Search Results (262)

This study introduces a Generative AI-Enhanced Robotic Data Automation (AI-ERDA) framework designed to improve accuracy, efficiency, and adaptability in healthcare data workflows. Conducted over a two-month, real-world experiment across three government health platforms—one web-based (NHSO) and two PC-based systems (CHi and TRT)—the study compared the performance of AI-ERDA against a conventional RDA system in terms of usability, automation accuracy, and resilience to user interface (UI) changes. Results demonstrated notable improvements in both usability and reliability. The AI-ERDA achieved a mean System Usability Scale (SUS) score of 80, compared with 68 for the traditional RDA, while Field Exact Match Accuracy increased by 1.8 percent in the web system and by 0.2 to 0.3 percent in the PC systems. During actual UI modifications, the AI-ERDA maintained near-perfect accuracy, with rapid self-correction within one day, whereas the baseline RDA required several days of manual reconfiguration and assistance from the development team to resolve issues. These findings indicate that generative and adaptive automation can effectively reduce manual workload, minimize downtime, and maintain high data integrity across heterogeneous systems. By integrating adaptive learning, semantic validation, and human-in-the-loop oversight, the AI-ERDA framework advances sustainable digital transformation and reinforces transparency, trust, and accountability in healthcare data management. Full article

Objectives: This study aimed to compare survival and outcomes between robotic-assisted and conventional sternotomy myxoma resection. Methods: This retrospective single-center study included 16 consecutive patients undergoing left atrial myxoma resection between April 2019 and June 2024. All procedures were performed by the same surgical team. The robotic approach involved peripheral cardiopulmonary bypass (CPB), Custodiol^® cardioplegia, and DaVinci Xi^® via right mini-thoracotomy. The primary endpoint was 30-day cerebrovascular accident-free survival. Secondary outcomes included 5-year survival, stroke, pacemaker implantation, bleeding, Intensive care unit, and hospital stay. Results: Sixteen patients were included (8 robotic, 8 sternotomy); median age was 58.0 [IQR 53.2–67.8] in the robotic group and 66.6 [62.0–71.0] years in the sternotomy group, with a similar sex distribution between groups. No significant baseline differences between groups except a lower EuroSCORE II in the robotic group (0.8% vs. 1.3%, p = 0.004). Robotic surgery resulted in significantly longer CPB time (181 vs. 46 min, p < 0.001) and cross-clamp time (67 vs. 31 min, p < 0.001), but similar intensive care unit stay (2.5 vs. 2.6 days, p = 0.95) and hospital stay (8.5 vs. 8.4 days, p = 0.87). At 30 days, stroke-free survival was 100% in both groups (p > 0.9). At 5 years, survival remained 100% in the robotic group versus 86% in the sternotomy group (p = 0.47). No conversions, reinterventions, or major postoperative complications were observed. Conclusions: Robotic-assisted resection of left atrial myxomas appears to be feasible and safe in a selected low-risk cohort, when compared with conventional sternotomy, with excellent mid-term survival despite longer operative times. Full article

Background and Objectives: Effective perioperative pain management remains a key goal of enhanced recovery protocols, especially in minimally invasive urologic surgery, where optimizing comfort while limiting opioid exposure is essential. Intravenous lidocaine has gained attention for its multimodal analgesic and anti-inflammatory properties, yet evidence in robotic prostatectomy remains limited. This study evaluated whether intraoperative lidocaine infusion was associated with lower early postoperative pain scores and reduced opioid use in patients undergoing robotic-assisted radical prostatectomy. Materials and Methods: A retrospective, single-center analysis was conducted at Pelican Clinical Hospital, Oradea, Romania, including 112 patients operated on between January 2020 and December 2023. All procedures were performed by the same surgical and anesthetic teams using standardized ERAS-based protocols. Patients were divided into two groups: the Lidocaine Group (LG, n = 51), who received a bolus of 1.5 mg/kg lidocaine followed by an infusion of 1.5 mg/kg/h during surgery, and the Control Group (CG, n = 61), who received standard anesthesia without lidocaine. Postoperative pain was measured using the visual analog scale (VAS) at 0, 4, 12, and 24 h, and opioid use was converted into morphine milligram equivalents (MME). Secondary outcomes included time to ambulation, gastrointestinal recovery, oral intake, hospital stay, and complications. Results: Pain intensity was significantly lower in the lidocaine group at 4 h postoperatively (VAS 3.5 ± 1.1 vs. 4.3 ± 1.3; p = 0.01), with similar scores later. Total opioid use was reduced by about 18% in the lidocaine group (25.7 ± 9.4 vs. 31.2 ± 10.5 MME; p = 0.03). Recovery parameters and complication rates were comparable between groups, and no lidocaine-related adverse events were recorded. Conclusions: Intraoperative intravenous lidocaine was associated with lower early postoperative pain scores and reduced opioid requirements after robotic-assisted radical prostatectomy without affecting recovery or safety. Its favorable profile and low cost support its inclusion as a practical adjunct in multimodal analgesia within ERAS pathways. Full article

