Search Results (489)

Path planning is critical for multi-robot systems (MRS), directly affecting the operation efficiency, execution time, and operational cost. Despite extensive research and successful applications of multiple algorithms, achieving globally optimal solutions in cluttered or dynamic environments remains a significant challenge. Issues such as scalability with an increasing number of robots, computational efficiency, system robustness, and coordination complexity continue to drive the development of more reliable approaches. This study reviews modelling approaches, optimisation criteria, and solution algorithms based on the roadmap planning methods that are widely used for multi-robot path planning (MRPP). It focuses on three graph-based algorithms: MRPP algorithm, central algorithm (CA), and the optimisation central algorithm (OCA). These algorithms utilise visibility graphs (VG) for environment representation and Dijkstra’s algorithm for shortest path computation, while incorporating algebraic connectivity to improve coordination, safety, and scalability. In addition, the technological context and implementation platforms, including simulation environments, cloud robotics, and AI-based frameworks, are conceptually examined. The potential applications of these methods in assistive robotics are highlighted, particularly in supporting a safe and reliable navigation in healthcare and human-centred environments. The article synthesises theoretical and practical insights, identifies current limitations and challenges, and outlines future research directions for efficient, scalable, and robust MRPP. Full article

Despite recent advances in healthcare robotics, most existing systems remain limited to single-purpose functions and lack the flexibility to collaborate dynamically with clinicians and facility systems. To address these limitations, this study presents an LLM-orchestrated framework for a multifunctional Robotic Health Attendant (RHA) that enables robot actions and environment interactions to be coordinated in healthcare environments. Within this framework, the RHA functions as a multifunctional nursing assistant capable of performing physical, communicative, and informational tasks through natural-language interaction. Tasks are expressed in natural language and decomposed into coordinated behaviors across three functional branches: physical, for navigation, object manipulation, and delivering medication; communicational, for dialog with patients and clinicians; and informational, for retrieving and summarizing clinical knowledge, such as patient education on complex heart transplant procedures. The framework integrates multiple Large Language Models (LLMs) and sensing nodes to combine facility data, patient information, and clinician commands, enabling robots and building systems to act in a context-aware manner through coordinated task execution across robotic and environmental components. Implemented in a simulated environment, the framework demonstrates the feasibility of executing representative tasks through LLM-based orchestration, serving as a proof-of-concept toward integrated robotic assistance in healthcare settings. Full article

The advent of robotic surgery has revolutionized multiple medical fields, notably in urology, gynecology, and both general and cardiovascular surgery. This article aims to explore the journey of robotic-assisted surgery (multi/single-port) in abdomen and pelvic surgeries, tracing its historical roots, examining its current landscape, and considering the potential future impact. A comprehensive review of the literature was conducted through PubMed/MEDLINE, utilizing keywords such as “robotic surgical systems,” “robotic surgery devices,” and “robotics AND urology.” Reference lists from selected articles were also explored to ensure a broad scope of understanding. The focus was on robotic systems designed for laparoscopic urological surgeries, all of which have been granted regulatory approval for clinical use. The historical trajectory of robotic surgery is traced back to the late 1980s with early systems like the Probot^®, preceding the transformative introduction of the daVinci^® system in the early 2000s. In addition to daVinci^®, the article introduces newer robotic platforms, including Senhance^®, Revo-I^®, Versius^®, Avatera^®, Hinotori^®, Edge^®, Shurui and HugoTM RAS, which are emerging as serious competitors. While daVinci^® has been the dominant force in robotic surgery for over a decade, these new systems are making significant strides with innovative designs, enhanced precision, and improved cost-efficiency. The growing competition among these platforms promises to expand their potential applications, increase accessibility, and optimize surgical outcomes across various specialties. Furthermore, as new technologies continue to evolve, there is a clear need for more extensive clinical trials and real-world data to assess their long-term impact on surgical practices, healthcare delivery, and patient outcomes. It remains to be seen how these advanced systems will integrate into healthcare infrastructures and their ultimate role in shaping the future of minimally invasive surgery. Full article

