Search Results (3,430)

Achieving safe physical interaction on the human back is challenging due to respiratory rhythms, complex topography, and varying tissue stiffness. To enable compliant force tracking within commercial closed position-control robot architectures, this paper presents an adaptive variable damping admittance control framework driven by multi-dimensional force sensor feedback. A stiffness-free admittance model is constructed to eliminate steady-state tracking errors, integrated with a nonlinear adaptive damping law that sensitively responds to real-time force sensor measurements. This mechanism rapidly dissipates dynamic impact energy during contacts while maintaining low impedance during steady state. Validated via a high-fidelity MATLAB R2024b-CoppeliaSim co-simulation platform replicating Traditional Chinese Medicine (TCM) manipulations, the proposed sensor-driven strategy significantly improves force tracking fidelity over traditional fixed-parameter control. Quantitative results demonstrate that across all complex therapeutic waveforms, the root mean square error (RMSE) remains below 0.42 N, the mean absolute error (MAE) is within 0.32 N, and the squared correlation coefficient ($r^2$) exceeds 0.97. These findings confirm the high efficiency and clinical potential of the proposed framework. Full article

To address prevalent industrial challenges, including the high cost of fabricating microstructures via photolithography and 3D printing, impurity residues easily generated by conventional physical/chemical pore-forming techniques, and the limited sensitivity of regular capacitive sensors, this paper innovatively proposes an integrated low-temperature in situ gas foaming strategy using ammonium bicarbonate for the fabrication of porous TPU-based ionic gels. Relying on the complete gaseous decomposition property of ammonium bicarbonate upon heating, a three-dimensionally interconnected continuous porous network is spontaneously constructed inside the polymer matrix. Thermoplastic polyurethane (TPU) is selected as the continuous polymer phase, and [EMIM][TFSI] imidazolium ionic liquid is blended as the ion source to synthesize composite ionic gel substrates. A PDMS composite slurry filled with graphene is employed to prepare flexible substrates, followed by low-temperature oxygen plasma surface modification to introduce polar functional groups such as hydroxyl and carboxyl onto electrode surfaces. A standard sandwich-structured ionic pressure sensor with the configuration of “top modified electrode—porous ionic gel dielectric layer—bottom modified electrode” is finally assembled. The porous framework and modified electrodes constitute a dual synergistic enhancement system: the porous structure markedly reduces the equivalent elastic modulus of the gel and improves its compressive deformation capacity; polar-modified electrodes optimize the interfacial compatibility between electrodes and gels, shorten ion migration paths and lower interfacial contact resistance. Systematic calibration of multiple batches of parallel samples reveals that the as-fabricated sensor achieves a high sensitivity of 25.3 kPa⁻¹ across the full measuring range from 0 to 1000 kPa with a linear fitting coefficient R² = 0.992. The loading response time and unloading recovery time of the device are 60 ms and 80 ms respectively, with a performance degradation of less than 3% after 1000 consecutive loading–unloading cycles, featuring low hysteresis error and excellent signal repeatability. Multi-scenario in vivo wearable tests on human subjects verify that the device can precisely capture subtle fluctuations of radial artery pulse and periodic laryngeal deformation during swallowing, distinguish characteristic waveform patterns of various English words according to differences in vocal cord vibration, and accurately detect bending motions when attached to finger joints. The entire fabrication process adopts common chemical raw materials and standard laboratory equipment without expensive micro-nano processing facilities, featuring convenient raw material procurement and high process fault tolerance, which enables large-area coating-based mass production. This work delivers a novel technical route for the low-cost large-scale production of high-performance ionic flexible sensors and bears significant industrialization reference value for applications in wearable medical monitoring, bionic robotic electronic skin, flexible human–machine interactive touch panels and other related fields. Full article

