Search Results (151)

Large language models (LLMs) exhibit strong semantic reasoning capabilities for autonomous driving decision-making; however, their substantial inference latency poses a critical challenge for real-time closed-loop vehicle control. This study proposes an engineering-oriented framework to enable latency-constrained LLM-based decision-making by integrating bird’s-eye-view (BEV) structured perception with low-bit quantized inference. The BEV perception module compresses multi-view visual inputs into structured semantic representations, thereby reducing input redundancy and enhancing inference efficiency. In addition, 4-bit post-training quantization (PTQ), combined with an optimized inference engine, is employed to alleviate computational and memory bandwidth constraints during autoregressive decoding. Experiments conducted on the CARLA simulation platform under car-following, overtaking, and mixed driving scenarios—validated through 500 independent trials—demonstrate that the proposed framework substantially reduces end-to-end inference latency while maintaining stable decision-making performance. The results indicate that the system satisfies the 10 Hz real-time control requirement and significantly improves control quality, as evidenced by reduced collision rates and lower Average Jerk compared with both traditional imitation learning (Behavioral Cloning, BC) and the Transformer-based TransFuser baseline. Furthermore, sensitivity analyses confirm the robustness of the framework under environmental degradation and perception noise, underscoring the practical feasibility of deploying LLMs for safe and reliable closed-loop autonomous driving. Full article

High-agility spacecraft require time-efficient attitude maneuvers under strict actuator- and system-driven saturation limits on angular rate and angular acceleration. Analytical methods for attitude profile generation are attractive for on-board use because of their deterministic structure and low computational burden; however, depending on boundary conditions and sequential constraint-enforcement logic, they may yield either infeasible commands that violate constraints or overly conservative commands that underutilize available authority and unnecessarily prolong maneuver time. In contrast, numerical optimization-based methods can produce (near-)minimum-time solutions but are often too iterative and tuning-sensitive for real-time deployment. The proposed method produces an iteratively refined closed-form solution. The inner loop yields a closed-form solution for a given set of parameters, while the outer loop updates the parameter set via an iterative rescale step. The resulting finite-jerk (jerk-limited) profiles are intended for use in a feedforward–feedback architecture to mitigate terminal mismatch induced by quaternion-kinematics linearization and acceleration-related variable mappings. Numerical studies evaluate the proposed method using representative single-case examples and Monte Carlo simulations with comparisons against a baseline analytical method and a numerical optimization-based method. These results indicate that the proposed approach substantially improves feasibility and optimality such that it achieves maneuver times close to those of numerically optimized solutions, while maintaining a semi-closed-form structure. Full article

As the core pressure-regulating component of the Electronic Controlled Pneumatic Braking System (ECPBS) for commercial vehicles, the Automatic Pressure Regulating Valve (APRV) directly determines the accuracy and responsiveness of brake pressure adjustment, which is crucial for ensuring braking safety, stability, and ride comfort—especially in the context of autonomous driving. The pressure change rate is a key indicator reflecting braking smoothness and dynamic response performance, and its accurate calculation is the foundation for optimizing braking control strategies. To address the complexity and computational inefficiency in calculating the pressure change rate of multi-component, nonlinear APRV systems, this study proposes an equivalent calculation method based on throttle orifice flow characteristics. By equating the openings and chambers of an APRV to throttling orifices (fixed and variable) and variable-volume cavities, we simplified the complex pneumatic system while preserving its core dynamic characteristics. Theoretical derivation was conducted by integrating the first law of thermodynamics, ideal gas law, and flow equations for fixed/variable throttle orifices to establish a pressure change rate calculation model. The validity of the proposed method was verified through theoretical analysis, numerical simulation, and experimental testing. Compared with existing models, the proposed method achieved a balance between calculation accuracy and efficiency, with the simulation error within 2% (pressure) and 10% (pressure change rate), and it significantly improved computational efficiency compared to conventional models. This research provides a concise and accurate theoretical tool for the rapid prediction and precise control of pressure change rate in ECPBS, which is of great significance for optimizing autonomous driving braking planning, enhancing braking ride comfort by reducing vehicle jerk, and promoting the development of active safety technologies. The proposed equivalent modeling approach also offers a reference for the performance analysis of similar complex pneumatic components or systems. Full article

