framework that highlights technological transitions from infrastructure-dependent guidance to autonomous, data-driven navigation. Recent trends such as cloud–edge integration, learning-based navigation, scalable fleet management architectures, and cooperative multi-robot systems are reviewed and discussed from a methodological standpoint, emphasizing their role in increasing flexibility, robustness, and operational efficiency in industrial and logistics environments. The survey also addresses cross-cutting challenges, including system transparency, safety and certification, interoperability, and sustainability. Finally, this paper outlines research directions aligned with the principles of Industry 5.0, highlighting the need for human-centered, resilient, and scalable AMR and AGV systems capable of safe and explainable operation in complex industrial contexts. Full article

Unmanned aerial vehicle (UAV) swarms increasingly operate in GNSS-denied environments, where cooperative localization provides relative positioning by fusing onboard odometry with inter-agent range measurements, for which ultra-wideband (UWB) two-way ranging is a common infrastructure-free choice. Beyond accuracy, safe swarm autonomy needs a trustworthy measure of positioning uncertainty, since collision-avoidance and formation-keeping decisions derive their safety margins from the reported covariance. Under sustained agile flight, both are hard to achieve at once: motion within each time-division multiple access (TDMA) polling round induces a ranging bias well above the UWB noise floor, and reusing shared information across the network drives the reported covariance below the true error. To address these two problems jointly rather than in isolation, we propose a modular architecture coupling an online maximum-likelihood polynomial least-squares (MPLS) ranging front-end with a fading split covariance intersection (SCI) cooperative back-end through a per-link variance interface: online MPLS compensates the motion-induced bias and reports a calibrated, time-varying variance that fading SCI consumes as measurement noise while its continuous-time fading factor bounds the reused-information covariance. Monte Carlo simulation over anchored and anchor-free 16-node swarms shows the two effects to be empirically decoupled, with front-end ranging quality governing positioning accuracy and back-end correlation handling governing estimator consistency. The method attains sub-meter positioning accuracy in both settings and, without per-scenario tuning, keeps consistency—quantified by the average normalized estimation error squared (ANEES)—within a trusted band; comparably accurate extended Kalman filter and covariance-intersection baselines fall outside it, becoming overconfident and overconservative, respectively. It thus delivers the trustworthy uncertainty that safety-critical swarm decisions require in GNSS-denied flight. Full article

Loneliness among older men may remain unrecognized when unwanted relational disconnection is not explicitly named as loneliness. This qualitative longitudinal, multimethod study examined how 32 community-dwelling men aged 60 years and older in Lisbon, Portugal, experienced, interpreted, and communicated relational disconnection between January and July 2025. Data were generated through narrative life-history interviews, participant-generated photographic, audio, or written diaries, elicitation interviews, and follow-up interviews. Six interrelated themes were developed and interpreted processually as disruptions and absences, forms of disconnection, identity-protective meaning-making, and responses. The analysis distinguished topics deliberately elicited by the design from empirical refinements and unanticipated temporal or material insights. The four processes initially used to sensitize inquiry—nonrecognition, linguistic substitution, identity protection, and relational concealment—were therefore not treated as discoveries; the data clarified their boundaries, interactions, and reversibility. Four longitudinal patterns were used as non-exclusive interpretive summaries rather than participant classifications. The most distinctive findings were that connection depended not only on contact but on relational consequentiality—being expected, needed, consulted, and able to contribute—and that greater willingness to name loneliness could reflect interpretive change rather than worsening symptoms. Unnamed loneliness was inferred only where participants described unwanted relational disconnection; chosen and satisfying solitude was excluded. Responses should foster emotionally safe, reciprocal, and meaningful relationships while protecting autonomy. Full article

Deployable autonomy remains a key challenge for unmanned aerial vehicles (UAVs) operating in open-ended missions. Large language models (LLMs) and their multimodal variants, which can process visual and other sensory inputs, have introduced new capabilities for semantic perception, task reasoning, and language-conditioned control. However, these capabilities do not by themselves produce flight-ready autonomy. We structure our analysis around a Perception–Cognition–Action (P–C–A) framework. At each layer, we identify the capabilities contributed by LLM-based components and examine how they connect to existing flight modules through input specifications, output representations, architectural coupling patterns, and safety mechanisms. Across the surveyed systems, LLMs extend UAV autonomy beyond fixed perception categories, scripted task plans, and pre-programmed controllers. However, field deployment depends on whether model outputs can be transformed into representations that downstream modules can parse, verify, and safely execute. Without adequate validation, captions, task plans, code, waypoints, and control commands may become failure points that propagate across the P–C–A loop. Our analysis highlights structured output contracts, independent safety barriers, and deterministic fallback mechanisms as key design elements for the reliable integration of LLM capabilities into UAV platforms. Full article

