Search Results (21,459)

Interface interaction mechanics analysis is of great significance for robot-assisted insertion surgery in minimally invasive surgery and therapy. Previous work indicates that the accurate modeling of soft tissue puncture forces plays a crucial role in surgical planning, robotic needle insertion, and biomechanical simulation, which can give insights useful for physicians to guide and operate assisted robots. The objective of this study is to develop a dynamic multi-component force model that integrates cutting force, stiffness resistance, and frictional interaction to characterize needle–soft tissue interaction during puncture. A dynamic force model is proposed, and a lateral periodic disturbance mechanism is introduced into the simulation framework in order to enhance the robustness and realism of the model under micro-manipulation scenarios. The model has been validated using a series of controlled puncture experiments on porcine liver and renal tissues under varying insertion angles (15°, 30°, 45°) and speeds (0.5 mm/s, 1.5 mm/s, 2.5 mm/s). Corresponding finite element simulations were also conducted using ANSYS software. The agreement between simulation and experiment has been quantitatively evaluated by comparing force–depth and force–time curves, and the statistical significance of the impact of angle and speed on puncture forces has been assessed using ANOVA and Tukey’s HSD tests. Quantitative comparison demonstrated strong consistency, with the optimal case reaching a coefficient of determination (R²) value of 0.96 and Root Mean Square Error (RMSE) below 0.13 N after incorporating a 0.05 mm lateral perturbation. Statistical analysis confirmed the impact of angle and speed on puncture force responses (p < 0.05). Furthermore, comparative analysis revealed that porcine liver exhibits more consistent biomechanical behavior than renal tissue, particularly under perturbation-enhanced simulation. This study successfully establishes a dynamic multi-component force model for soft tissue puncture, validated with high fidelity against experimental data. The incorporated lateral disturbance mechanism enhanced the model’s realism. This work can provide a reliable foundation for the future design of intelligent robot-assisted puncture systems and high-fidelity simulation-based training platforms. Full article

In this paper, a nonlinear integral sliding-mode controller (SMC) based on a radial basis function (RBF) neural network is proposed to address the challenges of high nonlinearity, parameter uncertainty, and unmodeled dynamics in the electro-hydraulic servo system of a robotic excavator. The controller design incorporates adaptive RBF neural networks to compensate for system perturbations and uncertain nonlinearities, while an integral sliding surface is employed to eliminate steady-state error. This approach not only compensates for uncertainties but also reduces the traditional SMC’s high dependency on precise system parameters. The mathematical model of the bucket electro-hydraulic servo system is established without linear approximation. Based on this model, the sliding-mode controller with RBF neural networks (SMC-RBF) is designed, and its asymptotic stability is proven using the Lyapunov method. Simulation and experimental results are compared with a traditional PID controller to verify the proposed controller’s superiority. The simulations show that the SMC-RBF controller meets the requirements for tracking performance and demonstrates robustness, improving sinusoidal tracking performance by 46% compared to the PID controller. Experimental results further demonstrate that the SMC-RBF controller improves the trajectory accuracy for a two-meter straight line by 52.46% in comparison to the traditional PID controller. Full article

Practical robotic systems require control methods that remain reliable under limited computational resources, uncertain environments, and frequent changes in operating conditions. Although model-based control forms the foundation of high-performance robotics, real-world deployment is often hindered by model uncertainty, time-varying dynamics, and costly identification. As a result, low-order and intuitive control schemes remain dominant, yet such approaches often fail to sustain consistent performance under disturbances and parameter variations. Robust and adaptive control provide representative paradigms to address this gap, where a Disturbance Observer (DOB) suppresses uncertainty through disturbance rejection and a Parameter Adaptation Algorithm (PAA) improves model fidelity through online identification. However, direct integration of a DOB and a PAA often introduces functional interference, including mutual masking between disturbance compensation and parameter estimation, which compromises closed-loop stability. This paper proposes an Adaptive Disturbance Observer (ADOB) that integrates a DOB with online parameter adaptation. The ADOB updates the nominal model of the DOB in real time using a Recursive Least Squares (RLS)-based PAA, while a dual-filtering structure separates disturbance rejection and parameter identification. Stability is analyzed using hyperstability theory, where a smoothing mechanism enforces the slowly varying parameter assumption. Experiments on a one-Degree-of-Freedom (DOF) electromagnetic actuator and a three-DOF robotic manipulator demonstrate reductions in model uncertainty and tracking error compared with a conventional DOB. Full article