Background: The vestibular system encodes head motion through specialized Type I and Type II hair cells, which differentially respond to acceleration and its temporal derivative, jerk. Molecular gradients of retinoic acid establish zonal distributions of these hair cells, prefiguring their functional specialization. Objectives & Methods: Here I integrate developmental, synaptic, biomechanical, and neural evidence to propose that Type I hair cells, via multimodal synaptic transmission, are particularly well suited for ultrafast detection of transient inertial deformation (jerk), whereas Type II cells play a greater role in encoding sustained acceleration through viscous-flow mechanisms. Molecular gradients of retinoic acid help establish central–peripheral zonal patterning in the otolith and canal epithelia, which in turn underlies differential mechanical and synaptic specialization rather than a simple redistribution of hair-cell types. Computational and experimental studies reveal that the vestibular organs operate in dual mechanical regimes, enabling the dynamic encoding of motion onset and continuity. In systems terms, these viscous and inertial activation modes correspond to distinct temporal filters, whose different time constants naturally give rise to distinct frequency responses. What has traditionally been described as ‘low- vs. high-frequency’ tuning therefore emerges as the frequency-domain signature of acceleration- versus jerk-sensitive pathways. Conclusions: This hierarchical organization elucidates the selective activation observed in clinical vestibular tests and informs novel diagnostic and rehabilitative strategies targeting specific receptor pathways. Together, these findings redefine vestibular transduction as a multimodal dynamic sensor, enhancing our understanding of balance and spatial orientation under complex motion conditions. Full article

Orbit determination for non-cooperative targets represents a significant focus of research within the domain of space situational awareness. In contrast to cooperative targets, non-cooperative targets do not provide their orbital parameters, necessitating the use of observation data for accurate orbit determination. The increasing prevalence of low-cost, low-thrust spacecraft has heightened the demand for advancements in real-time orbit determination and parameter estimation for low-thrust maneuvers. This paper presents a novel dual-layer filter approach designed to facilitate real-time acceleration estimation for non-cooperative targets. Initially, the method employs a square-root cubature Kalman filter (SRCKF) to handle the nonlinearity of the system and a Jerk model to address the challenges in acceleration modeling, thereby yielding a preliminary estimation of the acceleration produced by the thruster of the non-cooperative target. Subsequently, a specialized filtering structure is established for the estimated acceleration, and two filtering frameworks are integrated into a dual-layer filter model via the cubature transform, significantly enhancing the estimation accuracy of acceleration parameters. Finally, to adapt to the potential on/off states of the thrusters, the Interacting Multiple Model (IMM) algorithm is employed to bolster the robustness of the proposed solution. Simulation results validate the effectiveness of the proposed method in achieving real-time orbit determination and acceleration estimation. Full article

Background: Myoclonus, a sudden brief shock-like involuntary movement, represents a common yet under-recognized manifestation across many inherited metabolic disorders. Although its occurrence has been reported in case series and small cohorts, the overall spectrum, pathophysiological mechanisms, and therapeutic relevance of metabolic myoclonus have not been systematically summarized. Methods: A systematic search of PubMed was conducted for English-language publications from 2014 to 2025 using predefined MeSH terms related to myoclonus, movement disorders, and inborn errors of metabolism. Titles and abstracts were screened independently by three reviewers. After removal of duplicates, 27 articles were included, complemented by 65 additional references addressing individual disorders. Data were organized according to the International Classification of Inherited Metabolic Disorders (ICIMD). Results: Myoclonus was documented across six ICIMD categories, including intermediary metabolism, mitochondrial energy metabolism, lipid metabolism, disorders of complex molecules and organelles, cofactor and mineral metabolism, and metabolic cell signaling disorders. Clinical presentation ranged from isolated jerks to progressive myoclonic epilepsies. Several conditions—such as GLUT1 deficiency, cerebrotendinous xanthomatosis, and folate receptor α deficiency—are treatable through dietary or pharmacological interventions. Conclusions: Recognition of myoclonus as a presenting feature of inherited errors of metabolism (IEMs) is critical for timely diagnosis and treatment. Metabolic screening should be considered in all unexplained cases of myoclonus, particularly when accompanied by developmental delay or systemic abnormalities. Full article