Textile-based flexible pressure sensors have attracted considerable attention in wearable sensing applications due to their good comfort and mechanical compatibility. However, their sensitivity usually exhibits a nonlinear dependence on pressure, while a compact analytical framework with interpretable physical parameters is still lacking. In this work, a simplified physical model based on lumped effective parameters was established based on the evolution of fiber–conductive particle contacts, and an expression describing the sensitivity–pressure relationship was derived. The model indicates that the sensitivity is mainly governed by an electrical parameter α and a mechanical parameter ratio $E_b/E_x$, and captures the dominant nonlinear decrease in sensitivity with increasing pressure. To verify the applicability of the model, the effects of conductive particle loading, filler type, surface treatment, sensing-layer area, weave structure, and layer number on the sensor response were systematically investigated. In addition, comparison between model-based calculation and experiment in the low- and medium-pressure range gave RMSE values of 0.0040 and 0.0056, and MRE values of 27.6% and 13.4% for the single-layer and four-layer structures, respectively. These results show that the proposed framework captures the main trends of the sensitivity–pressure behavior and provides a physically interpretable basis for discussing how structural and material factors regulate sensor response. This work offers a useful framework for understanding the structure–property relationship of textile-based piezoresistive pressure sensors and may provide preliminary guidance for the design of customized sensors in wearable healthcare and soft robotics applications. Full article

Biomechanics sits at the interface of engineering and medical sciences, offering essential insight into how tissues, organs, and biological systems respond to mechanical loading. This review brings together recent advances in musculoskeletal and cardiovascular biomechanics, illustrating how experimental techniques, computational modeling, and multiscale analysis are used to characterize load transfer, tissue deformation, fatigue, and injury mechanisms. In musculoskeletal applications, predictive simulations, wearable sensing technologies, and neuromechanical assessment tools support improved injury prevention, rehabilitation planning, and assistive device development. In the cardiovascular domain, patient-specific modeling, fluid–structure interaction analyses, and advanced imaging approaches clarify how hemodynamics, vessel wall mechanics, and device–tissue interactions influence disease progression, implant performance, and therapeutic outcomes. Emerging technologies including artificial intelligence, machine learning, digital twin frameworks, biofabrication, soft robotics, and self-powered sensing are enabling data-driven, real-time, and personalized interventions that connect mechanistic understanding with clinical practice. Despite these advances, challenges remain in accounting for individual variability, integrating multiscale data, and translating computational predictions into clinically validated solutions. By emphasizing interdisciplinary strategies that unite biomechanics, computational analytics, and innovative device engineering, this review outlines a pathway toward predictive, patient-centered healthcare and next-generation therapeutic and rehabilitation solutions. Full article

Robotics integration across manufacturing, healthcare, and hazardous environments demands robust performance evaluation. This study proposes a comprehensive Task–Environment–System–Metric (TESM) framework to link operational tasks and environmental constraints with quantifiable metrics. Based on TESM, a multi-level evaluation system is established, covering kinematic/dynamic performance, perception, human–robot interaction (HRI), reliability, and lifecycle economics. We systematically review key evaluation methodologies, including mechanistic modeling, digital twin simulation, physical testing, and multi-criteria decision-making (MCDM). Furthermore, typical engineering applications—ranging from industrial manipulators and mobile robots to collaborative and field systems are analyzed to demonstrate practical implementation. Despite significant progress, challenges persist regarding unified standards, testing fidelity, and the “black box” nature of data-driven assessments in safety-critical scenarios. This review concludes by identifying future research directions, such as establishing benchmark testing platforms, improving lifecycle assessment schemes, and developing modular evaluation tools. These advancements aim to ensure the scalable and reliable deployment of robotic systems in complex engineering environments. Full article

