Search Results (1,768)

This paper develops an extended Cucker–Smale model that integrates nonlinear velocity alignment with state-dependent binding forces to achieve stable, collision-free flocking in multi-agent systems. Our framework introduces two dedicated control mechanisms: a velocity-dissipative term $\left(K_1\right)$ for accelerated convergence, and a distance-regulating term $\left(K_2\right)$ for formation cohesion and collision avoidance, which collectively ensure stable flocking. Rigorous Lyapunov analysis establishes provable guarantees for asymptotic velocity alignment and collision safety under verifiable initial energy conditions. Numerical simulations validate the theoretical predictions for a 20-agent swarm; scalability analysis demonstrates effective coordination in systems of up to 100 agents and reveals that velocity synchronization improves substantially—with errors decreasing by nearly two orders of magnitude—as $K_2$ increases from 0.05 to 0.50. A Pareto-optimal parameter region ($K_2\in [0.15,0.30]$) is identified, which achieves sub-centimeter-per-second alignment accuracy while maintaining energy consumption below 35% of the baseline. The proposed framework provides a theoretically rigorous yet practically viable solution for applications demanding guaranteed safety and precise coordination, such as UAV formations, robotic swarms, and autonomous vehicle platoons. Full article

High-Mix Low-Volume (HMLV) manufacturing increasingly relies on heterogeneous robot fleets, but automatic generation of vendor-specific robot control code remains difficult due to platform fragmentation and safety-critical feasibility constraints. Although recent Large Language Model (LLM)-based approaches have shown promise for translating natural language into robot programs, they remain largely limited to single-platform or simulation-oriented settings and are vulnerable to physical hallucination, including spatially inconsistent commands and dynamically infeasible motions. This paper proposes a Digital Twin-integrated verification framework for adaptive control code generation in heterogeneous robot systems. The framework uses a structured intermediate task representation to support runtime spatial grounding, robot selection, pre-execution dynamics validation, and adaptive motion scaling before vendor-specific code generation and execution. Evaluation on 170 task-description scenarios and eight robot selection tasks showed improved ranking discriminability in lightweight stress cases where conventional baselines exhibited limited separation. In addition, adaptive dynamics scaling enabled safe execution in all analytically verified test cases, compared with 50% without scaling. These results suggest that Digital Twin-grounded verification and adaptive feasibility control can improve the reliability of LLM-based multi-vendor robot programming and help mitigate physical hallucination in heterogeneous robot systems. Full article

Autonomous path planning in obstacle-dense environments remains challenging for swarm intelligence methods due to infeasible initialization, insufficient exploration–exploitation balance, and poor trajectory smoothness for real-robot execution. To address these issues, this paper proposes a Kinematic-Aware Improved Hippo Optimization algorithm (KA-IHO) for mobile robot path planning. The proposed method integrates four components: an elite safety pool initialization strategy to improve feasible solution generation in dense maps, a hierarchical elite-scout update mechanism to better balance global exploration and local exploitation, anti-stagnation mechanisms including a Population Stagnation Restart strategy and a 10-Direction Radial Micro-Search to guarantee high feasibility rates across all map complexities, and a late-stage Laplacian Line-of-Sight Ironing Operator to reduce path redundancy and improve trajectory smoothness. Comparative experiments are conducted on five reproducible grid maps with different complexity levels ($40\times 40$ and $80\times 80$), where KA-IHO is evaluated against six representative algorithms, including HO, SBOA, PSO, GWO, ARO, and INFO, over 20 independent runs. The results show that KA-IHO consistently achieves collision-free planning and obtains lower mean fitness values with smaller standard deviations than the compared methods, indicating improved robustness and solution quality. In addition, hardware closed-loop experiments on a differential-drive mobile robot demonstrate that the planned paths can be executed reliably in real environments, with trajectory tracking errors controlled within $\pm 4$ cm. Full article