The construction industry is increasingly adopting digital technologies to enhance productivity and efficiency, in alignment with the principles of Construction 4.0 (C4). The progress and advances recorded thus far are largely due to advancements in cyber-physical systems (CPS), computational processing power, deep learning solutions, robotics, and other related technologies. However, a major challenge in this research space is the lack of an integrated solution for both the interior and exterior construction environments, which has led to fragmented data, hindering efficiency. Several researchers have proposed frameworks in recent years that focused on either indoor or outdoor construction environments; this approach has resulted in the creation of siloed information, to the detriment of the C4 ideals and principles. In this study, a comprehensive system architecture for raw data captured using sensors and other inputs to provide useful insight for the construction team and stakeholders was mapped out. This study presents an integrated framework of various technologies for both indoor and outdoor construction environments. The solution provided for localisation algorithms and technologies such as Simultaneous Localisation and Mapping (SLAM), odometry, and inertial measurement unit (IMU) devices. The unified 5-level Cyber-Physical Systems (CPS) architecture was used as the primary architecture, and it was compared with the IoT Architecture layers in terms of data analytics and management perspectives. The Digital Twin (DT), which sits at the cyber level of the architecture, warehouses and tracks in real-time the dynamic complexities of the construction site throughout the project life cycle, serving as the single source of truth for the project. This system architecture and framework presented in this research contributed towards advancing the field of construction automation by offering a scalable solution for efficient construction in project management. Full article

Coordination algorithms are required to minimise congestion when every robot in a robotic swarm has a common target area to visit. Some of these algorithms use artificial potential fields to enable path planning to become distributed and local. An efficiency measure for comparing them is the time to complete a task in relation to the number of individuals in the swarm. To compare distinct solutions as the swarm grows, experiments with different numbers of robots must be simulated to form a plot of the function of the task completion time versus the number of robots or other parameters. Nevertheless, plotting it for many robots through simulation is time-consuming. Additionally, the inference of a global swarm behaviour as the task completion time from the local individual robot motion controller based on potential fields and other dynamical variables is intractable and requires experimental analysis. Based on that, equations are presented and compared with simulation data for estimating the expected task completion time of state-of-the-art algorithms, robots using only attractive and repulsive force fields and mixed teams for the common target area problem in robotic swarms with not only the number of robots as input but also environment- and algorithm-related global variables, such as the size of the common target area and the working area, average speed and average distance between the robots. This paper is a fundamental first step to start a discussion on how better approximations can be achieved and which mathematical theories about local-to-global analysis are better suited to this problem. Full article

In recent years, humanoid robots have made substantial advances in motion control and multimodal interaction. However, full-size humanoid robots face significant technical challenges due to their inherent geometric and physical properties, leading to large inertia of humanoid robots and substantial driving forces. These characteristics result in issues such as limited biomimetic capabilities, low control efficiency, and complex system integration, thereby restricting practical applications of full-size humanoid robots in real-world settings. To address these limitations, this paper incorporates a biomimetic design approach that draws inspiration from biological structures and movement mechanisms to enhance the robot’s human-like movements and overall efficiency. The platform introduced in this paper, Loong, is designed to overcome these challenges, offering a practically viable solution for full-size humanoid robots. The research team has innovatively used highly biomimetic joint designs to enhance Loong’s capacity for human-like movements and developed a multi-level control architecture along with a multi-master high-speed real-time communication mechanism that significantly improves its control efficiency. In addition, Loong incorporates a modular system integration strategy, which offers substantial advantages in mass production and maintenance, which improves its adaptability and practical utility for diverse operational environments. The biomimetic approach not only enhances Loong’s functionality but also enables it to perform better in complex and dynamic environments. To validate Loong’s design performance, extensive experimental tests were performed, which demonstrated the robot’s ability to traverse complex terrains such as 13 cm steps and 20° slopes and its competence in object manipulation and transportation. These innovations provide a new design paradigm for the development of full-size humanoid robots while laying a more compatible foundation for the development of hardware platforms for medium- and small-sized humanoid robots. This work makes a significant contribution to the practical deployment of humanoid robots. Full article

