Search Results (269)

Large language models (LLMs) exhibit strong general conversational capabilities, yet their deployment in domain-specific service environments such as hospitality remains limited by the absence of emotionally grounded datasets and validated end-to-end system architectures. This paper presents HOSPIT-LLM, an EU-funded euROBIN Technology Exchange Program pilot, as a complete, integrated pilot pipeline for human-centered conversational AI in hotel reception scenarios. We deploy a multimodal hotel-terminal assistant in a real hotel reception, capturing synchronized dual-camera video and audio to collect authentic guest–staff interactions. Speech is transcribed using Whisper, and emotion is extracted from the corresponding video segments via DeepFace, producing 582 real Greek guest–receptionist exchange examples. The resulting data are classified into eight Standard Operating Procedure (SOP) categories. To address data scarcity, we augment the corpus with 1269 synthetic dialogues generated by eight diverse LLMs through the OpenRouter API, yielding a total of 1851 dialogue records with explicit emotion-token annotation. We fine-tune Qwen3.5-35B-A3B using Low-Rank Adaptation (LoRA) through a two-stage process: supervised fine-tuning (SFT) on an 888-example conversation pool and Simple Preference Optimization (SimPO) on a 1899-pair preference pool, each split 80/10/10 into training, validation, and test. The resulting model is integrated into an interactive hotel-terminal system combining YOLO-based person detection, face-recognition-driven guest personalization, Kokoro neural text-to-speech (TTS), and a multi-service orchestration layer connected to the hotel Property Management System (PMS). Evaluation combines standard text metrics, emotion-aware scoring, and a large-model judge. The results indicate targeted improvements in the rule-based contextual emotion-policy match and staff-emotion policy compliance compared to the base model, while general response-quality gains remain more modest. In particular, the rule-based contextual policy-match score improves from 0.614 to 0.901, while forbidden staff-emotion outputs decrease from 0.142 to 0.018. The deployed pilot demonstrates the practical integration of a personalized, emotion-aware LLM assistant in an interactive hotel-terminal setting; end-to-end latency and fully hotel-side edge deployment were not evaluated and are left for future work. HOSPIT-LLM provides a reproducible framework for multimodal dataset creation, preference-based fine-tuning, and deployment of human-centered AI systems. A mobile robotic embodiment is planned as future work. Full article

Accurate and robust localization remains the fundamental bottleneck for truly autonomous robotic systems, despite decades of progress in probabilistic estimation and SLAM. This paper provides a critical and comprehensive review of mobile robot localization across sensing modalities, estimation paradigms, and deployment domains, covering ground, aerial, and underwater platforms. Beyond a descriptive survey, we explicitly analyze the limitations and trade-offs of existing approaches. We introduce an updated taxonomy that spans classical proprioceptive and exteroceptive sensors, emerging technologies such as 4D imaging radar and event cameras, and infrastructure-based positioning systems including GNSS and Ultra-Wideband. We revisit the evolution of localization algorithms, from Bayesian filtering techniques (EKF, UKF, and particle filters) to modern graph-based SLAM frameworks and tightly coupled multi-sensor fusion systems. Particular emphasis is placed on the recent paradigm shift toward learning-based and neural implicit approaches, including NeRF-SLAM and Gaussian Splatting, highlighting both their transformative potential and their current impracticality for real-time deployment. Unlike previous surveys, this work provides a unified cross-domain perspective while critically examining scalability, robustness, computational cost, and real-world deployability. We identify key unresolved challenges, including long-term consistency, operation in degraded environments, and the integration of semantic understanding into localization pipelines. Furthermore, we propose standardizing evaluation metrics with a formal Trajectory Completeness formulation to expose tracking brittleness. Finally, we outline future research directions toward resilient, certifiable, and truly autonomous localization systems, emphasizing the critical transition from passive estimation to Active SLAM in unstructured environments. Full article

To address the inefficiency of demolition robots at nuclear contamination sites due to frequent retreats to safe zones for attachment replacement, this study develops and experimentally evaluates a six-wheeled mobile platform for attachment-replacement support near the work area. Structurally, the prototype adopts a well-established passive rocker-bogie suspension architecture combined with six-wheel independent drive. The focus of this work is not to claim a new suspension topology, but to evaluate its engineering feasibility and drive-load margins for a heavy-duty nuclear support platform through multibody simulation and onboard-sensor measurements. A constrained multibody model was implemented in ADAMS/Simulink to represent rocker joints, wheel revolute joints, actuator limits, and wheel–ground contact. A full-scale prototype was tested on representative nuclear-facility terrain conditions, including a 20° slope and a 250 mm vertical step. The results show that the prototype completed both tests while the measured motor torques remained within the allowable drive range. The positive and negative torque signs observed on the left and right sides are explained by mirrored motor installation and coordinate definitions rather than by a special torque-distribution mechanism. This study provides a structural selection and experimental performance reference for mobile operation support in radiation environments. Full article

