Search Results (331)

Vision navigation systems provide significant advantages in agricultural scenarios such as pesticide spraying, weeding, and harvesting by interpreting crop row structures in real-time to establish guidance lines. However, the delay introduced by image processing causes the path and pose information relied upon by the controller to lag behind the actual vehicle state. In this study, a hierarchical delay-compensated cooperative control framework (HDC-CC) was designed to synergize Model Predictive Control (MPC) and Sliding Mode Control (SMC), combining predictive optimization with robust stability enforcement for agricultural navigation. An upper-layer MPC module incorporated a novel delay state observer that compensated for visual latency by forward-predicting vehicle states using a 3-DoF dynamics model, generating optimized front-wheel steering angles under actuator constraints. Concurrently, a lower-layer SMC module ensured dynamic stability by computing additional yaw moments via adaptive sliding surfaces, with torque distribution optimized through quadratic programming. Under varying adhesion conditions tests demonstrated error reductions of 74.72% on high-adhesion road and 56.19% on low-adhesion surfaces. In Gazebo simulations of unstructured farmland environments, the proposed framework achieved an average path tracking error of only 0.091 m. The approach effectively overcame vision-controller mismatches through predictive compensation and hierarchical coordination, providing a robust solution for vision autonomous agricultural machinery navigation in various row-crop operations. Full article

Path tracking is a fundamental function for autonomous vehicles, but its performance often degrades under parameter variations and controller fragility—an issue seldom addressed together in prior studies. This paper develops a robust non-fragile Linear Quadratic Regulator (LQR) using linear matrix inequality (LMI) optimization, explicitly considering uncertainties in vehicle speed, mass, and cornering stiffness as well as gain perturbations from implementation. A two-degrees-of-freedom bicycle model is employed for controller design, and a weighted least-squares allocation method integrates multiple actuators, including front steering, rear steering, four-wheel independent drive, and braking. A double lane-change maneuver in CarSim evaluates the proposed design. The robust and non-fragile LQR maintains lateral offset within 0.02 m and overshoot below 1% under ±20% parameter variation, offering improved stability margins compared with the baseline LQR. The results highlight context-dependent actuator effects and clarify the trade-off between control complexity, robustness, and real-world applicability. Full article

The integration of autonomous vehicles (AVs) into road transport requires robust experimental tools to analyze the human–machine interaction, particularly under conditions of system disengagement. This study presents the primary controls calibration and virtual scenario validation of the EVACH autonomous driving simulator, designed to reproduce the SAE Level 2 and Level 3 driving modes in rural road scenarios. The simulator was customized through hardware and software developments including a dedicated data acquisition system to ensure the accurate detection of braking, steering, and other critical control inputs. Calibration tests demonstrated high fidelity, with minor errors in brake and steering control measurements, consistent with values observed in production vehicles. To validate the virtual driving rural environment, comparative experiments were conducted between naturalistic road tests and simulator-based autonomous driving, where five volunteers participated in the preliminary pilot test. Results showed that average speeds in the simulation closely matched those recorded on real roads, with differences of less than 1 km/h with minimum standard deviation and confidence values. These findings confirm that the EVACH simulator provides a stable and faithful reproduction of autonomous driving conditions. The experimental platform offers valuable support for current and future research on the safe deployment of automated vehicles. Full article

This paper presents a kinematics-constrained grid-based path planning algorithm that generates real-time, safe, and executable trajectories, thereby enhancing the performance and reliability of autonomous vehicle parking systems. The grid resolution adapts to the minimum turning radius and steering limits, ensuring feasible motion primitives. The cost function integrates path efficiency, direction-switching penalties, and collision risk to ensure smooth and feasible maneuvers. A cubic spline refinement produces curvature-continuous trajectories suitable for vehicle execution. Simulation and experimental results demonstrate that the proposed method achieves collision-free and curvature-bounded paths with significantly reduced computation time and improved maneuver smoothness compared with conventional A* and Hybrid A*. In both structured and dynamic parking environments, the planner consistently maintained safe clearance and stable tracking performance under variations in vehicle geometry and velocity. These results confirm the robustness and real-time feasibility of the proposed approach, effectively unifying kinematic feasibility, safety, and computational efficiency for practical autonomous parking systems. Full article