Low-latency visual perception is essential for interactive machine vision on edge AI devices, but conventional frame-based image sensors impose frame period delays and generate dense image data that increase memory bandwidth and processing latency. Although Dynamic Vision Sensors (DVSs) are known to provide low latency, sparse output, and high dynamic range, these sensor-level properties do not automatically translate into practical application-level latency reduction on resource-constrained edge platforms. This paper presents a latency-driven sensing algorithm co-design approach for DVS-based low-latency machine vision. The main objective is to connect DVS sensor-level characteristics, event representations, task-dependent processing flows, and measured response times on mobile application processors. We first analyze latency requirements for three representative edge AI applications (i.e., person detection, gesture recognition, and Simultaneous Localization and Mapping (SLAM)), which correspond to different latency regimes and processing structures. We then describe the DVS operating principle, pixel-level event latency, and readout latency, showing how asynchronous event generation reduces sensing delay and suppresses redundant static background information before algorithmic processing. In contrast to prior event camera studies that mainly optimize a single task or a specific event representation, this work evaluates three task-specific event processing systems on mobile processors. Person detection achieves 92 ms processing latency on Exynos 7570, gesture recognition based on event-driven 4-DoF motion estimation achieves 20 ms latency on Exynos 5422, and SLAM achieves 15.9 ms latency on Snapdragon 845. These results satisfy the practical latency targets of the corresponding applications and demonstrate that DVS-based sensing can provide not only sensor-level speed advantages but also system-level latency benefits for AIoT, mobile, robotics, and AR/VR machine vision systems. Full article

Preclinical testing is essential for evaluating new implant designs and materials for total knee replacement (TKR). Standardized wear tests, such as ISO 14243, are widely accepted but only partially represent physiological kinematics and kinetics, as they do not account for all six degrees of freedom of the knee joint. More advanced setups, including robotic systems and joint simulators, enable complex load cases; however, the influence of lubrication conditions and testing speeds remains insufficiently standardized. This study investigated the kinematic and kinetic effects of different lubrication conditions (dry, synthetic synovial fluid, silicone oil) and speeds (static, 10%, 50%, 100% of normal gait) in a joint simulator setup using a posterior cruciate ligament-retaining TKR during level walking. Complementary pin-on-disk measurements revealed significant dependencies on both lubrication and speed. During joint simulator tests, omitting lubrication resulted in more than double the maximum flexion–extension moment, while the range of anterior–posterior femoral translation increased by approximately 73%. At 50% and 100% speed, silicone lubrication yielded results comparable to static tests, in contrast to the dry and synthetic synovial fluid conditions. These findings demonstrate that physiologically relevant lubrication and appropriate test speeds are essential for obtaining reliable results in experimental studies of TKR dynamics. Full article

A reactive agent operating in a complex environment must classify its perceived state and select an action under uncertainty. This uncertainty may arise from sensor noise, ambiguous perceptual configurations, or the limited separability of the action regions induced by the agent’s policy. We propose a hybrid classical–quantum architecture for a reactive agent in which the perceived state, represented as a classical sensor vector, is mapped onto a quantum feature space. In this space, learned conceptualizations or rule-defined perceptual regions are represented as reference states, and similarities between the current perception and such references are used to support action selection. The architecture is evaluated on a public wall-following robot dataset. Two implementations are considered: (i) a quantum-kernel classifier based on ZZ feature maps and (ii) an illustrative quantum circuit that explicitly encodes sensor conditions into qubits and performs measurement-based action selection. The experimental evaluation is intended as an offline proxy for reactive decision-making, not as a demonstration of a complete closed-loop robotic controller or of quantum advantage. The results show that the proposed framework can represent perceptual ambiguity and connect quantum-state measurement to the selection of discrete reactive actions. Full article

Reliable indoor localization is required for automated guided vehicles (AGVs), robot validation, and industrial digital-twin applications, but ultrasonic positioning can degrade sharply when acoustic visibility changes. This paper evaluates Marvelmind Super-Beacon localization in controlled laboratory experiments involving both AGV tracking and UR10 robot-arm positioning. The non-inverse architecture (NIA) and inverse architecture (IA) configurations are included as parallel validation scenarios to assess the robustness of the proposed mitigation framework across different Marvelmind deployment modes. The baseline analysis identifies the dominant acoustic failure modes, including multipath-induced scatter, crossover-zone handover jumps, update-rate degradation, complete non-line-of-sight (NLoS) outages, and height-dependent 3D jitter. To mitigate these effects, an embedded ultrasonic–inertial pipeline is implemented on an ESP32-S3-WROOM-1 module. The system combines UART packet validation, interrupt-driven ICM-20948 inertial acquisition at 500 Hz, sliding-window kinematic outlier rejection, and a 15-state error-state Kalman filter (ESKF). The embedded estimator logic is designed to maintain motion continuity during intermittent or corrupted acoustic positioning while reintroducing validated ultrasonic absolute corrections. Using recorded AGV and UR10 datasets, mitigation performance was quantitatively assessed through a firmware-consistent replay of the recorded measurements, using the same gating, inertial propagation, and measurement-update logic as the real-time ESP32-S3 implementation. Across ten trials per configuration, the replay-based trial-mean RMSE in the 2D AGV scenarios decreased from 101.2–104.1 mm for raw ultrasonic data to 47.2–48.7 mm after fusion, while peak failure-interval errors were reduced by 64.2–65.7%. In the 3D UR10 scenarios, replay-based trial-mean RMSE decreased from 157.6–158.4 mm to 80.2–80.5 mm, and peak height-sensitive 3D errors were reduced by 58.8–60.0%. The results demonstrate the feasibility of embedded ultrasonic–inertial robustness enhancement for localization in controlled laboratory AGV and robot-arm scenarios. While the proposed approach shows promising performance under the investigated conditions, further validation is required before extending the conclusions to larger-scale and dynamically changing industrial environments. Full closed-loop online robot localization and control based directly on the fused localization output remain subjects for future investigation. Full article