Tensegrity structures have lightweight, compliant, impact-resistant, and large-deformation characteristics, providing a deformable structural solution for mobile robots in complex environments. Inspired by earthworm peristaltic locomotion, this study proposes BAM-STR, a soft tensegrity robot driven by McKibben pneumatic artificial muscles. The robot adopts a three-layer, three-strut tensegrity structure, and the McKibben pneumatic artificial muscles are arranged at the diagonal and additional tendon positions to generate axial–radial coupled deformation under low-pressure actuation. A bio-inspired segmented peristaltic waveform control strategy is further designed. By sequentially activating and releasing the artificial muscles in the three tensegrity units, the robot generates an axially propagating deformation wave and achieves continuous forward crawling. Experimental results show that BAM-STR can achieve approximately $31\%$ axial contraction and $21\%$ radial expansion at an input pressure of $100\mathrm{kPa}$. When the control time interval is $\Delta T=1.0–1.25\mathrm{s}$, the robot reaches its maximum average crawling speed of approximately $6.5\mathrm{mm}/\mathrm{s}$. Multi-scenario experiments further show that BAM-STR can adapt to channel widths ranging from 190 to $235\mathrm{mm}$, complete continuous crawling while carrying an additional payload of $200\mathrm{g}$, and maintain forward locomotion on a rough artificial grass surface. These results indicate that BAM-STR has path-width adaptability, load-carrying crawling capability, and rough-ground adaptability. Full article

Indoor companion, rehabilitation, logistics, laboratory transport, and service robot scenarios require mobile platforms that can follow a human operator safely and flexibly under lighting changes, occlusion, texture-poor corridors, and dynamic pedestrian environments. Vision-, LiDAR-, and UWB-based following systems can provide high perception capability, but their deployment cost, environmental dependence, and sensing complexity remain limiting factors for low-perception-dependence applications. This paper presents a passive following system for a Mecanum-wheel mobile platform based on gimbal posture perception and orthogonal odometry fusion. A rope-tensioned two-axis gimbal is mounted above a 300 mm × 300 mm × 150 mm omnidirectional chassis, and a six-axis inertial sensor installed at the top of the gimbal detects pitch and roll changes induced by user traction. A piecewise posture-to-velocity mapping model with a dead zone, saturation, low-pass filtering, and acceleration limiting converts the user’s traction intention into planar velocity commands in the vehicle coordinate frame. To reduce pose errors caused by Mecanum-wheel slip and discontinuous roller-ground contact, two orthogonal passive odometry wheels and inertial attitude estimation are fused to provide planar position feedback for closed-loop following. A prototype was implemented using an Infineon TRAVEO CYT4BB77 controller, TI DRV8701E motor drivers, six-axis IMUs, magnetic encoders, and an embedded display interface. Experiments evaluated attitude estimation accuracy, planar localization accuracy, passive following performance, gyroscope compensation, and open-loop/closed-loop following. The compensated attitude module achieved a static yaw drift of 0.45 deg/h and a dynamic attitude RMSE below 0.56 deg. Orthogonal odometry fusion produced an average positioning error of 3.8 mm over a 3000 mm linear displacement, reducing error by approximately 84.6% compared with pure Mecanum-wheel drive odometry. In a 5000 mm forward traction task, closed-loop following reduced the average distance error from 38.6 mm to 11.5 mm compared with open-loop attitude mapping. The results indicate that the proposed gimbal-orthogonal odometry architecture provides a compact, intuitive, and environment-robust solution for passive following on omnidirectional mobile platforms. Full article