LEGO SPIKE Prime robots (The LEGO Group, Billund, Denmark) are widely used in educational settings to foster STEM skills and develop problem-solving competencies. A common task in robotics classes and competitions is moving and controlling wheeled vehicles where precise manoeuvrability, especially turning, is essential for successful navigation. This study aims to provide a comprehensive analysis of the turning mechanisms of LEGO SPIKE Prime robots to facilitate more accurate and effective control. This research combines theoretical analysis with experimental validation. We mathematically derived formulas to relate wheel speeds and steering parameters to the turning radius, and we used regression analysis to refine our models. Additionally, we developed a method where the robot itself collects data on its turning performance, enabling autonomous regression modelling. We found that directly adjusting wheel speeds offers greater precision in turning than using a steering parameter. This finding is supported by the results of the Wilcoxon test, which was performed on a random sample of 30 elements and showed that the effect size is significant (r = 0.7) at a significance level of 0.05. This study provides educators and students with a detailed understanding of turning mechanisms and offers guidance on practical and effective means for achieving the accuracy and consistency needed in educational robotics and robot competitions. Full article

Accurate vehicle state estimation in a lane coordinate system is essential for safe and reliable operation of Advanced Driver Assistance Systems (ADASs) and autonomous driving. However, achieving robust lane-based state estimation using only low-cost sensors, such as a camera, an IMU, and a steering angle sensor, remains challenging due to the complexity of vehicle dynamics and the inherent signal delays in vision systems. This paper presents a lane-coordinate-based vehicle state estimator that addresses these challenges by combining a vehicle dynamics-based bicycle model with an Extended Kalman Filter (EKF) and a signal delay compensation algorithm. The estimator performs real-time estimation of lateral position, lateral velocity, and heading angle, including the unmeasurable lateral velocity about the lane, by predicting the vehicle’s state evolution during camera processing delays. A computationally efficient camera processing pipeline, incorporating lane segmentation via a pre-trained network and lane-based state extraction, is implemented to support practical applications. Validation using real vehicle driving data on straight and curved roads demonstrates that the proposed estimator provides continuous, high-accuracy, and delay-compensated lane-coordinate-based vehicle states. Compared to conventional camera-only methods and estimators without delay compensation, the proposed approach significantly reduces estimation errors and phase lag, enabling the reliable and real-time acquisition of vehicle-state information critical for ADAS and autonomous driving applications. Full article

This paper presents an innovative end-to-end framework for conformal antenna array design and beam steering in Low Earth Orbit (LEO) satellite-based IoT communication systems. We propose a multi-stage learning architecture that integrates machine learning (ML) for antenna parameter prediction with reinforcement learning (RL) for adaptive beam steering. The ML module predicts optimal geometric and material parameters for conformal antenna arrays based on mission-specific performance requirements such as frequency, gain, coverage angle, and satellite constraints with an accuracy of 99%. These predictions are then passed to a Deep Q-Network (DQN)-based offline RL model, which learns beamforming strategies to maximize gain toward dynamic ground terminals, without requiring real-time interaction. To enable this, a synthetic dataset grounded in statistical principles and a static dataset is generated using CST Studio Suite and COMSOL Multiphysics simulations, capturing the electromagnetic behavior of various conformal geometries. The results from both the machine learning and reinforcement learning models show that the predicted antenna designs and beam steering angles closely align with simulation benchmarks. Our approach demonstrates the potential of combining data-driven ensemble models with offline reinforcement learning for scalable, efficient, and autonomous antenna synthesis in resource-constrained space environments. Full article

As high-end oil and gas equipment, the high-temperature high-pressure (HTHP) adaptability and intelligence level of Rotary Steerable Systems (RSS) directly determine the development efficiency of deep unconventional resources. This paper reviews the technological breakthroughs and industrial chain synergy pathways of domestic RSS in China, with core conclusions as follows: (1) domestic technologies represented by the CG STEER system have achieved stable operation at 150 °C, high build rates of 15.3°/30 m, and reservoir penetration rates of 98.7%, with key indicators reaching international advanced levels; (2) collaborative innovations in material system reconstruction, hybrid steering mechanisms, and vibration suppression technology have reduced single-well drilling cycles by 50%; (3) industrial chain synergy effects are significant: a 95% localization rate reduced the cost per bottom hole assembly (BHA) run to CNY 2 million, and the “Penta-Helix” innovation model increased patent sharing rates to >60%; (4) breakthroughs in 175 °C high-temperature chips and downhole intelligent decision-making algorithms are urgently needed. This study provides technological paradigms and industrial upgrading pathways for the autonomous development of drilling equipment for extreme conditions. Recognizing the need for comprehensive improvement, the revised manuscript will strengthen three key aspects: (1) supplementing systematic comparisons between domestic technologies and international benchmarks in terms of HTHP adaptability and intelligent control; (2) elaborating technical details of hybrid steering mechanisms and vibration suppression technologies to clarify their innovation in industrial processes; (3) adding case studies of autonomous decision-making systems in ultra-deep wells to verify the practical effectiveness of the proposed methods. These revisions aim to address the current limitations and enhance the scientific rigor of the study. Full article