Progressive rock slope destabilization poses significant geohazard risks, necessitating advanced monitoring frameworks to detect precursory failure signals. This study presents a comprehensive time-dependent evaluation of the displacement probability (CTEDP) model, which integrates GNSS-derived spatiotemporal data with multi-parameter reliability indices to enhance landslide risk assessment. Five monitoring points on a destabilizing rock slope were analyzed from mid-November 2024 to early January 2025 using kinematic metrics (velocity, acceleration, and jerk), statistical measures (e.g., moving averages), and reliability indices (RI₀, RI₁, RI₂, and RI_combined). Point 1 exhibited the most critical behavior, with a cumulative displacement of ~60 mm, peak velocities of 34.5 mm/day, and accelerations up to 1.15 mm/day². The CTEDP for active points converged to 0.56–0.61, indicating sustained high risk. The 90th percentile displacement threshold was 58.48 mm for Point 1. Sensitivity analysis demonstrated that the GNSS-derived reliability indices dominated the RI_combined variance (r = 0.999, explaining 99.8% of variance). The first- and second-order reliability indices (RI₁, RI₂) at Point 1 exceeded the 60-index threshold, indicating a transition to Class B (“Low Risk—Trend Surveillance Required”) status, while other points showed coherent deformation of 37–45 mm. Results underscore the framework’s ability to integrate spatiotemporal displacement, kinematic precursors, and statistical variability for early-warning systems. This approach bridges gaps in landslide prediction by accounting for spatial heterogeneity and nonlinear geomechanical responses. Full article

Background/Objectives: The vestibular labyrinth is classically viewed as a sensor of low-frequency head motion—linear acceleration for the otoliths and angular velocity/acceleration for the semicircular canals. However, there is now substantial evidence that air-conducted sound (ACS) can also activate vestibular receptors and afferents in mammals and other vertebrates. This sound sensitivity underlies sound-evoked vestibular-evoked myogenic potentials (VEMPs), sound-induced eye movements, and several clinical phenomena in third-window pathologies. The cellular and biophysical mechanisms by which a pressure wave in the cochlear fluids is transformed into a vestibular neural signal remain incompletely integrated into a single framework. This study aimed to provide a narrative synthesis of how ACS activates the vestibular labyrinth, with emphasis on (1) the anatomical and biophysical specializations of the maculae and cristae, (2) the dual-channel organization of vestibular hair cells and afferents, and (3) the encoding of fast, jerk-rich acoustic transients by irregular, striolar/central afferents. Methods: We integrate experimental evidence from single-unit recordings in animals, in vitro hair cell and calyx physiology, anatomical studies of macular structure, and human clinical data on sound-evoked VEMPs and sound-induced eye movements. Key concepts from vestibular cellular neurophysiology and from the physics of sinusoidal motion (displacement, velocity, acceleration, jerk) are combined into a unified interpretative scheme. Results: ACS transmitted through the middle ear generates pressure waves in the perilymph and endolymph not only in the cochlea but also in vestibular compartments. These waves produce local fluid particle motions and pressure gradients that can deflect hair bundles in selected regions of the otolith maculae and canal cristae. Irregular afferents innervating type I hair cells in the striola (maculae) and central zones (cristae) exhibit phase locking to ACS up to at least 1–2 kHz, with much lower thresholds than regular afferents. Cellular and synaptic specializations—transducer adaptation, low-voltage-activated K⁺ conductances (K_LV), fast quantal and non-quantal transmission, and afferent spike-generator properties—implement effective high-pass filtering and phase lead, making these pathways particularly sensitive to rapid changes in acceleration, i.e., mechanical jerk, rather than to slowly varying displacement or acceleration. Clinically, short-rise-time ACS stimuli (clicks and brief tone bursts) elicit robust cervical and ocular VEMPs with clear thresholds and input–output relationships, reflecting the recruitment of these jerk-sensitive utricular and saccular pathways. Sound-induced eye movements and nystagmus in third-window syndromes similarly reflect abnormally enhanced access of ACS-generated pressure waves to canal and otolith receptors. Conclusions: The vestibular labyrinth does not merely “tolerate” air-conducted sound as a spill-over from cochlear mechanics; it contains a dedicated high-frequency, transient-sensitive channel—dominated by type I hair cells and irregular afferents—that is well suited to encoding jerk-rich acoustic events. We propose that ACS-evoked vestibular responses, including VEMPs, are best interpreted within a dual-channel framework in which (1) regular, extrastriolar/peripheral pathways encode sustained head motion and low-frequency acceleration, while (2) irregular, striolar/central pathways encode fast, sound-driven transients distinguished by high jerk, steep onset, and precise spike timing. Full article