Exoskeletons are widely utilized across various domains, including biomedical and rehabilitative engineering. In clinical applications, precise joint torque evaluation is critical to ensuring exoskeleton efficiency, especially when assisting patients with impaired mobility. This work presents a straightforward inverse-dynamics framework to compute human joint torques using motion capture and force plate data. Estimating these torques is a key requirement for exoskeleton systems to deliver appropriate and individualized assistive support. A key innovation of the proposed model is the explicit integration of a three-link chain—comprising the thigh, shank, and foot—treated as a cohesive multi-segment limb. By formally incorporating the foot segment, the model enables a more rigorous representation of ground reaction forces (GRF) and the dynamic migration of the center of pressure (COP). The proposed framework was validated against OpenSim 4.0 using benchmark datasets involving walking, squatting, and drop-jump maneuvers. The results demonstrated strong agreement with OpenSim, yielding normalized root mean square errors of approximately 10% across major lower-limb joints during walking. In contrast, the squatting posture provided a significant magnitude offset, despite maintaining close temporal phase alignment. Beyond torque estimation, the results provide insight into the sensitive interplay among COP trajectories, foot geometry, and GRF orientation. The proposed framework offers a computationally efficient tool for biomechanical analysis and provides a practical foundation for future lower-limb exoskeleton and assistive robotic applications. Full article

This study evaluates an Adaptive Monte Carlo Localization-Extended Kalman Filter (AMCL-EKF) pose-estimation stack for repeatable 2D LiDAR SLAM in GPS-denied indoor inspection scenarios. AMCL was used as an online map-referenced correction source fused with LiDAR odometry and Inertial Measurement Unit (IMU) data, and the resulting pose estimate was supplied online to three SLAM backends: Cartographer, GMapping, and SLAM Toolbox. Experiments were performed with a wheeled Husarion Panther and a quadruped Boston Dynamics Spot in three indoor environments of different geometric complexity, producing 720 SLAM executions. Trajectory repeatability was assessed using SE(2)-aligned pairwise and centroid-based ATE-style dispersion and translational RPE, while map repeatability was evaluated with occupied-cell IoU. Accordingly, the metrics were used to quantify between-run dispersion rather than absolute accuracy against external ground-truth data. The results show that AMCL-EKF fusion is highly dependent on the environment, platform, and SLAM backend. AMCL improved selected configurations, especially for Spot in structured environments and for Panther map consistency, but degraded others in geometrically repetitive corridors and mixed-structure spaces. The study also shows that the presence of AMCL-assisted odometry correction alone does not determine final trajectory repeatability, because each SLAM backend incorporates the supplied fused pose estimate differently. The findings support confidence-aware AMCL integration and motivate integrated SLAM architectures resistant to over-correction. These results provide guidance for robust autonomous mapping and inspection with heterogeneous mobile robotic platforms in real environments. Full article

Background/Objectives: With increasing frequency in sublobar resections, accurate intraoperative localization has become essential to ensure adequate resection margins and spare lung parenchyma. Our study evaluates the efficacy of shape-sensing robotic bronchoscopy (SS-RAB) with integrated mobile cone-beam CT (mCBCT) for intraoperative localization of lung tumors using indocyanine green (ICG). We further aimed to explore the feasibility of a single intubation-single positioning technique for bronchoscopy and surgery. Methods: We retrospectively reviewed patients who underwent SS-RAB with integrated mCBCT for ICG marking, followed by minimally invasive sublobar resection. ICG marking was deemed successful when it allowed the operative team to localize and resect the lesion with adequate pathology margins. Results: A total of 28 patients with 30 pulmonary lesions from a single institution were included. Median tumor size was 10.5 mm (IQR, 8.7–14.6 mm) and distance from pleura 7.8 mm (IQR, 2.45–13.8 mm). Twenty lesions (66.6%) were solid, 5 lesions (16.6%) semi-solid, and 5 lesions (16.6%) ground-glass. ICG localization was successful in 28 lesions (93%). Nineteen patients (68%) were intubated only with a double-lumen endotracheal tube (DL-ETT), used for bronchoscopy and surgery, and in 10 patients (36%) ICG marking and surgery were both performed in lateral decubitus. One patient developed a small pneumothorax during bronchoscopy which did not prevent ICG injection. Conclusions: SS-RAB with integrated mCBCT for ICG marking is successful and safe. Single intubation with DL-ETT and lateral decubitus positioning for both bronchoscopy and surgery are feasible. Further studies are needed to prove a potential increase in efficiency with this technique. Full article