To address the key challenges in shortest path planning for known static obstacle maps—such as the tendency to converge to local optima in U-shaped/narrow obstacle regions, unbalanced computational efficiency, and suboptimal path quality—this paper presents an enhanced Artificial Lemming Algorithm (DMSALAs). The algorithm integrates a dynamic adaptive mechanism, a hybrid Nelder–Mead method, and a localized perturbation strategy to improve the search performance of ALAs. To validate DMSALAs efficacy, we conducted ablation studies and performance comparisons on the IEEE CEC 2017 and CEC 2022 benchmark suites. Furthermore, we evaluated the algorithm in mobile robot path planning scenarios, including simulated grid maps (10 × 10, 20 × 20, 30 × 30, 40 × 40) and a real-world experimental environment built by our team. These experiments confirm that DMSALAs effectively balance optimization accuracy and practical applicability in path planning problems. Full article

The scalability of coordination policies is a critical challenge in swarm robotics, where agent numbers may vary substantially between deployment scenarios. Reinforcement learning (RL) offers a promising avenue for learning decentralized policies from local interactions, yet a fundamental question remains: can policies trained on one swarm size transfer to different population scales without retraining? This zero-shot transfer problem is particularly challenging because the traditional RL approaches learn fixed-dimensional representations tied to specific agent counts, making them brittle to population changes at deployment time. While existing work addresses scalability through population-aware training (e.g., mean-field methods) or multi-size curricula (e.g., population transfer learning), these approaches either impose restrictive assumptions or require explicit exposure to varied team sizes during training. Graph Neural Networks (GNNs) offer a fundamentally different path. Their permutation invariance and ability to process variable-sized graphs suggest potential for zero-shot generalization across swarm sizes, where policies trained on a single population scale could deploy directly to larger or smaller teams. However, this capability remains largely unexplored in the context of swarm coordination. For this reason, we empirically investigate this question by combining GNNs with deep Q-learning in cooperative swarms. We focused on well-established 2D navigation tasks that are commonly used in the swarm robotics literature to study coordination and scalability, providing a controlled yet meaningful setting for our analysis. To address this, we introduce Deep Graph Q-Learning (DGQL), which embeds agent-neighbor graphs into Q-learning and trains on fixed-size swarms. Across two benchmarks (goal reaching and obstacle avoidance), we deploy up to three times larger teams. The DGQL preserves a functional coordination without retraining, but efficiency degrades with size. The ultimate goal distance grows monotonically (15–29 agents) and worsens beyond roughly twice the training size ($\approx 20$ agents), with task-dependent trade-offs. Our results quantify scalability limits of GNN-enhanced DQL and suggest architectural and training strategies to better sustain performance across scales. Full article

Coordinating robotic teams across multiple concurrent search tasks is a critical challenge in search and rescue operations. This work presents a new multi-agent framework designed to manage and optimize search efforts when several missing-person reports occur in parallel. The method extends iso-probability curve-based trajectory planning to the multi-target case and introduces a dynamic task allocation scheme that distributes search agents (e.g., UAVs) across tasks according to evolving probabilities of success. Overlapping search regions are explicitly resolved to eliminate duplicate coverage and to ensure balanced effort among tasks. The framework also extends the behavior-based motion prediction model for missing persons and the non-parametric estimator for iso-probability curves to capture more realistic search conditions. Extensive simulated experiments, with multiple concurrent tasks, demonstrate that the proposed method tangibly improves mean detection times compared with equal-allocation and individual static assignment strategies. Full article