Freehand three-dimensional (3D) ultrasound enables cost-effective volumetric breast imaging, but accurate reconstruction requires reliable probe tracking during manual scanning. This study proposes a portable freehand 3D ultrasound framework using dual-rotary-encoder two-degree-of-freedom (2DoF) pose sensing to measure probe displacement and inclination during breast scanning. A slip-resistant roller mechanism and time-aware trajectory modeling were introduced to improve measurement robustness under practical scanning conditions. The framework was evaluated through robotic experiments and phantom-based volumetric reconstruction. Positional displacement experiments achieved root mean square errors (RMSEs) of 0.38 mm on dry surfaces and 0.81 mm under gel-coated conditions. Inclination sensing using the rotary encoder outperformed an inertial measurement unit (IMU), achieving an RMSE of 2.76° with improved temporal stability. Reconstruction experiments using a breast phantom with spherical inclusions demonstrated successful volumetric visualization across multiple scanning trajectories. Statistical analysis revealed significant effects of inclusion size and scanning trajectory on relative reconstruction error, as well as a significant interaction between the two factors. Larger inclusions generally exhibited lower relative errors, while the influence of scanning trajectory depended on the target size. These findings support the feasibility of the proposed reduced-dimensional mechanical pose sensing approach for reliable freehand 3D ultrasound reconstruction with reduced hardware complexity. Full article

To address the problem of high-frequency roll disturbances generated during dynamic balancing in non-coaxial two-wheeled robots, this paper proposes a Rotation-Optimized Inertial–Visual SLAM system (ROIV-SLAM) for robust state estimation. The proposed approach adopts a decoupled architecture for translation and rotation estimation. In the front-end, an Extended Kalman Filter (EKF) is employed to fuse LiDAR, an inertial measurement unit (IMU), and wheel odometry to obtain an initial translation estimate. Meanwhile, a physical manifold constraint is constructed using the gravity vector and surface normals extracted from RGB-D point clouds, supporting stable rotation estimation under high-frequency disturbances through Lie-group-based optimization. In the back-end, a factor graph is established, and loop closure robustness is enhanced through vision–LiDAR scan matching. Experimental results indicate that ROIV-SLAM achieves improved trajectory consistency with respect to the optimized reference trajectory and more robust mapping performance compared with the evaluated baseline approaches in the tested scenarios. The results further suggest that introducing task-specific physical dynamic constraints and a decoupled estimation mechanism helps suppress high-frequency motion noise inherent to balancing robots, thereby improving the robustness of state estimation in complex environments. Full article

Source-Seeking in unknown scalar fields is a fundamental problem in robotics with applications in environmental monitoring and disaster response. In this work, we present a source-seeking approach with non-reversing forward velocity regulation by fusing measurement data from multiple sensors within the Stochastic Extremum Seeking (SES) framework. Specifically, a device model with multiple sensors is first constructed, and then a velocity regulation scheme is designed by leveraging the boundedness of the hyperbolic tangent function and the non-negativity of the exponential function to guarantee strictly positive forward velocity. We then evaluate the algorithm both in simulation environments and on the real-world Two-Wheeled Differential Drive Robot platform. The experiments show that our approach not only ensures the forward velocity remains non-negative, aligning with the design expectation, but also accurately locates the source. This work provides new insights into the design of velocity regulation strategies within the SES framework. Full article

