Search Results (1,903)

Autonomous drifting requires coordinated steering and driving actuation near the tire friction limit, where strong tire nonlinearity and rapidly changing constraints challenge control accuracy and real-time solvability. This article proposes an equilibrium-free successive convexification (SCvx) nonlinear model predictive control framework for drift tracking under constant and varying curvature conditions. The front steering angle and rear-axle longitudinal force are optimized jointly subject to actuator, state, and tire-force constraints. A physics-guided MLP residual tire model is introduced to improve rear-tire-force prediction. Online reference generation determines the heading error, yaw rate, and rear longitudinal force targets from path curvature, lateral error, sideslip variation, and rear slip ratio error, eliminating the need for precomputed drift equilibria. SCvx converts the nonlinear predictive control problem into convex subproblems using virtual control, slack variables, and trust regions. Hardware-in-the-loop experiments confirm stable actuator coordination under both test conditions. Under varying curvature drifting, the proposed method reduces lateral error, velocity error, and yaw rate error by 39.2%, 53.7%, and 24.9%, respectively, compared with the Fiala tire model using the same solver. The results demonstrate improved tracking accuracy and numerical robustness for constrained autonomous drift control. Full article

Structural analysis using X-rays and electron beams has long provided the average arrangement of atoms and molecules—that is, “structural information”—with high precision. By contrast, static measurements cannot directly yield dynamic information on how a material changes over time; instead, information on motion is convolved into a single numerical value such as the B-factor (atomic displacement parameter). Taking this limitation as its starting point, this review surveys the recent trend of introducing a time axis into measurements to observe material dynamics directly. First, we outline the technological foundations that have made the transition from static to time-resolved measurement possible. It rests on the dramatic shortening of exposure times, enabled by the increased brilliance of X-ray and electron sources and by advances in detection technology such as direct photon-counting detectors. Next, we survey dynamic measurement techniques, including time-resolved X-ray crystallography, coherent X-ray scattering, neutron scattering, and time-resolved electron microscopy. We also point out the essential limitation that most of them still return ensemble or volume averages. Building on this, we systematically describe diffracted X-ray tracking (DXT), diffracted X-ray blinking (DXB), small-angle X-ray blinking (SAXB), transmitted X-ray blinking (TXB), and electron-beam molecular dynamics (EBMD), which use gold nanocrystals and gold nanoparticles as motion probes. We distinguish throughout between methods that follow individual objects—DXT and EBMD, which yield trajectories of single labeled molecules or single particles—and methods that analyze intensity fluctuations arising from many contributors within one pixel or illuminated volume—DXB, SAXB and TXB. The latter are not single-molecule measurements; rather, they replace a global ensemble average by a spatially localized statistical one, retaining local heterogeneity that a bulk measurement would average away. Finally, we discuss the implementation and prospects of the large-volume data analysis—principal component analysis, Bayesian inference, machine learning, and autonomous measurement—needed to handle the explosively increasing amount of information that the time axis introduces. We close with the outlook that time-resolved measurement incorporating AI and big-data analysis will become established as a new measurement platform that complements and extends conventional static structural analysis. Full article

Coronary slow flow (CSF) is delayed distal contrast transit on angiography without flow-limiting stenosis; disturbed sleep may impair vascular control through autonomic, endothelial, inflammatory, and metabolic pathways. We evaluated Pittsburgh Sleep Quality Index (PSQI)-based sleep quality in relation to TIMI frame count (TFC)-defined CSF. This cross-sectional study enrolled 307 adults with nonobstructive coronary arteries undergoing angiography for chest pain; PSQI referenced the preceding month, and CSF was defined by corrected TFC > 27 frames in any major vessel; 132 participants had CSF and 175 normal flow. Global PSQI score was higher in CSF (median 7.0 vs. 5.0) and poor sleep quality (score > 5) was more frequent (75.8% vs. 49.7%; both p < 0.001). The global score correlated with mean TFC overall but not within flow groups. Each PSQI point independently raised CSF odds, including after STOP-Bang adjustment, although discrimination was modest. Sleep latency and short duration raised CSF odds; poor efficiency did not. Poorer sleep quality was independently linked to this angiographic slow-flow phenotype, mainly differentiating flow categories rather than tracking frame-count burden. Because coronary microvascular function was not measured directly, these hypothesis-generating findings should prompt systematic sleep assessment and prospective studies integrating objective sleep measures with invasive coronary physiology. Full article