Polymer worm gears are increasingly utilized in electric power steering (EPS) systems due to their favorable manufacturing features and performance. Ensuring consistent mechanical properties under various operating conditions is critical for steering reliability throughout a vehicle’s lifespan. This study investigates the durability of injection-molded polyamide 66 worm gears within a Pinion-EPS configuration, where torque from the assist motor is transmitted through a worm–worm gear set to the rack and ultimately to the vehicle wheels. Given the complexity of steering maneuvers and the absence of mechanical integrity in steer-by-wire systems, durability testing becomes essential to understand if the considered worm gear for a certain steering system application provides safety and the needed performance within a specified product service life. This paper compares multiple testing methodologies. Traditional approaches, such as maximum torque and rotational speed, prove insufficient for comprehensive durability assessment, especially considering the thermal sensitivity of polymer materials. The findings highlight the limitations of conventional testing methods and emphasize the need for application-specific testing methods that reflect real-world boundary conditions. This research contributes to the development of more accurate and reliable evaluation techniques for polymer gear components in modern EPS systems, with implications for both conventional and autonomous vehicle platforms. Full article

In this paper, the concept of symmetry is utilized to design active fault tolerant trajectory-tracking control of autonomous distributed-drive electric vehicles—that is, the construction and the solution of active fault tolerant trajectory-tracking controllers are symmetrical. This paper presents a hierarchical fault tolerant control strategy for improving the trajectory-tracking performance of autonomous distributed-drive electric vehicles (ADDEVs) under steer-by-wire (SBW) system failures. Since ADDEV trajectory dynamics are inherently affected by complex traffic conditions, various driving maneuvers, and other road environments, the main control objective is to deal with the ADDEV trajectory-tracking control challenges of system uncertainties, SBW failures, and external disturbance. First, the differential steering dynamics model incorporating a 3-DOF coupled system and stability criteria based on the phase–plane method is established to characterize autonomous vehicle motion during SBW failures. Then, by integrating cascade active disturbance rejection control (ADRC) with Karush–Kuhn–Tucker (KKT)-based torque allocation, the active fault tolerant control framework of trajectory tracking and lateral stability challenges caused by SBW actuator malfunctions and steering lockup is addressed. The upper-layer ADRC employs an extended state observer (ESO) to estimate and compensate against uncertainties and disturbances, while the lower-layer utilizes KKT conditions to optimize four-wheel torque distribution to compensate for SBW failures. Simulations validate the effectiveness of the controller with serpentine and double-lane-change maneuvers in the co-simulation platform MATLAB/Simulink-Carsim^® (2019). Full article

The present article compares the Proportional Directional Control Valve (PDCV) and the Stepper Motor-Driven Orbitrol Valve (SMDOV) coupled to the steering system of the articulated steered vehicles. Simulation models of both valve coupled steering systems are developed in a MATLAB (r2019b) environment, and results are well validated with the experimental data. Comparison analysis is performed between the PDCV and SMDOV steering systems by controlling the desired position demand using a conventional PID controller. From the comparative study, it is observed that the SMDOV provides almost 50% energy reduction, but the valve response is low compared to PDCV. However, the steering response provided by the SMDOV is quite enough for performing steering operations in mining conditions. Overall, the orbitrol valve-assisted steering system offers more efficient and smooth steering than the PDCV valve. The future work of the present study extends to the development of autonomous steering operation using an orbitrol valve-operated articulated steering system. Full article