This paper presents the design, construction, and calibration of a modular low-cost 3D printer based on open-source technologies, developed as part of an academic research project. The printer utilises fused filament fabrication (FFF) and is built using locally available materials and components, including a T-slot aluminium frame, NEMA 23 stepper motors, and an Arduino Mega 2560 with RAMPS 1.4 control board. The system integrates Marlin firmware and CURA slicing software, enabling autonomous operation via an LCD panel and encoder interface. A detailed methodology is provided for mechanical assembly, electronic integration, firmware configuration, and calibration procedures. Special attention is given to the challenges encountered during the initial testing phase, including filament feeding issues, thermal inconsistencies, and mechanical misalignments. Solutions such as replacing inadequate components (e.g., fibreglass bushings with PTFE), adjusting spring tension, and refining firmware parameters are discussed. The results demonstrate successful printing of complex geometries after iterative calibration, validating the printer’s performance and replicability. This work contributes to the democratisation of additive manufacturing by offering a replicable, open-source solution for educational and prototyping purposes. The findings are relevant to machine design, automation, and robotics communities seeking practical insights into low-cost fabrication systems. Full article

This paper addresses the position control problem for a Lagrangian pendulum. Using a strict Lyapunov function, a rigorous analysis is presented to prove that the closed-loop system equilibrium point composed of the pendulum dynamics and a classical linear PID control is globally asymptotically stable. Motivated by these results, the theoretical proposal is extended to analyze a novel hyperbolic PID-type control scheme; reformulating the Lyapunov function, global asymptotic stability of the equilibrium point for the corresponding closed-loop equation is demonstrated. The proposed hyperbolic scheme is a rational function with bounded control action composed of a suitable combination of hyperbolic sine and cosine functions. The hyperbolic structure is used in the proportional, integral, and derivative terms of the control algorithm to drive the position error and joint velocity to zero. Experimental results of both a linear PID and a novel hyperbolic PID-type controller on a direct-drive pendulum are presented to illustrate the effectiveness and performance of the proposed control algorithm. Full article

In this paper, we propose a distributed admittance control framework for joint manipulation of objects by multiple robotic arms that addresses the challenges of human–robot interaction. The system is developed to control the joint transportation of an object by N Franka Emika Panda robots (validated with up to four in simulations) using external human force estimation in a distributed manner without relying on centralized computation or force sensors. We integrate a hybrid observer by combining a distributed force estimator with a nonlinear disturbance observer (NDOB) to achieve accurate human force estimation and minimize estimation errors in simulations. Adaptive radial basis function neural networks (RBFNNs) are employed to dynamically adjust the damping and inertia parameters, enhancing the system’s adaptability and stability. Event-based communication minimizes network bandwidth usage, while consensus protocols ensure synchronization of state estimates across robots. Unlike conventional methods, the proposed observer operates in a fully sensorless manner: no human-force measurements are required. The estimation relies solely on locally available robot states, maintaining high accuracy while reducing system complexity. The framework demonstrates scalability to multiple robots, enhancing robustness in distributed settings. Simulation results show superior performance in terms of path tracking, force estimation accuracy, and communication efficiency compared to centralized approaches. Specifically, the event-triggered strategy reduces communication messages by approximately 70% compared to always-connected mode while maintaining comparable RMSE in position $(9.97\times {10}^{-5}$ vs. $7.39\times {10}^{-5}$) and velocity $(2.52\times {10}^{-5}$ vs. $3.76\times {10}^{-5})$, outperforming periodic communication. Full article

Background: Cerebral palsy (CP) is the most common cause of disability in developmental age, affecting motor and postural skills. With growth, lower-limb orthopedic surgery often becomes necessary. Post-surgical walking rehabilitation programs generally involve conventional therapy with only limited evidence on the use of robot-assisted gait training (RAGT). The aim of the present pilot study is to assess the feasibility and the preliminary functional outcomes of an intensive 3-week rehabilitation of 15 sessions with Lokomat combined with 15 sessions of conventional physiotherapy. Methods: In total, 27 patients with diplegic cerebral palsy who underwent orthopedic surgery were recruited. Outcomes collected: the 6 min walking test (primary outcome), the Gross Motor Function Measure-88, the Gillette Functional Assessment Questionnaire, 3D gait analysis, and spasticity and force metrics of the lower limbs. Paired statistical tests were used to assess pre–post changes. Results: A pre–post statistically significant improvement was observed in gait endurance in the 6MWT (Δ = 28.56 ± 34.28 m; p < 0.001) and in gross motor functional skills. Gait parameters showed some functional and structural improvements, and joint stiffness was reduced in some measures. Conclusions: This combined rehabilitative approach seems to be promising in postoperative patients with CP. Future studies, involving a control group and larger sample size, are needed to generalize our results. Full article