This article proposes a multi-agent path planning and decision-making solution for high-tempo field robotic operations, such as search-and-rescue, in large-scale unstructured environments. As a representative example, the subterranean environments can span many kilometers and are loaded with challenges such as limited to no communication, hazardous terrain, blocked passages due to collapses, and vertical structures. The time-sensitive nature of these operations inherently requires solutions that are reliably deployable in practice. Moreover, a human-supervised multi-robot team is required to ensure that mobility and cognitive capabilities of various agents are leveraged for efficiency of the mission. Therefore, this article attempts to propose a solution that is suited for both air and ground vehicles and is adapted well for information sharing between different agents. This article first details a sampling-based autonomous exploration solution that brings significant improvements with respect to the current state of the art. These improvements include relying on an occupancy grid-based sample-and-project solution to terrain assessment and formulating the solution-search problem as a constraint-satisfaction problem to further enhance the computational efficiency of the planner. In addition, the demonstration of the exploration planner by team MARBLE at the DARPA Subterranean Challenge finals is presented. The inevitable interaction of heterogeneous autonomous robots with human operators demands the use of common semantics for reasoning across the robot and human teams making use of different geometric map capabilities suited for their mobility and computational resources. To this end, the path planner is further extended to include semantic mapping and decision-making into the framework. Firstly, the proposed solution generates a semantic map of the exploration environment by labeling position history of a robot in the form of probability distributions of observations. The semantic reasoning solution uses higher-level cues from a semantic map in order to bias exploration behaviors toward a semantic of interest. This objective is achieved by using a particle filter to localize a robot on a given semantic map followed by a Partially Observable Markov Decision Process (POMDP)-based controller to guide the exploration direction of the sampling-based exploration planner. Hence, this article aims to bridge an understanding gap between human and a heterogeneous robotic team not just through a common-sense semantic map transfer among the agents but by also enabling a robot to make use of such information to guide its lower-level reasoning in case such abstract information is transferred to it. Full article

This work presents a validation process of a Helmholtz cage developed by the authors at AGH University of Krakow. This type of test stand can generate a near-uniform, precisely controlled magnetic field inside its workspace. This is a crucial tool for several applications, including calibration of magnetic sensors, testing magnetorquers, and hardware-in-the-loop tests of attitude determination and control systems of small satellites. Although many institutions develop Helmholtz cages, we found the literature on methods of validating the final accuracy and uniformity of the generated magnetic field somewhat lacking. In this research, we showcase an approach to perform 3D scans of the magnetic field inside the cage using a probe actuated by a robotic arm. With that method, we verified that the magnitude and angle nonuniformity of the magnetic field vectors in our cage are below 2 percent and 0.4°, respectively, for a wide range of control inputs. We also perform background magnetic field measurements to identify and quantify sources of magnetic disturbances coming from the outside of our system and propose methods of minimizing their impact. It turns out that careful design and building process of the cage and its power driver might not be sufficient to achieve the optimal performance. In our case, we found that some factors, if unmitigated, can cause an error of a few milligauss. Hopefully, this work will help other teams developing similar devices avoid at least some of the possible pitfalls. Full article

Human–Robot Collaboration (HRC) in construction projects promises enhanced productivity, safety, and quality, yet realizing these benefits requires understanding the multifaceted human and robotic factors that influence team performance. This study develops and validates a multidimensional framework that links key human abilities (operational skill, decision-making ability, and learning ability) and robot capacities (functionality and operability) to HRC team performance, with task complexity considered as contextual influence. A field survey of construction practitioners (n = 548) was analyzed using partial least squares structural equation modeling (PLS-SEM) to test direct effects and human–robot synergies. Results reveal that all five main effects are positive and significant, indicating that both human abilities and robot capacities have significant contribution. Moreover, every hypothesized two-way interaction is supported, evidencing strong interaction effects. Three-way moderation analyses further reveal that task complexity significantly strengthened the interactions of human abilities with robot functionality, whereas its interactions with robot operability were not significant. The study contributes an integrated and theory-driven model of HRC team performance that accounts for human abilities and robot capacities under varying task complexity, and validated constructs that can be used to diagnose and predict performance. The findings offer actionable guidance for project managers by recommending that they prioritize user-friendly robot operability to translate worker expertise into performance across a wide range of tasks, invest in training to strengthen operators’ skills and decision-making, and, for complex tasks, pair highly skilled workers with high-functionality robots to maximize performance gains. Full article