Background: Robotic-assisted surgery (RAS) has progressively expanded in gynecologic practice. Although its technical advantages are recognized, its economic sustainability and equitable accessibility remain debated. Methods: This clinical update provides a critical narrative review of current evidence on RAS in gynecology, integrating data on clinical outcomes, cost-effectiveness, diffusion patterns, and health equity across different healthcare settings. Results: In both benign and oncologic indications, RAS demonstrates consistent perioperative advantages over open surgery, including reduced blood loss, shorter hospital stay, and lower conversion rates. In routine cases, outcomes are largely comparable to conventional laparoscopy. However, robotic approaches appear particularly beneficial in complex scenarios, such as obesity, advanced malignancy, and technically demanding procedures. Economic evidence is heterogeneous. Short-term hospital-based studies report higher direct costs for RAS, especially in benign surgery. Conversely, cost–utility models in oncologic settings suggest that RAS may achieve acceptable cost-effectiveness when long-term outcomes, quality-adjusted life years, and institutional volume are considered. Accessibility remains strongly influenced by reimbursement policies, procedural volume, infrastructure, and workforce training. In the absence of structured reimbursement frameworks, robotic surgery may contribute to socioeconomic and geographic disparities. Conclusions: RAS represents an important component of modern gynecologic surgery, particularly in high-complexity and high-risk cases in which its technical advantages may translate into meaningful perioperative benefit. Its long-term sustainability depends on appropriate patient selection, institutional volume, reimbursement models, and health system organization. Future research incorporating long-term and societal economic perspectives is required to support balanced and equitable implementation. Full article

Background: The incidence of thyroid cancer has increased globally. In recent years, robotic surgical systems have been applied in thyroid surgery, and the rapid development of fifth-generation (5G) communication technology has laid a solid foundation for the smooth implementation of remote surgery. Objective: The aim was to explore the feasibility and safety of telerobotic radical thyroidectomy using 5G communication technology to treat thyroid cancer. Methods: From August 2024 to October 2024, telerobotic radical thyroidectomy was performed on seven female patients using a 5G wireless network and a dedicated line network (or ordinary wired broadband) spanning 22–2200 km. The patients’ clinical and information transmission data were analyzed. Results: All patients (papillary thyroid carcinoma, female, with an average age of 44.0 ± 4.6 years) underwent uneventful surgical procedures without any transfer to open surgery or complications. The average surgical duration was 91.3 ± 11.8 min, the average blood loss was 11.4 ± 4.8 mL, and the average postoperative hospital stay was 3.6 ± 0.8 days. All subjects were successfully discharged within 5 days after surgery. The average total latency time of the intraoperative network was 137.5 (range, 121–159) ms, and there were no adverse events, such as network disconnection, frame loss, or network attacks. The operator worked smoothly without any obvious delay or lag, and the recorded audio and video are clear. Conclusions: Telerobotic radical thyroidectomy for thyroid cancer over a 5G network demonstrates promising feasibility and safety. With stable network transmission and a clear surgical field, the precise operations required in thyroid surgery can be performed reliably. These findings suggest that this technology can facilitate high-quality surgical care in remote areas, contributing to a more balanced distribution of medical resources. Full article

Multi-personpose estimation is a fundamental technology for deep learning-based intelligent sensing systems, enabling downstream understanding of human action in applications such as surveillance, robotics, healthcare, and sports analytics. Most two-stage multi-person pose estimation algorithms suffer from low efficiency due to their decoupled representation between body and keypoints, resulting in a complex inference process. Single-stage algorithms such as AdaptivePose that represent body parts as semantic proxy points can substantially simplify the pipeline while achieving competitive results; however, the limited receptive field of convolutional features still makes it difficult to localize proxy points for long-range extremities in semantically informative regions. IHPP addresses this issue as a task-specific refinement of AdaptivePose; it enriches the proxy-point perceiver with directional context modeling along the horizontal and vertical axes and redesigns the second-step regression stage with a part-wise branch to reduce inter-part feature interference while keeping the overall pipeline lightweight. Here, we present IHPP in three sizes (IHPP-S, IHPP-M, and IHPP-L), optimizing the tradeoffs between efficiency and accuracy through fine-tuning of the channel dimensions for each bodypart feature. IHPP-L achieves 72.3 AP at 28 fps on COCO test-dev, surpassing DEKR-W48 and SWAHR-W48 by 1.3 AP and 0.3 AP, respectively, while IHPP-M reaches 69.0 AP at 35 fps and IHPP-S runs at 42 fps with only 9.4 M parameters. On CrowdPose, IHPP-M outperforms AdaptivePose-W48 by 0.2 AP with about one-third the parameters. Comprehensive experiments on the MS COCO and CrowdPose datasets validate the effectiveness of this design. Full article