The transition of the mining industry towards Industry 5.0 demands predictive models capable of strictly adhering to physical laws and modeling complex, non-Euclidean geometries—capabilities often lacking in standard graph neural networks. This systematic review, conducted under the PRISMA 2020 protocol, analyzes the emergence of “Era 5” architectures by synthesizing 96 high-impact studies from 2019 to 2026, focusing on Clifford (geometric algebra) GNNs, simplicial and cell complex neural networks, symplectic/Hamiltonian GNNs, and generative flow networks (GFlowNets). The analysis demonstrates that Clifford architectures provide superior rotational equivariance for robotic control; Simplicial networks capture high-order topological interactions critical for geomechanics; Symplectic GNNs ensure energy conservation for stable long-term simulation of structural dynamics; and GFlowNets offer a novel paradigm for generative mine planning. We conclude that shifting from data-driven approximations to these mathematically rigorous, structure-preserving architectures is fundamental for developing reliable, physics-informed digital twins that optimize structural integrity and operational efficiency in complex industrial environments. Full article

Self-sustained oscillatory systems enable autonomous motion through continuous interaction with ambient energy sources, positioning them as promising candidates for soft robotic actuation, energy conversion, and biomedical applications. However, their utility is often limited by inherent vibrations and frictional losses, which can lead to impaired efficiency and generate noise. To overcome these limitations, a continuously rotating disc mechanism is proposed, which exploits the photothermal response of liquid crystal elastomers (LCEs) under uniform illumination. The resulting temperature field within the material is obtained via photothermal modeling of the LCE. The rotational actuation torque is generated through mass displacement resulting from light-induced LCE contraction. Based on the above conditions, we establish the equilibrium conditions and critical thresholds for continuous motion and reveal a synergy between the thermal field and torque. Through the interplay of the temperature field and the actuating rotating moment, the system ultimately attains steady self-rotation. Therefore, the absorbed energy offsets damping losses. Numerical simulations reveal that the steady-state self-spinning and translational velocity are influenced by multiple parameters including incident heat flux, gravitational field strength, material contraction coefficient, LCE element dimensions, illumination geometry, and resistive torque. The proposed LCE disc configuration exhibits exceptional operational stability and minimal damping, which has potential for implementation in advanced soft robotic systems and mechanical energy conversion applications. Full article

Background: Embodied artificial intelligence (EAI), integrating advanced AI algorithms with robotic platforms capable of sensing, planning, and acting, has emerged as a transformative approach in healthcare delivery. This systematic review synthesizes evidence on robotic perception, decision-making, and clinical impact of EAI systems in healthcare settings. Methods: Following PRISMA 2020 guidelines, we searched PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library for studies published between January 2020 and August 2025. Seventeen studies met eligibility criteria, spanning four domains: surgical assistance, rehabilitation, hospital logistics, and telepresence. The protocol was prospectively registered in PROSPERO under ID: CRD420261285936. Results: Perception architectures predominantly employed multimodal sensor fusion, combining vision with force/torque, depth, and physiological signals. Decision-making approaches included imitation learning, reinforcement learning, and hybrid symbolic-neural control. Key findings indicate that surgical robots demonstrated consistency advantages in specific experimental tasks, rehabilitation robotics produced statistically significant improvements (SMD = 0.29) across 396 randomized controlled trials, and both logistics and telepresence systems achieved very high operational success levels. Nonetheless, important barriers remain, including limited external validation, small sample sizes, and insufficient cost-effectiveness data. Conclusions: Future research should prioritize standardized benchmarks, prospective multicenter trials, and patient-centered outcome measures to facilitate clinical translation of EAI technologies. Full article

Artificial intelligence (AI) is a scientific and engineering discipline that involves designing systems capable of autonomously replicating the cognitive functions typically associated with human intelligence. Current AI uses data to extract patterns, supports decision-making, and enhances analytical reasoning across diverse domains, including sports performance or strategic claims, and assists in clinical applications. In sports, AI enables robotic systems to assist in training, object tracking, performance monitoring, strategy development, and talent identification. In medicine and rehabilitation, AI facilitates robotic surgery, rehabilitation training, and decision-support systems. Machine learning and deep learning techniques, combined with computer vision, enable estimation of human posture and movement in 2D or 3D from video recordings, providing objective, quantitative, and markerless movement analysis. For instance, human pose estimation systems, including open-source and framework tools, have been applied for multi-athlete and individual tracking, performance assessment, and injury prevention. Additionally, AI-powered systems and generative AI for data simulation enhance strategy planning and training efficiency. This review provides a comprehensive overview of AI applications in human movement assessment, highlighting methodological approaches, practical implementations, and emerging technologies. Understanding the capabilities and limitations of these systems helps optimize human movement assessment and support data-driven decisions. Full article