In recent years, deep learning (DL) has emerged as a transformative technology with significant potential to advance the Architecture, Engineering, and Construction (AEC) industry. DL enables automation, intelligent decision-making, and predictive analytics across various phases of construction, including design, site monitoring, safety management, and facility operations. Despite its growing adoption, research on the comprehensive methods, practical challenges and emerging opportunities of DL in the AEC industry remains limited. This study presents a state-of-the-art review of DL applications in the AEC industry by focusing on key methods, challenges, emerging opportunities and future research directions. A systematic literature review (SLR) was conducted in this study. Three major DL methods applied in the AEC industry were examined: (i) data-driven computer vision, (ii) natural language processing (NLP), and (iii) generative and simulation-based methods. Key challenges were identified: (i) data scarcity issues, (ii) high computational requirements, (iii) limited generalization across projects, (iv) human factors and resistance to adoption, and (v) lack of standardization and interoperability. Additionally, emerging opportunities and future research directions are highlighted: (i) advanced construction site monitoring and safety management, (ii) automated design and generative modeling, (iii) predictive maintenance and facility management, (iv) integration with robotics and autonomous construction systems, and (v) smart project management and decision support systems. This study advances a holistic understanding of DL in the AEC industry by systematically synthesizing current methods, challenges, and emerging trends. It establishes a structured foundation for future research to overcome technical, practical, and organizational challenges, thereby supporting the scalable, intelligent, and sustainable transformation of construction practices. Full article

Generally, land seismic data acquisition in arid areas is a labor-intensive, costly, and challenging process, often hindered by challenging terrain and safety risks. To overcome these limitations, we propose the integration of autonomous Unmanned Aerial Vehicles (UAVs) into land seismic data acquisition, enabling efficient data collection in difficult, inaccessible terrain. This is a cooperative mission workflow combining a Scouting UAV for high-resolution aerial scouting, followed by the swarm deployment of an Autonomous Seismic Acquisition Device (ASAD) for seismic data recording. The cooperative system allows for precise landing and subsequent deployment of seismic sensors in optimal locations. Previously, we demonstrated the applicability of passive seismic recorded with ASAD drones to near-surface characterization. This study covers the results of a field trial, where both the ASAD and Scouting UAV systems successfully acquired high-resolution seismic data with an active source, comparable to that of a conventional seismic data acquisition system. The results show that the ASAD seismic data exhibit a slightly higher noise level due to coupling variances and the fact that geophones were hardwired into 9-sensor arrays. However, due to its single-point sensing nature, it yields a superior frequency bandwidth, making it suitable for imaging shallow anomalies. The system underwent P-wave refraction tomography modeling and accurately detected a shallow subsurface cavity, showcasing its potential for near-surface characterization and shallow geohazard identification. This heterogeneous robotic system can support seismic data acquisition by enhancing safety, improving efficiency, and streamlining equipment mobilization, while minimizing environmental footprint. Full article

All-inorganic cesium lead iodide (CsPbI₃) has been investigated for more than a decade as an absorber for perovskite photovoltaics thanks to its attractive bandgap, thermal robustness compared with hybrid perovskites, and compatibility with tandem concepts. Yet, despite remarkable efficiency progress, CsPbI₃ remains far from widespread commercialization. The core roadblock is the metastability of the photoactive black perovskite phases (α/γ/β) against transformation to the photoinactive yellow δ-phase under realistic conditions, amplified by defect chemistry, ion migration, and interfacial reactions. Additional barriers arise from scale-up constraints (film uniformity, throughput, solvent management), long-term operational stability (humidity, heat, UV, bias), and environmental/safety requirements, especially lead containment, sequestration, and end-of-life strategies. This review critically analyzes the intertwined physical, chemical, and engineering factors that still limit CsPbI₃ deployment, with emphasis on how solutions in one domain can fail without co-design in others. This review summarizes state-of-the-art stabilization strategies (size/strain engineering, additive/doping routes, surface/interface passivation, and encapsulation), highlight scalable manufacturing pathways including solvent-minimized and vacuum-assisted approaches, and discuss lead-mitigation technologies such as Pb-adsorbing functional layers. Finally, I argue that artificial intelligence (AI)—from machine-learning stability models to process monitoring, robotic optimization, and digital twins—has become essential to navigate the enormous parameter space of CsPbI₃ materials and manufacturing. It concludes with actionable recommendations and future directions toward bankable, scalable, and sustainable CsPbI₃ photovoltaics. Full article