The Parameter Expansion Method (PEM) is employed to study nonlinear Jerk equations, which are often difficult to solve because of their strong nonlinearity. This method provides higher accuracy and broader applicability, enabling analytical insights and closed-form approximations. This study explores the use of Prof. He’s PEM to derive approximate analytical solutions of the nonlinear third-order Jerk equation, this model is commonly encountered in the analysis of complex dynamical systems across physics and engineering. Owing to the strong nonlinearity inherent in Jerk equations, exact solutions are often unattainable. The PEM provides a simple, effective framework by expanding the solution with respect to an embedding parameter, allowing accurate approximations without the need of small parameters or linearization. The method’s reliability and precision are validated through comparisons with numerical simulations, demonstrating its practicality and robustness in tackling nonlinear problems. The results indicate that PEM provides highly accurate approximations of nonlinear Jerk equation, showcasing greater simplicity and efficiency relative to other analytical methods, along with excellent concordance with numerical simulations. Additionally, the nonlinear Jerk equation demonstrates exact approximate solutions via PEM, closely mirroring numerical results and surpassing several contemporary analytical techniques in efficiency and usability. Furthermore, the study indicates that PEM is a straightforward and effective approach in solving nonlinear Jerk equation. It generates accurate estimates that nearly align with numerical simulations and surpass numerous other analytical methods. Full article

This article explores whether jerk, the derivative of acceleration, should be limited in trajectory planning for position-controlled mechanical systems or in the controller. The excess jerk excites structural resonances and increases actuator wear, motivating the use of a limited jerk. However, we question the necessity of incorporating the jerk directly in trajectory planning by comparing third-order jerk-limited trajectories with second-order trajectories with reduced controller bandwidth that regulate torque gradients. We demonstrate by a typical practical application that reducing controller bandwidth can achieve comparable or superior jerk reduction without extending overall motion time for point-to-point trajectories. As a result, second-order parabolic trajectory profiles simplify on-line implementation. This investigation relies on a detailed sensitivity analysis of a one-dimensional model, incorporating crucial elements such as signal and sensor quantisation, sampling, and modes of structural resonances. The study shows that smooth trajectories reduce resonant vibrations and wear, but the jerk limitation may be addressed more effectively within the controller rather than within the trajectory generator. We conclude that although the limitation of the jerk in the trajectories is valuable, feedback controllers can reduce the jerk more effectively by bandwidth reduction, allowing simpler point-to-point trajectory designs without compromising performance. Full article

Dental caries is one of the most widespread chronic infectious diseases for humans. It results in localized destruction of dental hard tissues and has negative impacts on systemic health. Aims: This study aims to design, model, and control a novel 4-DOF dental robotic manipulator, Dentatron, specifically tailored for dental applications. The objectives were to (1) develop a compact robotic arm optimized for dental workspace constraints, (2) implement and compare three controllers—Computed Torque Control (CTC), Fuzzy Logic Control (FLC), and Neural Network Adaptive Control (NNAC), (3) evaluate tracking accuracy, transient response, and robustness in step and trajectory tasks, and (4) assess the potential of adaptive neural controllers for future clinical integration. Materials and Methods: The Dentatron system integrates a custom-designed robotic manipulator with adaptive controllers. The methodology consists of five main stages: robot modeling, control design, neural network adaptation, training, and evaluation. Simulations were performed to evaluate performance across joint tracking and Cartesian trajectory tasks using MATLAB 2022. Human-inspired trajectory design is fundamental to the Dentatron control and simulation framework to emulate the continuous curvature and minimum jerk characteristics of human upper-limb motion. The desired end-effector paths were formulated using fifth-degree polynomial trajectories that produce bell-shaped velocity profiles with gradual acceleration changes. Results: The study revealed that the Neural Network Adaptive Controller (NNAC) achieved the fastest convergence and lowest tracking error (<3 mm RMSE), consistently outperforming Fuzzy Logic Control (FLC) and Computed Torque Control (CTC). NNAC consistently provided precise joint tracking with minimal overshoot, while FLC ensured smoother but slower responses, and CTC exhibited large overshoot and persistent oscillations, requiring precise modeling to remain competitive. Conclusion: NNAC demonstrated the most robust and accurate control performance, highlighting its promise for safe, precise, and clinically adaptable robotic assistance in dentistry. Dentatron represents a step toward the development of compact dental robots capable of enhancing the precision and efficiency of future dental procedures. Full article

Motion planning in robotic systems, particularly in industrial contexts, must balance execution speed, precision, and safety. Excessive accelerations, especially centripetal ones in high, curvature regions, can cause vibrations, reduce tracking accuracy, and increase mechanical wear. This paper presents an off-line motion planning method that integrates curvature-based velocity modulation with jerk- and acceleration-limited S-curve profiles. The approach autonomously adjusts the speed along a predefined path according to local curvature by planning the motion at piecewise constant velocity and ensuring compliance with dynamic constraints on jerk, acceleration, and velocity. A non-linear filter tracks the velocity reference and smooths transitions while maintaining fluid motion, automatically adjusting velocity based on path curvature, ensuring smooth S-curve trajectories without requiring manual intervention. By jointly addressing geometric feasibility and dynamic smoothness, the proposed method reduces execution time while minimizing vibrations in applications involving abrupt curvature variations, as confirmed by its application to planar and spatial trajectories with varying curvature complexity. The method applies to smooth parametric trajectories and is not intended for paths with tangent discontinuities. The simulation results confirm full compliance with the imposed acceleration and jerk limits; nevertheless, future work will include experimental validation on realistic process trajectories and a quantitative performance assessment. Full article