Reliable gesture interfaces are essential for coordinating distributed robot teams in the field. However, models trained in a single domain often perform poorly when confronted with new users, different sensors, or unfamiliar environments. To address this challenge, we propose a memory-efficient replay-based domain incremental learning (DIL) framework, ReDIaL, that adapts to sequential domain shifts while minimizing catastrophic forgetting. Our approach employs a frozen encoder to create a stable latent space and a clustering-based exemplar replay strategy to retain compact, representative samples from prior domains under strict memory constraints. We evaluate the framework on a multi-domain air-marshalling gesture recognition task, where an in-house dataset serves as the initial training domain and the NATOPS dataset provides 20 cross-user domains for sequential adaptation. During each adaptation step, training data from the current NATOPS subject is interleaved with stored exemplars to retain prior knowledge while accommodating new knowledge variability. Across 21 sequential domains, our approach attains $97.34\%$ accuracy on the domain incremental setting, exceeding pooled fine-tuning ($91.87\%$), incremental fine-tuning ($80.92\%$), and Experience Replay ($94.20\%$) by $+5.47$, $+16.42$, and $+3.14$ percentage points, respectively. Performance also approaches the joint-training upper bound ($98.18\%$), which represents the ideal case where data from all domains are available simultaneously. These results demonstrate that memory-efficient latent exemplar replay provides both strong adaptation and robust retention, enabling practical and trustworthy gesture-based human–robot interaction in dynamic real-world deployments. Full article

Introduction: In minimally invasive, off-clamp partial nephrectomy (ocMIPN), the reconstructive strategy profoundly influences functional outcomes. Traditional nephrometry scores aid preoperative planning but do not directly inform the choice of closure technique. This dual-institutional study aimed primarily to identify preoperative CT-derived parameters predictive of renorrhaphy versus a sutureless approach, and secondarily to compare perioperative and functional outcomes between these techniques. Methods: We retrospectively analyzed 201 consecutive ocMIPN cases performed using a standardized off-clamp technique by two experienced surgical teams across robotic platforms and conventional laparoscopy. Preoperative CT scans were centrally reviewed to quantify morphometric features, including contact surface area (CSA), tumor radius, and Gerota’s fascia thickness. Univariable and multivariable logistic regression models—one restricted to radiologic variables and one expanded with RENAL score terms—were generated to identify independent predictors. Perioperative outcomes, renal functional metrics, and Trifecta rates were compared between cohorts. Results: Among the 201 patients, 101 (50.2%) underwent sutureless reconstruction and 100 (49.8%) renorrhaphy. Cohorts were comparable at baseline except for tumor size (3.1 vs. 3.6 cm; p = 0.04). In multivariable analysis, CSA > 15 cm² (OR 3.93; 95% CI 1.26–12.26; p = 0.02) and tumor radius (OR 1.14 per mm; 95% CI 1.01–1.29; p = 0.04) consistently predicted renorrhaphy, while Gerota’s fascia < 10 mm emerged as significant only in the expanded specification (OR 0.08; 95% CI 0.01–0.70; p = 0.02). Integration with RENAL improved predictive performance (ΔAUC 0.06; NRI 0.14; IDI 0.07), and the final model demonstrated strong discrimination (AUC 0.81) with satisfactory calibration. Perioperative outcomes, postoperative renal function, and Trifecta achievement were similar between groups (all p ≥ 0.21). Conclusions: A concise set of CT-derived morphologic markers—CSA, tumor radius, and perinephric fascia thickness—anticipated reconstructive strategy in ocMIPN and augmented the discriminatory power of RENAL nephrometry. When anatomy was favorable, sutureless repair was not associated with statistically significant differences in perioperative safety or renal function, although the study was not powered for formal equivalence testing. These findings support the integration of radiologic markers into preoperative planning frameworks for nephron-sparing surgery. Full article

Smart cities are an emerging technology that is receiving new ethical attention due to recent advancements in artificial intelligence. This paper provides an overview of smart city ethics while simultaneously performing novel theorization about the definition of smart cities and the complicated relationship between (smart) cities, ethics, and politics. We respond to these ethical issues by providing an innovative representation of the agent-deed-consequence (ADC) model in symbolic terms through deontic logic. The ADC model operationalizes human moral intuitions underpinning virtue ethics, deontology, and utilitarianism. With the ADC model made symbolically representable, human moral intuitions can be built into the algorithms that govern autonomous vehicles, social robots in healthcare settings, and smart city projects. Once the paper has introduced the ADC model and its symbolic representation through deontic logic, it demonstrates the ADC model’s promise for algorithmic ethical decision-making in four dimensions of smart city ethics, using examples relating to public safety and waste management. We particularly emphasize ADC-enhanced ethical decision-making in (economic and social) sustainability by advancing an understanding of smart cities and human-AI teams (HAIT) as group agents. The ADC model has significant merit in algorithmic ethical decision-making, especially through its elucidation in deontic logic. Algorithmic ethical decision-making, if structured by the ADC model, successfully addresses a significant portion of the perennial questions in smart city ethics, and smart cities built with the ADC model may in fact be a significant step toward resolving important social dilemmas of our time. Full article