Cardiovascular disease remains the world’s leading cause of mortality, yet everyday care still relies on episodic, symptom-driven interventions that detect ischemia, arrhythmias, and remodeling only after tissue damage has begun, limiting the effectiveness of therapy. A narrative review synthesized 183 studies published between 2016 and 2025 that were located through PubMed, MDPI, Scopus, IEEE Xplore, and Web of Science. This review examines CVD diagnostics using innovative technologies such as digital cardiovascular twins, which involve the collection of data from wearable IoT devices (electrocardiography (ECG), photoplethysmography (PPG), and mechanocardiography), clinical records, laboratory biomarkers, and genetic markers, as well as their integration with artificial intelligence (AI), including machine learning and deep learning, graph and transformer networks for interpreting multi-dimensional data streams and creating prognostic models, as well as generative AI, medical large language models (LLMs), and autonomous agents for decision support, personalized alerts, and treatment scenario modeling, and with cloud and edge computing for data processing. This multi-layered architecture enables the detection of silent pathologies long before clinical manifestations, transforming continuous observations into actionable recommendations and shifting cardiology from reactive treatment to predictive and preventive care. Evidence converges on four layers: sensors streaming multimodal clinical and environmental data; hybrid analytics that integrate hemodynamic models with deep-, graph- and transformer learning while Bayesian and Kalman filters manage uncertainty; decision support delivered by domain-tuned medical LLMs and autonomous agents; and prospective simulations that trial pacing or pharmacotherapy before bedside use, closing the prediction-intervention loop. This stack flags silent pathology weeks in advance and steers proactive personalized prevention. It also lays the groundwork for software-as-a-medical-device ecosystems and new regulatory guidance for trustworthy AI-enabled cardiovascular care. Full article

Given the growing need for high-performance operation of 4WID-UGVs, coordinated optimization of trajectory tracking, vehicle stability, and energy efficiency poses a challenge. Existing control strategies often fail to effectively balance these multiple objectives, particularly in integrating energy-saving goals while ensuring precise trajectory following and stable vehicle motion. Thus, a hierarchical control architecture based on Model Predictive Control (MPC) is proposed. The upper-level controller focuses on trajectory tracking accuracy and computes the optimal longitudinal acceleration and additional yaw moment using a receding horizon optimization scheme. The lower-level controller formulates a multi-objective allocation model that integrates vehicle stability, energy consumption, and wheel utilization, translating the upper-level outputs into precise steering angles and torque commands for each wheel. This work innovatively integrates multi-objective optimization more comprehensively within the intelligent vehicle context. To validate the proposed approach, simulation experiments were conducted on S-shaped and circular paths. The results show that the proposed method can keep the average lateral and longitudinal tracking errors at about 0.2 m, while keeping the average efficiency of the wheel hub motor above 85%. This study provides a feasible and effective control strategy for achieving high-performance, energy-saving autonomous driving of distributed drive vehicles. Full article

This paper presents a fully electronic, CMOS-compatible ultrasonic sensing system integrated into a 3D beamforming architecture for advanced automotive applications. The proposed system eliminates mechanical scanning by implementing a dual-path beamforming structure comprising programmable transmit (TX) and receive (RX) paths. The TX beamformer introduces per-element time delays derived from steering angles to control the direction of ultrasonic wave propagation, while the RX beamformer aligns echo signals for spatial focusing. Electrostatic actuation governs the CMOS-compatible ultrasonic transmission mechanism, whereas dynamic modulation under acoustic pressure forms the reception mechanism. The system architecture supports full horizontal and vertical angular coverage, leveraging delay-and-sum processing to achieve electronically steerable beams. The system enables low-power, compact, and high-resolution sensing modules by integrating signal generation, beam control, and delay logic within a CMOS framework. Theoretical modeling demonstrates its capability to support fine spatial resolution and fast response, making it suitable for integration into autonomous vehicle platforms and driver-assistance systems. Full article

This study presents a simulation-based feasibility analysis of a beam steering metasurface, theoretically driven by mechanical energy harvested from human motion via a triboelectric nanogenerator (TENG). In the proposed model, the TENG converts biomechanical motion into alternating current (AC), which is rectified into direct current (DC) to bias varactor diodes integrated into each metasurface unit cell. These bias voltages are numerically applied to dynamically modulate the local reflection phase, enabling beam steering without external power. Full-wave electromagnetic simulations were conducted to confirm the feasibility of beam manipulation under TENG-generated voltage levels. The proposed simulation-driven design offers a promising framework for battery-free, adaptive electromagnetic control with potential applications in wearable electronics, intelligent sensing, and energy-autonomous radar systems. Full article