This work addresses the design and implementation of an automated system for the handling and transportation of parts, integrating speed sensors, an optimized PID controller, an HMI interface, and an industrial robotic system. The speed sensors, powered by 5 V DC, enable continuous measurement of the conveyor belt’s speed and direction of rotation, providing the feedback signal required for the control loop. The core element of the system is the implementation of a PID controller applied to a direct current motor responsible for driving the conveyor belt. This controller regulates the motor speed by analyzing the error between the reference speed and the measured speed, using proportional, integral, and derivative actions to improve system stability, reduce steady-state error, and minimize oscillations. The application of PID control makes it possible to achieve an appropriate dynamic response, ensuring accuracy and reliability in the transportation process. System monitoring and operation are carried out through a human–machine interface (HMI) developed in LOGO Web Editor, which communicates with the PLC (LOGO V8) to visualize and control the status of the conveyor belt, sensors, and control elements in real time. This interface facilitates interaction between the operator and the system, allowing both virtual and physical operation. In addition, RAPID programming is used to control the IRB 14000 industrial robot, enabling the reading of PLC signals and the execution of coordinated trajectories between both arms. The operating sequence includes picking up a part with the left arm, placing it on the conveyor belt, and, after detection by sensors and PLC control, subsequent manipulation by the right arm to a specific point. Finally, both arms return to their original position, ensuring synchronized and collision-free operation. Lastly, this work integrates scientific knowledge related to the modeling, analysis, and control of dynamic systems, particularly in the implementation of closed-loop PID control optimized using genetic algorithms. This control is applied directly to an embedded system through the use of an Arduino board as the processing and control platform. Likewise, technological knowledge associated with industrial automation, PLC programming, HMI development, and industrial robotics is incorporated. The convergence of these scientific and technological approaches results in a comprehensive and compelling project that demonstrates the practical application of theoretical concepts in a functional automated system representative of real industrial environments. Full article

Reliable outdoor localization is essential for autonomous mobile robots, where the Global Navigation Satellite System (GNSS) is widely used to provide global positioning information. However, GNSS signals are often degraded in real-world environments due to occlusions, multipath effects, and environmental interference, leading to unstable localization and degraded navigation performance. This paper proposes an adaptive multi-sensor fusion framework for robust outdoor localization and path tracking under weak GNSS conditions. The proposed system integrates GNSS, LiDAR, wheel odometry, and inertial measurement unit (IMU) measurements within an Extended Kalman Filter (EKF) framework. To address the limitations of GNSS, an adaptive weighting mechanism is introduced to dynamically adjust the influence of GNSS observations based on signal quality indicators. Furthermore, a GNSS quality-aware mode-switching strategy is developed, enabling seamless transition between GNSS-dominant localization and multi-sensor fusion-based localization. In the fusion mode, LiDAR, odometry, and IMU jointly provide robust pose estimation, while GNSS acts as a weak global constraint. The IMU further enhances heading estimation, improving orientation stability and path tracking performance. The estimated pose is then used for trajectory tracking using a path-following controller. Experimental results conducted in outdoor environments demonstrate that the proposed framework significantly improves localization robustness and path tracking performance under degraded GNSS conditions. Compared with raw GNSS localization, the proposed method reduces the mean localization error by 47.2% and decreases the root mean square localization error by 55.5%, while maintaining smoother and more continuous trajectory estimation in weak GNSS environments. Full article

The integration of collaborative robots into industrial environments requires rigorous safety validation under the Power and Force Limiting (PFL) regime. This review article systematically maps the technological and normative development of certified Pressure and Force Measurement Devices (PFMDs) and experimental biofidelic instruments for Physical Human–Robot Interaction (pHRI) between the years 2011 and 2026. A quantitative screening of 68 studies revealed a publication peak in impact metrology in 2021. This peak occurred with a five-year latency after the release of the ISO/TS 15066 technical specification. Although global interest in collaborative robotics steadily grows, the publication trend indicates a gradual shift in scientific focus from reactive testing toward proactive prevention. A methodological deconstruction of four Research Questions (RQs) identifies persistent limitations in safety evaluation. The findings demonstrate that the internal structure of conventional sensors induces nonlinear shock filtering and parasitic oscillations (RQ1). Furthermore, the rigid fixation of test stands generates unrealistic pressure spikes. This physical limitation forces a transition to flexible and pendulum-based configurations (RQ2). Commercial flat films physically fail due to sensor saturation and introduced stiffness. Such failures accelerate the development of conformable electronic skins (e-skins) and multimodal test manikins (RQ3). To ensure interlaboratory reproducibility within the current ISO 10218-2:2025 standard, the text defines imperative metrological parameters. These parameters strictly include frequency response, calibration protocols, and volumetric mapping of inertial masses (RQ4). Furthermore, the analysed publications were systematically stratified into distinct technological categories, strictly reflecting their primary engineering domains, ranging from empirical metrological evaluation and sensor hardware design to advanced numerical modeling. Finally, the vision for future research anticipates a definitive shift toward proactive anti-collision technologies, encompassing Artificial Intelligence (AI), machine vision, and Augmented Reality/Virtual Reality/Mixed reality (AR/VR/MR). Future methodologies must also consider demographic anisotropies and the cognitive fatigue of the human operator. Full article