Gel-based flexible electronic sensors have emerged as a transformative class of materials for next-generation applications. These applications are wearable electronics, soft robotics, electronic skin (e-skin), and healthcare monitoring systems. Owing to their intrinsic softness, stretchability, and biocompatibility, gels provide an ideal platform for constructing highly deformable and skin-conformable sensing devices. This paper provides insight into emerging fabrication techniques, including 3D printing, bioprinting, and microfabrication. These techniques have facilitated the creation of complex architectures with improved sensitivity and scalability. The review also focuses on recent advancements that have focused on overcoming traditional limitations. These limitations are poor mechanical strength, dehydration, limited environmental stability, and low sensitivity. In particular, the incorporation of conductive fillers and ionic species has enabled a range of sensing mechanisms. These mechanisms include piezoresistive, capacitive, piezoelectric, and ionotronic responses. Therefore, it allows for the accurate detection of strain, pressure, temperature, and biochemical signals. Finally, this review provides a summary of future research, which is expected to focus on multifunctional integration, sustainable materials, and intelligent data processing. It provides pathways to the widespread adoption of gel-based flexible electronic sensors in both consumer and clinical applications. Full article

Facial expression recognition (FER) for assistive and telepresence robotics remains challenging under resource-constrained conditions because landmark normalization is often unstable, many datasets have limited variability, and full facial landmark sets introduce redundancy. This paper proposes a lightweight, privacy-preserving FER framework for assistive healthcare robotics based on geometric facial landmarks rather than raw RGB images. The objective is to improve recognition robustness and deployment suitability on low-power edge devices through two complementary contributions: a revised nose-centered landmark normalization method and an optimized Facial Feature Mapping, FFM-L03. The proposed normalization replaces the expression-sensitive upper-lip reference with a geometrically stable nose-center anchor, while FFM-L03 combines FACS-guided anatomical priors with ANOVA F-score, LASSO, PCA, and t-SNE/UMAP to retain 60 informative landmarks. In addition, a heterogeneous Freepik dataset was constructed to increase variability in lighting, background, resolution, and subject appearance. Experimental evaluation across 15 landmark groups, four datasets, and four classifiers shows that the proposed method consistently improves performance over prior landmark configurations, achieving gains of up to 22.4 percentage points over the Ciraolo baseline and 22.1 percentage points over the full-landmark baseline in accuracy, precision, recall, and F1-score, while maintaining lightweight operation. These results demonstrate that principled normalization and targeted landmark selection can substantially improve FER for real-time, privacy-aware assistive robotic systems. Full article

In the field of human–robot interaction (HRI), researchers have extensively examined the role of social robot characteristics and how these can influence human–robot relationships. In particular, the robot’s voice is one of the most studied aspects, with numerous studies focusing on specific features such as tone, frequency, pitch, and gender. The robot’s voice represents a powerful social signal, whose design can influence people’s affective evaluations and acceptance of robots. With regard to language, however, relatively few studies have investigated the role of a robot’s accent (native or foreign). This experimental study therefore explores the influence of native accent on trust in robots. The study was conducted on two different samples: 60 Italian participants and 37 Arabic participants. Participants listened to two robot presentations in their native language: one delivered with a native accent and the other with a foreign accent. After listening to both presentations, participants were asked to indicate which robot they trusted. The results showed a 77.3% preference for the robot speaking with a native accent, compared to 22.7% for the robot with foreign accent. These findings demonstrate that, regardless of the language (Italian or Arabic), accent significantly influences the choice to invest trust in the robot, supporting the similarity-attraction effect. Accent calibration thus emerges as a low-cost, high-impact parameter in socially assistive and commercial robotics. Since accent influences trust-based delegation, voice design should be strategically adapted in service, healthcare, education, and customer-facing contexts. Full article