Background: Technology such as telerehabilitation, virtual reality, robotics, and wearable systems are reshaping pediatric physiotherapy. While evidence remains fragmented, there is little guidance on how these approaches can be integrated into coherent, family-centered care pathways. Objective: To develop the Digital Pediatric Physiotherapy Framework (DPPF) based on a systematic review of randomized evidence on digital interventions in pediatric physiotherapy. Methods: Several databases were searched for randomized trials published after 1 January 2020, including PubMed, Web of Science Core Collection, and Google Scholar. The included studies assessed the results of physiotherapist-delivered or physiotherapist-supervised digital interventions in children and adolescents aged 18 and younger. Population, intervention, outcome, implementation, and safety data were extracted. Considering the substantial heterogeneity of the findings, they were synthesized narratively. Cochrane RoB 2 was used to assess risk of bias, and GRADE was used to evaluate certainty of evidence. Results: Twenty-nine trials involving 1196 participants were included. Most studies examined virtual reality and gaming-based interventions, with fewer evaluating telerehabilitation/tele-exercise and robotic or wearable technologies. Digital interventions were most often directed at body-function and activity-level outcomes, while participation outcomes were less frequently studied. The strongest evidence supported short-term benefits in balance, gross motor function, upper-limb activity, pain, and selected fitness outcomes, particularly in children with cerebral palsy. Evidence for telerehabilitation and robotic or wearable approaches was more limited but generally promising. Implementation, equity, cost, and long-term outcomes were rarely reported. No eligible trial directly evaluated electronic patient-reported outcome measures, digital triage, or clinical decision support as stand-alone interventions. Conclusions: Digital interventions have the potential to strengthen pediatric physiotherapy, particularly for short-term motor and functional outcomes. The proposed DPPF provides an implementation-informed structure to guide future research, pathway design, and more purposeful integration of digital health into pediatric rehabilitation practice. Full article

Multi-omnidirectional mobile robot formations offer significant advantages for applications in unstructured environments. However, under constraints such as limited field of view and high operator cognitive load, existing teleoperation frameworks struggle to guarantee formation safety and stability. In this study, a bilateral shared control framework for multi-robot formation that integrates intent perception and vortex-field modulation is proposed. First, an Intent-Mediated Asymmetric Vortex Modulation (IM-AVM) strategy is developed, where the operator’s micro-intentions are mapped to determine the topological orientation of a vortex field. By constructing a dynamic asymmetric modulation matrix, saddle points in the potential field are geometrically eliminated, enabling deadlock-free obstacle avoidance while maintaining a rigid formation. Second, a multi-dimensional perception-based dynamic authority arbitration and topological deadlock escape mechanism is constructed, facilitating a seamless transition from assisted deadlock to autonomous escape. Finally, a formation coordination system based on anisotropic flow field modulation and adaptive sliding mode control is designed. Rigid formation constraints are transformed into a tangential safe flow field, and robust tracking is subsequently achieved through an Adaptive Nonsingular Fast Terminal Sliding Mode Controller (ANFTSMC). Theoretical analysis and experimental results demonstrate that the proposed framework achieves collision-free navigation for the formation in simulated environments. Full article

Assistive bathing for the elderly and disabled presents significant challenges regarding caregiver workload and safety. This paper presents the design and verification of a multi-module integrated intelligent bathing assistance system. The system automates the entire bathing sequence through four coordinated modules: a robotic scrubbing unit, a climate-controlled cabin, a passive multifunctional wheelchair, and a multi-degree-of-freedom transfer device. A key innovation is the wheelchair’s passive design with an automated docking mechanism, ensuring safety in wet environments. Unlike existing commercial solutions and the existing literature, which primarily focus on fragmented, singular functionalities (such as transfer-only devices or fixed-spray cabins), the core advantage of the developed system lies in its holistic integration of safe physical transfer, adaptive robotic scrubbing, and microenvironment control into a seamless, unified architecture. Employing a modular and ergonomic approach, the system executes a predefined 12-step automated workflow. Experimental validation demonstrates an average bathing time of 16.6 min and a quantifiable 69.8% reduction in caregiver workload, confirming the system’s high efficiency and practical utility in alleviating caregiver burden. Full article