UAVs increasingly rely on accurate SLAM for aerial mapping and inspection in GPS-denied environments. 3D Gaussian Splatting (3DGS) has opened a new direction for UAV mapping by allowing SLAM systems to build dense, photorealistic, and renderable maps. Yet in 3DGS SLAM the map is optimized from the pose graph, so a false loop closure can deform both the UAV trajectory and the Gaussian map consumed by downstream UAV autonomy. Reliable loop admission is therefore relevant to safe GPS-denied operation because it protects the state and map estimates on which autonomous functions depend. The present work evaluated this upstream estimation-integrity problem; it did not measure closed-loop guidance, control, or navigation-safety outcomes. We address the loop-admission problem that arises after a place-recognition (PR) module proposes a candidate loop and relative-pose seed. GSSeq is a rendered-reference sequential verifier that uses the current Gaussian map as active evidence before inserting a loop factor. It renders RGB-D references with the PR seed, checks LiDAR/rendered-depth consistency and image/rendered-reference consistency over active support, and propagates the seed through a short query trajectory window. A loop is admitted only when this evidence remains geometrically supported and photometrically stable. On fixed LiDAR-PR candidate sets spanning MARS-LVIG, MUN-FRL, and independent NTU-VIRAL aerial sequences together with ground-mobility benchmarks, GSSeq provides a competitive precision-oriented operating point while suppressing false loop admissions. Thresholds calibrated only on NTU-VIRAL spms_01 combine rendered RGB agreement with LiDAR-submap geometry and are then frozen for spms_02. On this held-out sequence, GSSeq rejects all seven false-positive BTC factors while retaining one of three true-positive factors. The trajectory-to-map experiment reduced ATE RMSE from $2.609\mathrm{m}$ to $1.417\mathrm{m}$ and improved selected-view PSNR from $13.80\mathrm{dB}$ to $16.46\mathrm{dB}$. These results show that rendered verification can preserve an aligned, renderable UAV trajectory-map pair before unsupported loop factors reshape the SLAM map. Full article

The connected operating room captures, transmits, and displays data, but it does not learn. This perspective presents Chirurgie 4.0 (C40), a French-led initiative born within the Commission Innovation of the Académie nationale de chirurgie, which proposes not only a concept but a method for the safe adoption of artificial intelligence (AI) in surgery. At its core is the C40 Maturity Model of the Operating Room, a human-governed “surgical world model” describing the transition from the Smart OR to the Learning OR across six levels, from the conventional operating room to a sovereign, federated network of surgical world models. We situate surgical autonomy on an explicit six-level scale, show that autonomous devices are already an accepted clinical reality in fields such as interventional cardiology, ophthalmology, neuro- and orthopedic surgery, and argue that governance must be native rather than retrofitted, through a Cognitive Governance Layer resting on human oversight, explainability, auditability and agent governance. We describe the economic and sovereignty stakes specific to intelligent surgical technologies and set out the design of the 2026 C40 field survey, whose results will feed a Livre Blanc for public decision-makers. C40 offers five steps that can genuinely be climbed, and a method for climbing them safely, with the surgeon retaining final clinical authority at every step. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly introduced into clinical trial operations, but operational usefulness does not imply regulatory or site-level readiness. This review maps AI applications across trial operations and proposes an author-developed, unvalidated site-level readiness framework and preliminary deployment-decision aid. Methods: We conducted a structured narrative review with evidence mapping of peer-reviewed literature, regulatory documents and contextual sources (2020–2026). AI use cases were mapped by lifecycle stage, technical-validity reporting, evidence maturity, autonomy, trial impact and governance implications. Maturity was assessed with an author-developed 0–8 score intended for transparent mapping, not risk-of-bias grading. Results: The comparatively strongest evidence concerns patient–trial matching and eligibility assessment, which nonetheless reached only moderate maturity, being evaluated mainly retrospectively or in simulated screening rather than inside a live trial; no use case reached the highest band. Other applications remain less mature or context-dependent. Recurrent risks include hallucination, automation bias, weak local validation, limited auditability, model drift and unclear accountability. We propose a preliminary framework linking evidence maturity, technical validity, AI autonomy, trial impact and site capacity. Conclusions: AI readiness in clinical trial operations should be assessed at the level of the AI-enabled workflow rather than the model alone. Safe adoption requires context-specific technical and operational validation, human accountability, auditability, lifecycle monitoring and alignment with Good Clinical Practice. Full article