This study presents a comprehensive symmetry-breaking analysis framework for a twelve-legged Jansen walking robot, integrating finite element analysis (FEA) with adaptive neuro-fuzzy inference system (ANFIS) surrogate modeling. A systematic dataset of 210 cases was generated by combining 21 single- and multi-leg failure scenarios across 10 load levels (20–200 N) on the PLA-based 3D-printed prototype. Two novel dimensionless metrics are introduced: the Resilience Index (RI), quantifying the proportional stress increase relative to the baseline, and the Asymmetry Index (AI), measuring leg-reaction force distribution imbalance. Results identify a clear fault-tolerance threshold between two- and four-leg failures: single-leg failures remain at LOW risk (RI < 0.20), while three-leg asymmetric failures (S18) reach CRITICAL level (RI = 1.13, ~97% of PLA yield strength). A hybrid machine learning framework is proposed, applying ANFIS to maximum stress (R² = 0.817) and safety factor (R² = 0.936) predictions, while reserving FEA tables for bimodal outputs. The ANFIS surrogate achieves approximately 10⁶× speedup over FEA (262.6 μs vs. 5–8 min), enabling real-time fault diagnosis and digital twin applications. The framework is generalizable to other multi-legged robotic systems requiring fault-tolerance evaluation. Full article

Maintaining reliable communication while ensuring collision-free motion is a central challenge for mobile relay robots operating in indoor disaster environments, where abrupt non-line-of-sight (NLOS) degradation and narrow structural bottlenecks can severely disrupt multi-hop connectivity. To address this problem, this paper proposes a Gaussian Process-Driven Adaptive Tube Model Predictive Control (GP-ATMPC) framework for communication-preserving relay navigation. Gaussian process regression (GPR) is used to construct a probabilistic spatial radio map from sparse received signal strength indicator (RSSI) measurements, providing both the predicted channel mean and its uncertainty over unvisited regions. Motion uncertainty is represented by an adaptive ellipsoidal error tube whose radius varies with translational motion, angular motion, and localization uncertainty. Based on this tube model, both obstacle and communication constraints are tightened over the full closed-loop state tube via a tube-tightened lower confidence bound (LCB) that jointly accounts for radio-prediction and motion-tracking uncertainty. Across two indoor disaster environments and 50 Monte Carlo runs each, the proposed method attains the highest connectivity satisfaction rate among controllers that preserve a safe motion margin, with significantly fewer end-to-end connectivity violations than nominal and heuristic adaptive-margin MPC by a paired Wilcoxon test, while maintaining millisecond-level online solve times. A reactive connectivity-first baseline reaches slightly higher raw connectivity but at three to four times the near-collision rate and without feasibility or stability guarantees. The radio-prediction layer is further validated in a higher-fidelity Gazebo environment and on real indoor RSSI measurements, where it reconstructs the measured channel with a mean absolute error of about 2.1 dB. These results indicate that coupling spatial radio prediction with adaptive tube-based robust control provides an effective framework for resilient communication-aware relay navigation in degraded indoor environments. Full article

The evaluation of wearable robotic systems remains a challenge, particularly in real-world environments where laboratory-based methods are impractical. Existing approaches rely on external instrumentation, such as surface electromyography (sEMG) or motion capture, which are difficult to deploy continuously and do not directly measure key internal metrics such as joint loading or spinal forces. This work introduces a new paradigm for exoskeleton evaluation in which biomechanical assessment is embedded directly within the device’s sensing and computational architecture. We present the ExoMetrix system, a platform that integrates onboard sensing, real-time data acquisition, cloud-based processing, and user-facing analytics into a unified workflow for continuous evaluation of human–exoskeleton interaction. Sensor data from the device are streamed and processed using physics-based models. The resulting outputs are translated into estimates of internal biomechanical quantities, including joint torques, spinal compression and shear forces, and muscle loading. By enabling real-time feedback and longitudinal monitoring without external instrumentation, this approach transforms evaluation from an external, episodic process into an embedded and continuous capability, supporting safer and more scalable deployment of exoskeleton technologies. Full article