Industry 5.0 is no longer understood merely as an extension of automation; it reflects a broader shift toward integrating technological advancement with human well-being, sustainability, and resilience. However, the literature reveals a fragmented landscape in which technological, industrial, and ecological dimensions are often treated separately, hindering a cohesive understanding of the paradigm. To address this gap, this study conducts a PRISMA-ScR-based review of 52 peer-reviewed studies (January 2021–March 2026), structured around ten research questions that examine technologies, sectors, methods, human-centered design, sustainability alignment, and implementation barriers. The review demonstrates high reliability (Cohen’s κ = 0.981). Findings highlight artificial intelligence (86%), collaborative robotics (80%), IoT (71%), and digital twins (63%) as core technologies, typically integrated within human-in-the-loop systems. Manufacturing and healthcare lead adoption, reporting reduced physical workload and improved safety. Nonetheless, only 63% of studies explicitly align with sustainability frameworks, revealing a persistent gap. Thus, inclusive Industry 5.0 remains a promising yet still insufficiently consolidated concept. Full article

Capsule endoscopy (CE) is evolving from a primarily small-bowel imaging modality into a broader diagnostic platform that increasingly incorporates artificial intelligence (AI), robotic technologies, biosensing capabilities, and decentralized models of care. The REFLECT symposium brought together an international, multidisciplinary audience of clinicians, engineers, scientists, and healthcare stakeholders to critically evaluate the present and future role of CE across a range of gastrointestinal (GI) applications, including inflammatory bowel disease, GI bleeding, coeliac disease, and colorectal cancer screening. Discussions explored the clinical impact of panenteric and colon capsule endoscopy, the potential of AI to enhance diagnostic performance and streamline workflows, innovations in capsule hardware, and the design of patient-centred diagnostic pathways. While conventional endoscopy continues to serve as the benchmark in many clinical scenarios, CE was recognized for its ability to improve access, acceptability, and scalability when deployed in appropriately selected populations. The symposium also identified key barriers to broader implementation, such as reinvestigation rates, absence of standardized quality indicators, limited real-world evidence for AI tools, and ongoing economic and environmental challenges. Overall, the meeting highlighted the importance of gradual, evidence-driven integration of CE, supported by robust validation, standardized metrics, close clinician-engineer collaboration, and meaningful incorporation of patient experience, to support the development of a safe, equitable, and sustainable pathway. Full article

As social robots are increasingly deployed in domains such as healthcare, education, and entertainment, there is growing demand for affective agents that can interpret users’ affective states and respond in contextually appropriate ways. Existing work has established strong foundations for emotion generation and appraisal, but the step that connects generated emotion to behavioral execution still relies heavily on model-specific rules or implicit links. We frame this issue as a Mechanism Gap and propose an Emotional BDI framework that introduces Frijda’s action tendency as an intermediate representation layer between the Affective Core and the Belief–Desire–Intention (BDI) Executor. Rather than mapping emotion directly to concrete behavior, the framework first transforms affective state into a directional action tendency and then lets BDI reasoning realize that tendency according to role and context. This creates an explicit emotion-to-behavior mediation structure through which the same emotion can be expressed differently across situations and roles. In an exploratory user evaluation with 26 participants, the proposed model received more favorable ratings than an Emotion-Driven Agent in satisfaction ($p=0.010$) and appropriateness ($p=0.002$). Compared with a Cooperative Agent, the proposed model showed a significant advantage only in satisfaction ($p=0.030$). These findings suggest that the proposed framework offers a useful architectural direction for affective decision making beyond direct mapping or unconditional compliance. Full article