Reinforcement learning (RL) has emerged as a promising approach for autonomous surgical robotic subtasks. Recent advances include deep reinforcement learning (DRL), imitation learning (IL), and vision–language–action (VLA) models. However, current evidence remains fragmented across simulation benchmarks, task-specific demonstrations, and limited clinical studies. Existing reviews primarily focus on RL algorithms, while the broader pathway from algorithm development to clinically deployable surgical autonomy has not been comprehensively synthesised. This PRISMA 2020-guided systematic review examines RL, IL, safe RL, simulation-to-real (sim-to-real) transfer, foundation models, VLA systems, and regulatory readiness in surgical robotics. We searched IEEE Xplore, PubMed/MEDLINE, Embase, Scopus, Web of Science, the Cochrane Library, ACM Digital Library, arXiv, and medRxiv for studies published between January 2015 and March 2026, with additional studies identified through backward citation tracing. Eligible studies proposed novel RL, imitation learning, or foundation-model approaches for surgical robotics with empirical validation in simulation or on physical robotic platforms. Two reviewers independently extracted data using a predefined coding scheme, and a third reviewer resolved disagreements. Owing to substantial heterogeneity in platforms, tasks, and outcome measures, a quantitative meta-analysis was not feasible; therefore, the evidence was synthesised narratively using a comparative framework. A total of 220 studies met the inclusion criteria, covering eleven active surgical RL platforms, seven paired sim-to-real studies, emerging foundation-model architectures, and three FDA-cleared robotic systems exhibiting Level 3 autonomy. Available comparative studies suggest that hierarchical approaches can outperform flat policies in long-horizon tasks, while language-conditioned models demonstrated promising multi-step surgical capabilities. Seven paired simulation-to-real studies were identified, encompassing tissue retraction, guidewire navigation, and surgical cutting tasks. Sim-to-real performance gaps varied substantially by task and metric, with success-rate gaps ranging from −10 to 50 percentage points (negative values indicating better real-world than simulated performance), while paired mean spatial errors differed by at most 0.61 mm. Most studies employed domain randomization or visual domain adaptation; hierarchical reinforcement learning demonstrated advantages over flat policies in multi-step surgical tasks. Explicit safety-constrained methods (CPO, CBF, and SER), formal verification, and regulatory-aligned evaluation were reported in fewer than 3% of applied studies. Most evidence remained simulation-based, with no reported autonomous RL execution in vivo in humans. Overall, RL-based surgical robotics appears mature at the simulation stage but remains preclinical for autonomous clinical deployment. Future progress requires stronger sim-to-real validation, multimodal safety-aware architectures, alignment with IEC 62304, ISO 14971, FDA guidance, and the EU AI Act, and open benchmarks that jointly evaluate performance, safety, and surgeon trust. Full article

Smart buildings increasingly depend on dense, distributed sensing infrastructures to improve energy efficiency, indoor environmental quality and operational flexibility. However, large-scale IoT/WSN deployment is still constrained by wiring effort, battery maintenance and limited access to sensing locations. Energy harvesting (EH) offers a promising approach toward low-maintenance and partly autonomous sensing, but its practical value in building automation depends on more than the output of individual transducers. This article presents a structured review of EH for IoT/WSN and edge-enabled building automation, focusing on smart-building, Building Management System (BMS) and Building Automation and Control System (BACS) contexts. Light-based, thermoelectric, mechanical, RF/wireless-power-transfer and hybrid harvesting technologies are interpreted through a system-oriented chain linking energy sources, power management, storage, communication, adaptive operation, gateways, diagnostics and edge intelligence. The synthesis shows that EH is most promising for low-duty-cycle environmental monitoring, envelope and façade sensing, occupancy and human–building interaction, airflow-related sensing, technical monitoring and retrofit automation. The main challenges concern the transition from device autonomy to sensing-service autonomy, complete-node evaluation under real building conditions, interoperability with supervisory systems and diagnostic interpretation of intermittent operation. Further research is also needed on lifecycle value assessment and safe transferability toward remote, temporary, resilient and closed ecological infrastructure applications. Full article