Achieving high fault-classification accuracy alone does not guarantee reliable autonomous operation under varying operating conditions, raising the need to assess prediction reliability and deployment readiness. This work proposes a resilience-oriented framework for fault diagnosis under varying operating conditions, characterizing diagnostic behavior under operating-condition shifts and providing complementary information on confidence, deployability, and supervision requirements. The framework fuses multi-sensor vibration and motor current signals within a Multi-Stage architecture combining a data-driven branch (DD-MSCNN) and a physics-aware branch (PA-MSCNN) integrating order-tracking descriptors, augmented by a confidence-aware deployability assessment layer. Evaluated on the Paderborn KAT dataset across six bidirectional shifts involving speed, torque, and radial force, the results reveal that operating-condition shifts are not equivalent and that their impact is strongly direction-dependent. Physical knowledge does not systematically guarantee superior performance, highlighting the complementary roles of the two representations. To formalize these observations, the Physics Contribution Index (PCI), the Shift Directionality Index (SDI), and a four-level deployability classification are introduced, providing quantitative insights into prediction reliability and autonomous operation readiness in dynamic industrial environments. Full article

Autonomous commercial vehicles (ACVs) frequently encounter drastic variations in payload and complex road conditions during practical operations. Consequently, effectively suppressing external disturbances caused by payload and road slope uncertainties has become a critical challenge in enhancing the robustness of their low-level control systems. To address this issue, this paper proposes a sliding mode control (SMC) strategy based on Luenberger observer disturbance compensation (LOSM), aiming to simultaneously mitigate the adverse effects of these two uncertainties on the vehicle’s speed control performance. First, according to the driving characteristics of commercial vehicles, a full-condition longitudinal dynamic model encompassing uphill, downhill, and flat road scenarios is established. Second, by deeply integrating the Luenberger observer with sliding mode control theory, an active disturbance rejection LOSM speed controller is designed. Furthermore, the boundary conditions for the closed-loop system to achieve asymptotic stability are rigorously derived and proven using Lyapunov functions. Finally, to comprehensively verify the effectiveness of the proposed strategy, eight typical testing scenarios are constructed, and three benchmark algorithms—PI control, radial basis function adaptive sliding mode (RBFSM) control, and radial basis function backstepping sliding mode (RBFBSSM) control are introduced for comparative analysis. The validation results demonstrate that although all four methods can achieve speed tracking and suppress disturbances, the proposed LOSM strategy exhibits the optimal comprehensive performance across various scenarios. Specifically, its steady-state mean error is typically maintained below 2.5%, and it yields the minimum steady-state variance in the majority of scenarios. These results demonstrate that the designed LOSM method can significantly improve the precision and smoothness of ACVs’ speed control under the dual disturbances of unknown mass and road slope. Full article

To address the need for low-power, real-time underwater acoustic signal processing and autonomous target detection on deep-sea unmanned mobile platforms, such as profiling acoustic buoys and underwater gliders, this study developed an embedded Linux-based signal processing system for buoy platforms. Conventional digital signal processing hardware platforms are often constrained by large size, high power consumption, and limited data communication capability. The proposed system adopts a compact, low-power architecture and a multithreaded processing framework based on the AM6254 heterogeneous multicore processor. It acquires four-channel vector-hydrophone signals together with attitude data from an inertial navigation module and performs band-pass filtering, fast Fourier transform (FFT), direction-of-arrival (DOA) estimation, and constant false alarm rate (CFAR) detection for autonomous target detection. The measured typical power consumption was approximately 2.3 W. Anechoic-tank and sea-trial results showed the lowest tested spectral level at which autonomous detection was achieved was 54 dB at 1 kHz, corresponding to an average in-band level of 46 dB. Under sea state 3, the system maintained continuous bearing tracking after target acquisition for a surface target traveling at 7 kn, up to a range of approximately 7 km, and provided unambiguous bearing estimation. These results demonstrate the target-detection capability and practical applicability of the system under representative operating conditions and indicate its potential for marine environmental monitoring and unmanned-platform observation and detection. Full article