Internal hospital logistics are inherently complex, characterized by the critical need to move essential materials with high efficiency, precision, and safety. The adoption of automated guided vehicles (AGVs) is essential for automating these flows, but designing and optimizing their routes represents a significant challenge. This study presents a methodology for analyzing and optimizing AGV paths within healthcare facilities, effectively managing three-dimensional spatial complexity. The methodology leverages BIM and the open IFC standard to obtain an accurate geometric and semantic representation of the building. These data are then converted into a graph model using graph theory. Pathfinding algorithms, such as A*, are applied to this graph to calculate and optimize AGV trajectories, considering operational and collision constraints. The approach provides distance-optimized AGV paths. The integration of BIM, IFC, and graph theory proves to be an effective tool for logistical planning, simulation, and proactive management of AGVs in multi-level environments. This research contributes to the digital transformation of the construction sector by demonstrating how the integration of open standards and advanced algorithms can optimize the operational performance of complex buildings. By bridging the gap between architectural modeling and robotic logistics, the proposed approach supports the development of “smart buildings” and promotes more sustainable and technologically advanced management of healthcare facilities. Full article

Emotion recognition is a key capability of social robots operating in real-world human-centered environments, especially when interacting with users with special needs. Such users may express emotions in atypical, subtle, or strongly context-dependent ways. These characteristics pose significant challenges for conventional emotion recognition systems. This paper proposes an adaptive deep learning framework for emotion recognition in social robots. The framework is designed to support inclusive and accessible human–robot interaction. It combines region-based convolutional neural networks with adaptive learning mechanisms. These mechanisms explicitly model individual variability, contextual information, and interaction dynamics. Multiple deep architectures are evaluated to assess robustness across diverse emotional expressions, including those influenced by cognitive, sensory, or developmental differences. Rather than relying on fixed emotion models, the proposed approach emphasizes adaptability. The system dynamically adjusts its perception strategies to user-specific expressive patterns. Experimental validation is conducted using context-aware emotion datasets. Performance is evaluated in terms of detection accuracy, robustness to variability, and generalization across emotion categories. The results show that adaptive mechanisms improve recognition performance in scenarios characterized by non-standard or low-intensity expressions, compared to static baseline models. This study highlights the importance of flexible, context-sensitive perception for inclusive social robotics. It also discusses design implications for deploying emotion-aware robots in assistive, educational, and therapeutic settings. Overall, the proposed framework represents a step toward socially intelligent robots capable of engaging more effectively with users with special needs. Full article

Three-dimensional (3D) bioprinting has rapidly emerged as a transformative technology at the interface of biomedical engineering and regenerative medicine. By enabling the spatially controlled deposition of living cells, biomaterials, and bioactive molecules, it offers an unprecedented potential to fabricate functional tissues and potentially whole organs in the future. This review explores recent advances in bioprinting materials, processes, and applications, emphasizing the integration of bioinks, printing methods, and mechanical design principles that underpin tissue functionality. Natural and synthetic biomaterials such as hydrogels (e.g., collagen, alginate), polyethylene glycol (PEG), and polyesters like PLGA are evaluated in terms of biocompatibility, printability, and degradation behavior. Key bioprinting modalities, including extrusion, inkjet, and laser-assisted bioprinting, are compared based on printing resolution, cell viability, and scalability. Structural considerations such as scaffold architecture, mechanical stability, and biomimetic design are discussed in relation to native tissue mechanics and requirements. The review also surveys emerging applications in tissue engineering (e.g., bone, cartilage, skin replacements), organ-on-a-chip systems for drug testing, and patient-specific implants, while addressing persistent challenges such as standardization of biofabrication, regulatory and ethical considerations, and manufacturing scale-up. Finally, future trends, including the integration of artificial intelligence (AI) and robotic automation, multi-material and four-dimensional (4D) bioprinting, and the maturation of personalized bioprinting strategies, are highlighted as pathways toward more autonomous and clinically relevant bioprinting systems. Collectively, these developments signify a paradigm shift in how biological constructs are designed and manufactured, bridging the gap between laboratory research and clinical translation. Full article

The development of prostheses that accurately reproduce fine motor skills remains a key challenge for daily assistance applications. This research presents the development of a soft robotic hand prosthesis prototype inspired by the natural behavior of muscles and tendons, incorporating internal vacuum-based reinforcement and textured fingertip surfaces to enhance friction and grasp adaptability, without relying on force sensors. The prosthesis reproduces open-hand and tripod pinch movements through myoelectric signals (EMG) acquired via a wearable armband equipped with eight surface electrodes. The signals are processed in real-time and classified by a lightweight dense neural network implemented on a low-power microcontroller. Tendon-driven actuation enables biomimetic motion with smooth and compliant behavior. The proposed system was validated through laboratory-based functional tests using user-specific models, showing response times ranging from 0.49 to 2.00 s and an overall grasping effectiveness of approximately 80% when manipulating small everyday objects with different geometries. These results indicate that the prototype constitutes an accessible and functional solution for fine motor assistance, with potential applicability in low-cost and resource-constrained myoelectric prosthetic systems. Full article