Introduction: Post-prostatectomy incontinence (PPI) remains a major functional concern after robot-assisted radical prostatectomy (RARP). Posterior musculofascial reconstruction (PMFR) has been shown to facilitate early urinary continence (EUC), but variations in technique may further improve outcomes. We evaluated whether a modified vesicourethral anastomosis (VUA) incorporating simultaneous PMFR with a single barbed suture [pontine VUA (P-VUA)] may facilitate continence recovery compared with the standard Van Velthoven anastomosis (ST-VUA). Materials and Methods: This prospective study included patients undergoing RARP between January 2021 and December 2023. Allocation was based on surgeon preference. UC was defined as the use of no pads or one dry safety pad per day and was assessed at 10, 30, 90, 180, and 365 days after catheter removal. Multivariable logistic regression was performed to evaluate factors associated with 30-day continence. Time to continence was additionally analyzed using Kaplan–Meier methods. Results: This prospective comparative study included 157 patients undergoing robot-assisted radical prostatectomy (RARP) between January 2021 and December 2023 (76 ST-VUA, 81 P-VUA). Baseline and pathological characteristics were comparable between groups. Catheterization time was significantly shorter in the P-VUA group (5.0 ± 1.1 vs. 6.7 ± 1.4 days, p < 0.001). Continence rates were higher in the P-VUA group at 10 days (72.8% vs. 55.3%, p = 0.03), 30 days (84.0% vs. 68.4%, p = 0.035), 90 days (92.6% vs. 76.3%, p = 0.007), 180 days (93.8% vs. 82.9%, p = 0.044), and 365 days (97.5% vs. 86.8%, p = 0.015). Kaplan–Meier analysis demonstrated a shorter time to continence in Group P (log-rank p = 0.0037). In multivariable analysis, P-VUA was independently associated with higher odds of 30-day continence (OR 6.38, 95% CI 2.08–19.63, p = 0.001). Conclusions: The study suggests that the P-VUA technique was associated with faster recovery of urinary continence compared with ST-VUA in this prospective, non-randomized cohort. These findings support the hypothesis that integrating anatomical reconstruction principles into the anastomotic step may enhance functional outcomes after RARP. However, the results should be interpreted with caution, given the study design and sample size, and require confirmation in larger, preferably randomized studies. Full article

Labor shortages and the need for increased productivity have accelerated the development of robotic harvesting systems for greenhouse crops; however, reliable operation under fruit occlusion and clustered arrangements remains a major challenge, particularly due to the limited integration between perception and motion planning modules. The paper presents the design and experimental validation of an autonomous robotic system for greenhouse tomato harvesting. The proposed platform integrates a rail-guided mobile base, a six-degrees-of-freedom robotic manipulator, and an adaptive end effector with a hybrid vision framework that combines convolutional neural networks and watershed-based segmentation to enable robust fruit detection and localization under occluded conditions. The proposed approach enables improved separation of overlapping fruits and provides accurate spatial localization through stereo vision combined with IMU-assisted camera-to-robot coordinate transformation. An occlusion-aware trajectory planning strategy was developed to generate collision-free manipulation paths in the presence of leaves and stems, enhancing harvesting safety and reliability. The system was trained and evaluated using a dataset of real greenhouse images supplemented with synthetic data augmentation. Experimental trials conducted under practical greenhouse conditions demonstrated a fruit detection precision of 96.9%, recall of 93.5%, and mean Intersection-over-Union of 79.2%. The robotic platform achieved an overall harvesting success rate of 78.5%, reaching 85% for unobstructed fruits, with an average cycle time of 15 s per fruit in direct harvesting scenarios. The rail-guided mobility significantly improved positioning stability and repeatability during manipulation compared with fully mobile platforms. The results confirm that integrating hybrid perception with occlusion-aware motion planning can substantially improve the functionality of robotic harvesting systems in protected cultivation environments. The proposed solution contributes to the advancement of automation technologies for greenhouse vegetable production and supports the transition toward more sustainable and labor-efficient agricultural practices. Full article

Redundant robot manipulators are widely used in constrained operations and tasks in complex environments. However, when multiple task constraints and inequality constraints coexist, motion planning becomes significantly more difficult. In high-dimensional configuration spaces, conventional planners are prone to local minima and may generate trajectories that are difficult to execute in real time. To address these issues, this paper proposes a hierarchical, redundancy-driven real-time replanning framework. First, we perform Cartesian sampling on the task-constraint manifold to reduce the search dimension and generate multiple candidate joint configurations for each Cartesian sample via a redundancy mapping. During connection, manipulability and executability margin are used as evaluation metrics, so that redundant degrees of freedom are explicitly exploited in tree expansion and configuration selection. Second, at the local execution layer, we employ a null-space manipulability optimization strategy to continuously improve dexterity while keeping the primary task unchanged and combine it with a priority-based hard inequality constraint filtering mechanism to project the nominal motion onto the feasible set under joint limits, velocity bounds, and safety-distance constraints in real time. Unlike existing approaches that treat global planning and local control as loosely coupled modules, the proposed framework unifies redundancy reconfiguration, feasibility maintenance, and topological replanning within a single closed-loop structure, thereby reinterpreting local minima as event-triggered topology-switching conditions. To handle the mismatch between dynamic environments and real-time perception, we further introduce a feasibility-margin monitoring mechanism that triggers event-based replanning based on changes in manipulability, constraint scaling, and safety distance, enabling fast topology-level switching and escape from local minima. Simulation and experimental results show that the proposed method effectively restores manipulability through redundancy-driven configuration adjustment and achieves a higher success rate of local recovery under dynamic obstacle intrusion. In forced replanning scenarios, the framework further demonstrates faster environmental response and lower replanning overhead while maintaining better task-constraint stability compared with existing approaches. Full article