Mobile RFID inventory in libraries must be planned and evaluated under noisy observations, configuration-dependent read regimes, and incomplete supervision. This paper presents an uncertainty-aware analytics framework for robot-assisted RFID inventory using the public RFID Location dataset. The framework has three phases. Phase 1 converts irregular list-encoded logs into atomic RFID events and quantifies how operating configuration changes read density and signal variability. Phase 2 performs map-constrained Bayesian shelf inference by synchronizing RFID reads with robot trajectory and antenna geometry and by fusing RSSI and carrier phase over feasible shelf candidates. Phase 3 translates posterior spread and non-convergence into proxy review workload and cost, enabling configuration comparison and certainty–throughput trade-off analysis when strict EPC-to-item linkage is unavailable. Across 688,073 aligned RFID observations, the pipeline produces 18,190 posterior tag estimates from five inventory runs. The empirical results show strong run dependence: the best run achieves a mean posterior spread of 0.906 m with a convergence rate of 0.553, whereas a degraded run reaches only 0.004 convergence with a mean spread above 2.1 m. Because EPC-to-item linkage is unavailable, these values are posterior concentration and workload indicators rather than ground-truthed localization-accuracy metrics. A saved phase-weight ablation further shows that adding phase information substantially sharpens posterior concentration relative to an RSSI-only baseline. Under the proxy workload model, autonomous-S1-P30 provides the most favorable balance among posterior certainty, scan effort, and implied review burden. Full article

This paper proposes a physically based hybrid architecture for controlling mobile robot drives. It combines a model-based controller, an adaptive neural network compensator for residual dynamics, and a Lyapunov-based stability supervision mechanism. Unlike existing hybrid control approaches, the proposed architecture implements a structured injection of neural network correction directly into the physical drive model with a controlled Lyapunov-based adaptation constraint. A mathematical model of the electromechanical drive of a differential mobile platform is developed, taking into account electrical and mechanical dynamics, wheel-to-surface contact interaction, and the system’s energy characteristics. Numerical simulation results demonstrate that the hybrid approach improves tracking accuracy, improves transient response, and ensures stable operation of the control system under parametric uncertainty, adhesion changes, and external disturbances. The proposed architecture maintains the physical interpretability of the model while simultaneously enhancing the system’s adaptability. The obtained results confirm the effectiveness of the developed method and its potential for application in control systems for mobile robotic platforms. Full article

Future planetary exploration missions are expected to employ increasingly sophisticated aerial, ground, and hybrid robotic systems that must interact directly with extraterrestrial regolith during landing, takeoff, mobility, anchoring, sampling, and subsurface investigation activities. Consequently, understanding the mechanical behavior of lunar and Martian regolith is essential for the design and reliable operation of autonomous exploration platforms. This review examines drone–regolith interaction from a system-level perspective by integrating knowledge of regolith mechanical properties with findings from penetration mechanics, anchoring technologies, mobility studies, numerical modelling, and in situ mission observations. Key differences between lunar and Martian regolith are identified, highlighting the predominantly friction-driven behavior of lunar soils and the combined frictional–cohesive response frequently observed in Martian regolith. Lessons learned from planetary missions, particularly the Apollo and Mars InSight programs, demonstrate how system–soil mismatch can significantly affect penetration, stabilization, and surface-operation performance. The review further discusses the implications of regolith mechanics for landing stability, rotor–surface interaction, anchoring efficiency, subsurface access, and future drone-assisted exploration concepts. Finally, current challenges in experimental validation and numerical modelling are assessed, emphasizing the need for integrated approaches that combine soil mechanics, robotic system design, and environmental constraints to enable reliable autonomous operations on the Moon and Mars. Full article

Inertial Measurement Units (IMUs) enable portable, multibody motion capture in diverse environments beyond the laboratory, making them a desirable choice for diagnosing mobility disorders and supporting rehabilitation in clinical or home settings. However, challenges associated with IMU measurements, including magnetic distortions and errors due to integration drift, complicate their broader use for motion capture. In this work, we propose a tightly coupled motion-capture approach that directly integrates IMU measurements with multibody dynamic models via an iterated extended Kalman filter to simultaneously estimate the system’s kinematics and kinetics. By enforcing the complete multibody system dynamics and utilizing only accelerometer and gyroscope data, our method accurately estimates joint kinematics and kinetics. Our algorithm is designed to fuse different sensor data, such as optical motion-capture measurements and joint torque readings, to further enhance estimation accuracy. We validated our approach using highly accurate ground-truth data from a 3-degree-of-freedom pendulum and a 6-degree-of-freedom collaborative robot. We demonstrate a maximum root-mean-square difference of 3.75° in the pendulum’s computed joint angles with respect to the marker motion-capture inverse kinematics. For the robot, we observed a maximum joint angle root-mean-square difference of 3.24° with respect to the joint encoders, while the maximum joint angle root-mean-square difference of the optical motion-capture inverse kinematics with respect to the encoders was 1.16°. With regard to kinetic estimates, we report a maximum joint torque root-mean-square difference of 3.02 Nm in the pendulum with respect to the marker motion-capture inverse dynamics and 4.27 Nm in the robot relative to its joint torque sensors. Full article