Co-Creative Robotics combines computational creativity, robotic embodiment, and human–robot collaboration to support or generate creative behavior in physical and social environments. As these systems become more autonomous, data-intensive, and interactive, cybersecurity and privacy can no longer be treated as external safeguards added after creative functionality has been designed. This PRISMA-informed review investigates whether principles of cybersecurity-by-design and privacy-by-design can be integrated into Co-Creative Robotics without constraining creativity, autonomy, and user agency. The database search covered ACM Digital Library, Google Scholar, IEEE Xplore, Scopus, SpringerLink, and Web of Science, and was complemented by two focused backward and forward snowballing iterations. From 623 database records, the final synthesis includes 27 primary studies. The results show that direct literature combining cybersecurity, privacy, and Co-Creative Robotics remains limited, but evidence from creative HRI, social-robot privacy, cyber-physical security, privacy-preserving interaction design, security modeling, and robot ethics supports a conditional answer. Integration is feasible when security and privacy mechanisms are adaptive, explainable, participatory, context-sensitive, and lifecycle-aware. However, the evidence on transparency-oriented privacy mechanisms is mixed: improvements in awareness or acceptance do not consistently translate into reduced disclosure or greater perceived safety. Rigid controls may constrain creative exploration, whereas well-designed controls can support trust, accountable autonomy, safe embodiment, privacy-aware interaction, provenance, and agency-preserving creativity. The review proposes a conceptual lifecycle-oriented research agenda for secure and privacy-aware Co-Creative Robotics. Full article

Access to safe and effective contraception is central to reproductive autonomy and public health, yet numerous contraceptive products have been associated with recalls, safety warnings, litigation, and allegations of insufficient risk disclosure. This article examines the harms associated with defective and insufficiently disclosed contraceptive products through the lens of white-collar and corporate crime. Drawing on scholarship on corporate harm, consumer fraud victimization, and gendered inequality, the article argues that these harms are not simply isolated product failures but reflect broader patterns of organizational decision-making, information asymmetry, and regulatory limitation within pharmaceutical systems. Using documented case illustrations involving the Dalkon Shield, oral contraceptives, Mirena, and NuvaRing, the analysis identifies recurring patterns involving delayed recognition of harm, contested disclosure of risk, and reliance on civil litigation as a primary mechanism of accountability. The article further emphasizes the gendered distribution of these harms given women’s disproportionate reliance on contraceptive technologies. By conceptualizing faulty contraceptive products as a form of corporate harm and consumer victimization, this article extends criminological scholarship on white-collar harm into reproductive health contexts and highlights how institutional systems shape the production and distribution of consumer risk. Full article

Current artificial intelligence evaluation often relies on static benchmarks and task-specific performance metrics, which are useful for system comparison but limited in assessing developmental progression, cognitive architecture, adaptive transfer, and real-world applicability. Building on developmental approaches to AGI testing, including our earlier conceptual proposal, this article develops an operational formulation of the General–Specialized–Applicable (GSA) framework for artificial general intelligence (AGI) evaluation. The framework organizes evidence into three non-interchangeable stages: the General stage, which assesses foundational capacities for open-ended generalization, value-oriented regulation, and autonomy; the Specialized stage, which evaluates stable domain-specific competence; and the Applicable stage, which examines whether such competence can be deployed safely and robustly in realistic environments. The article further introduces a dynamic task-generation pipeline and a provisional operational rubric for interpreting stage-specific evidence. An illustrative case study using household tasks in a simulated embodied environment illustrates how GSA can provide diagnostic information beyond aggregate benchmark scores by identifying capability gaps in current multimodal large language model (MLLM) agents. Rather than offering a final universal standard, the GSA framework provides an evaluation-oriented structure for connecting benchmark performance with architectural readiness, specialization, and deployment-level applicability. Full article

Modern power grids require safer and more reliable field operations, yet conventional robots often face limitations in unstructured environments because of rigid pre-programming and weak perception–action coupling. This review examines Embodied Intelligence (EI) as an emerging direction for enhancing power-system field operations. We first evaluate the environmental adaptability of morphological carriers, including quadrupeds, humanoids, and unmanned aerial vehicles, and then define the perception–cognition–execution closed-loop architecture used in this review. Three application domains are then examined. Intelligent inspection focuses on active perception and potential open-vocabulary object detection. Live-line maintenance emphasizes Sim-to-Real methods and shared autonomy, while disaster-response applications involve heterogeneous air–ground robotic coordination. The review also discusses the potential for EI to reduce human exposure to hazardous tasks and influence labor structures, while a regional text-based proxy illustrates differences in policy attention to digital infrastructure. Finally, we analyze major constraints, including hardware endurance under extreme climates, edge-computing latency, foundation-model uncertainty and hallucination, cybersecurity, and safety certification. Overall, EI should not be interpreted as a mature replacement for current utility practice; it is a developing technological direction whose safe deployment will require field validation, standardized evaluation, cybersecurity assurance, and continued human supervisory authority. Full article