Parallel robots are widely applied in many fields because of their unique advantages. To ensure their operational safety and reduce maintenance costs, designing an accurate and reliable fault diagnosis method is essential. Focusing on the 2RRU-RRS parallel robot, this paper proposes an intelligent fault diagnosis method based on a multi-scale convolutional residual network integrated with an Efficient Channel Attention mechanism (MS-ECA-ResNet). Firstly, to fully retain the time-frequency features of the signals, the one-dimensional vibration signals are converted into two-dimensional images using the Continuous Wavelet Transform (CWT). Secondly, a multi-scale convolutional feature extraction structure is designed to enhance the model’s feature extraction ability at different time scales. Furthermore, the ECA mechanism is introduced into the residual network to reinforce important feature channels and suppress noise interference. Comparative experiments, noise environment experiments, and ablation experiments were conducted on a 2RRU-RRS parallel robot experimental platform with a vibration signal dataset. The results demonstrate that the proposed method achieves superior diagnostic accuracy and robustness compared to typical deep learning models, particularly in maintaining high performance under simulated noise conditions. This provides a preliminary validation of the method’s effectiveness in capturing fault-related impacts, offering a potential technical reference for the health monitoring of parallel robots in real-world scenarios. Full article

Artificial intelligence (AI) is increasingly transforming spine surgery, with expanding applications in diagnostics, intraoperative imaging, and surgical navigation. As the field advances toward greater precision and safety, machine learning (ML) and deep learning technologies are being integrated to augment surgeon expertise and optimize operative workflows. In particular, AI-driven innovations in image acquisition and navigation are reshaping intraoperative decision-making and technical execution. This narrative review provides an overview of AI applications relevant to intraoperative imaging and navigation in spine surgery. We begin by defining key concepts in AI, ML, and deep learning and briefly outline the historical evolution of AI within spine practice. We then examine current capabilities in image recognition and automated pathology detection, emphasizing their clinical relevance. Given the central role of imaging accuracy in modern navigation-assisted procedures, we review conventional acquisition platforms, including intraoperative computed tomography (CT) systems (e.g., O-arm, GE, Airo), surface-based registration to preoperative CT (Stryker, Medtronic), and optical surface mapping technologies (e.g., 7D Surgical). Emerging AI-optimized advancements are subsequently discussed, including low-dose intraoperative CT protocols, expanded scan windows, metal artifact reduction algorithms, integration of 2D fluoroscopy with preoperative CT datasets, and 3D reconstruction derived from 2D imaging. These developments aim to improve image quality, reduce radiation exposure, and enhance navigational accuracy. By synthesizing current evidence and technological progress, this review highlights how AI-enhanced imaging systems are redefining intraoperative spine surgery and shaping the future of precision-based care. The primary purpose of this review is to outline the applications of AI and its potential for perioperative and intraoperative optimization, including radiation exposure reduction, workflow streamlining, preoperative planning, robot-assisted surgery, and navigation. The secondary purpose is to define AI, machine learning, and deep learning within the medical context, describe image and pathology recognition, and provide a historical overview of AI in orthopedic spine surgery. Full article

Artificial intelligence (AI) is rapidly reshaping head and neck surgical oncology by augmenting decision-making across the full perioperative continuum. This state-of-the-art review aims to provide head and neck surgical oncologists with a conceptual framework for understanding and critically appraising AI tools entering clinical practice, summarizing how machine learning, deep learning, and generative AI are being integrated into contemporary surgical workflows. Preoperative applications include detection of occult nodal metastasis and extranodal extension. Intraoperative innovations include augmented reality-assisted navigation, real-time margin assessment, and improving visual clarity and tissue handling for robotic platforms. Postoperatively, AI can predict complications like free flap failure and oncologic outcomes. Large language models are being operationalized for clinician-facing applications such as documentation and inbox support, as well as patient-facing education. Despite promising results, broad clinical deployment remains limited by concerns about privacy, validation, reliability, safety, and ethics. Widespread adoption will require prospective clinical trials, robust governance, and human-centered workflows that ensure AI remains a safe, assistive copilot. Full article