The introduction of autonomous mobile robotic systems into ground handling operations at airports has been limited by aviation safety requirements and the high costs associated with a robot colliding with an aircraft. To ensure safety, traditional robotic navigation methods use static buffer zones, which limit the functionality of robotic systems working near the aircraft fuselage. A probabilistic risk assessment model was developed in this study to simulate close-in operation of heterogeneous mobile robotic systems around an aircraft. The proposed model employs a hybrid framework that integrates an extended Kalman filter, Monte Carlo simulations, and a Bayesian network to consider the kinematic uncertainty of a robot, random environmental conditions, and sensor data for real-time evaluation of collision probabilities. The modeling of near-aircraft inspection scenarios conducted in MATLAB demonstrated the feasibility of the proposed approach: the system successfully completed 49 out of 50 simulated missions while testing landing gear inspection scenarios. In addition, the modeling reduced the minimum distance to the inspection object to 0.48 m, compared with a baseline safe distance of 2 m. These results are interpreted as a simulation-based verification of the feasibility of the proposed approach rather than as an operational validation. Experiments, including hardware modeling, data from real sensors, and controlled tests on the airport apron, are required before implementing the approach in real-world conditions. Full article

As human space missions become longer and more autonomous, robots are expected to assume broader responsibilities in inspection, maintenance, logistics, scientific support, and crew assistance. Among available robot forms, humanoid robotic astronauts are especially relevant because their anthropomorphic embodiment is compatible with human-centered habitats, tools, interfaces, and procedures. Their deployment in orbital and planetary environments, however, introduces challenges that differ from those of terrestrial humanoids, including floating-base dynamics, intermittent contact, whole-body coordination, constrained perception, and delayed supervision. This review contributes a mission-oriented and astronaut-centered synthesis of humanoid robotic astronauts, distinguishing itself from platform-by-platform or morphology-only surveys. It treats these systems as mission-compatible embodied agents whose feasibility depends on the coupling among mission context, morphology, contact behavior, perception, autonomy, and validation evidence. The primary goals are threefold: to classify representative platforms according to mission context, to synthesize the core technical foundations required for mission-compatible operation, and to identify cross-cutting deployment bottlenecks and benchmarking priorities for future development. Representative systems are organized into intravehicular assistance, extravehicular operations and on-orbit servicing, and surface exploration or transitional scenarios, showing how mission demands shape embodiment, mobility, manipulation, autonomy, and validation strategies. This review further summarizes recent progress in microgravity dynamics and contact mechanics, multimodal perception and scene understanding, whole-body motion planning and control, teleoperation and supervised autonomy, and evaluation and benchmarking methods. The analysis indicates that humanoid robotic astronauts are not simple extensions of terrestrial humanoids but astronaut-oriented embodied systems for mission-constrained environments. Three priorities are identified for future development: contact-rich whole-body intelligence under support transitions, delay-tolerant supervised autonomy with explicit authority handoff, and systematic benchmarking pipelines that connect simulation, ground analogs, short-duration microgravity tests, human-in-the-loop trials, and mission-context demonstrations. Full article

This paper presents a comprehensive review of recent works (2020–2026) on machine learning (ML) algorithms applied to autonomous platforms such as unmanned underwater vehicles (UUVs), unmanned surface vehicles (USVs), unmanned aerial vehicles (UAVs), and ground-based mobile robots. The review focuses on the following functional areas: environment perception, simultaneous localization and mapping (SLAM), collision avoidance and path planning, and motion control. Different ML methods are covered, including supervised, semi-supervised, and unsupervised learning, as well as reinforcement learning and deep reinforcement learning. The reviewed methods are analyzed with respect to their performance, robustness, and suitability for different operational environments, including underwater, surface, air, and land domains. Finally, the authors identify key challenges and outline promising future directions aimed at improving the safety, autonomy, and reliability of autonomous vehicles. Full article