Background/Objectives: Low-cost portable load cell dynamometers allow for real-time assessment of muscular strength. This study evaluated the reliability and repeatability of the G-Force load cell system during isometric hip abduction and adduction in young physically active Chilean adults. Methods: In total, 24 participants (19 men, 5 women) performed two maximal voluntary contractions per movement, repeated after a 24 h interval. Measured variables included Peak Force, peak rate of force development (Peak RFD), RFD at 50, 100, and 200 ms (RFD50, RFD100, RFD200), and maximum jerk. Reliability was assessed using intraclass correlation coefficients (ICCs), standard error of measurement (SEM), coefficient of variation (CV%) and Bland–Altman plots. Results: Peak Force showed excellent within-day (ICC = 0.94–0.96) and high between-day reliability (ICC = 0.87–0.89; CV = 20–30%). Bland–Altman analysis indicated negligible bias for Peak Force in abduction (−6.54 N; 95% CI −19.55 to 6.47) and adduction (−17.57 N; 95% CI −37.24 to 2.09), confirming the absence of systematic error. Peak RFD, RFD50–200, and maximum Jerk showed moderate repeatability and lower between-day reliability (ICCs = 0.39–0.70; CVs = 34–57%), indicating higher variability in explosive force indices compared with maximal strength. Conclusions: The G-Force load cell reliably measures maximal isometric hip strength, while Peak RFD, RFD50–200, and maximum jerk should be interpreted cautiously. These findings support the device as a practical, low-cost tool for sports and rehabilitation, though future studies should validate dynamic indices in larger and more diverse populations. Full article

Understanding vehicle acceleration behavior during intersection departures is critical for advancing traffic safety, sustainable mobility, and intelligent transport systems. This study presents a high-resolution kinematic analysis of 714 vehicle departures from signalized intersections, encompassing straight crossings, left turns, and right turns, and involving a diverse sample of internal combustion engine (ICE), hybrid electric (HEV), and battery electric vehicles (BEV). Using synchronized Micro Electro-Mechanical Systems (MEMS) accelerometers and Real-Time Kinematic (RTK)-GPS systems, the study captures longitudinal acceleration and velocity profiles over fixed distances. Results indicate that BEVs exhibit significantly higher acceleration and final speeds than ICE and HEV vehicles, particularly during straight crossings and longer left-turn maneuvers. Several mathematical models—including polynomial, arctangent, and Akçelik functions—were calibrated to describe acceleration and velocity dynamics. Findings contribute by modeling jerk and delay propagation, supporting better calibration of AV acceleration profiles and the optimization of intersection control strategies. Moreover, the study provides validated acceleration benchmarks that enhance the accuracy of forensic engineering and road accident reconstruction, particularly in scenarios involving intersection dynamics, and demonstrates that BEVs accelerate more rapidly than ICE and HEV vehicles, especially in straight crossings, with direct implications for traffic simulation, ADAS calibration, and urban crash analysis. Full article

Electro-hydraulic servoactuators have great potential in mobile robotics due to their robustness, high bandwidth and power density, but compared with electromechanical actuators, they can be inefficient and more difficult to integrate into systems. The Integrated Smart Actuator (ISA) developed by Moog Controls Ltd. is a hydromechatronic device that aims to address these issues by combining a novel efficient servovalve, cylinder, sensors and control electronics into a single component. The aim of this work was to develop a robust motion control algorithm that can make integration of the ISA into a robotic system straightforward by requiring minimal controller set-up despite variations in the load characteristics. The proposed controller is a sliding mode controller with a varying boundary layer that contains two robustness parameters and a single bandwidth parameter that defines the response. The controller outperforms a conventional high-performance linear controller in terms of tracking performance and its robustness to variations in the load mass and fluid bulk modulus. The response when the system was subject to some unachievable demand trajectories, such as large step demands, was found to be poor, and an online velocity, acceleration and jerk limited trajectory filter was demonstrated to rectify this issue. The successful implementation of a robust motion controller enables this highly novel integrated actuator to live up to its ‘smart’ epithet. Full article