Urban expansion generates habitat fragmentation, light pollution, and vegetation loss, driving biodiversity decline globally. Insectivorous bats are particularly suited to track these changes: their taxonomic and functional diversity, combined with their differential sensitivity to landscape configuration and roost availability, makes them reliable bioindicators of urbanization impacts. This study characterized bat acoustic diversity and community composition along an urban–rural gradient in Tacna, Peru, using passive acoustic monitoring. Twenty-three stations equipped with autonomous ultrasonic recorders were established between December 2024 and April 2025. Sonotype identification was validated through linear discriminant analysis (DFA), achieving 94.47% accuracy by Leave-One-Out Cross-Validation (LOOCV). Nine sonotypes were identified, seven Molossidae and two Vespertilionidae; Myotis atacamensis showed the highest detection frequency across all districts. Diversity indices were highest in the peri-urban zone of Pocollay (S = 8; H′ = 1.664) and lowest in Gregorio Albarracín (S = 4; H′ = 0.964). The high dissimilarity between Pachía and the urban core of Tacna (BC = 0.817) points to marked compositional differences linked to urbanization intensity. Records of Nyctinomops laticaudatus and N. macrotis constitute the first documentation for the urban area of Tacna. These results suggest urban and peri-urban sectors may function as ecological oases for bat sonotypes within a hyperarid matrix, providing a baseline for evaluating the role of green spaces, landscape connectivity, and artificial lighting in arid cities. Full article

Accurate multi-object tracking and metric localization support traffic monitoring and cooperative intelligent transportation at complex intersections. This study presents a fixed-camera vision-map fusion framework that addresses two practical difficulties: axis-aligned boxes poorly represent turning vehicles, and unconstrained image-plane tracking can produce physically implausible trajectories. A map-aided frontend first generates candidate detections using improved You Only Look Once version 8 nano (YOLOv8n) horizontal bounding box (HBB) branch and an improved YOLOv8 oriented bounding box (OBB) branch. A high-definition (HD) map selector then retains the candidate geometry consistent with the straight-driving or turning region and converts it into a unified detection record. The selected reference point is projected to the ground plane through an offline-estimated homography, whereas the appearance feature bypasses the homography and is passed directly to the association stage. The tracking backend uses a 12-dimensional joint image/metric state, symmetric central-difference evaluations of the process and measurement functions, appearance-motion association, and a feasible-road projection derived from HD-map lane polygons. On the evaluated public sequences, the complete configuration achieved a multiple object tracking accuracy (MOTA) of 74.5%, an identification F1 score (IDF1) of 82.6%, 614 identity switches, and a throughput of 26.8 frames per second (FPS) on an RTX 4090 workstation. In a descriptive Vehicle-in-the-Loop case study involving one instrumented vehicle at one intersection, the overall localization mean absolute error (MAE) was 0.180 m, compared with 0.208 m for the baseline end-to-end configuration. These results indicate the feasibility of combining branch-specific vehicle geometry with map-constrained tracking; controlled same-detector comparisons, repeated multi-vehicle trials, and embedded-device latency and power profiling remain necessary for broader claims. Full article

This paper presents, to our knowledge, the first framework that jointly and provably optimizes communication and computation energy across an entire fleet of wirelessly connected mobile robots, rather than for a single device or under a fixed offloading policy as in prior work. Battery capacity limits the endurance of autonomous mobile robots, and on-board computation and radio communication increasingly rival locomotion in energy draw; across a fleet, the two are further coupled through shared uplink bandwidth and edge computing capacity. We formulate the joint selection of each robot’s task-offloading ratio, DVFS processor frequency, and transmit power, together with the fleet-wide allocation of bandwidth and edge capacity, subject to hard per-task deadlines. Closed-form inner solutions reduce each robot’s problem to a jointly convex program, coupled fleet-wide only through two linear resource constraints. We exploit this structure in JECCO-M, a distributed price-based algorithm that provably converges to the global fleet optimum while exchanging only a few scalars per iteration. A trajectory-conditioned channel-prediction extension handles robot mobility. Evaluated in simulations against optimization-based and learning-based baselines from the literature and on a physical three-robot testbed with embedded GPU compute, an IEEE 802.11ac uplink, and instrumented power rails, JECCO-M substantially reduces combined electronic energy while meeting all deadlines, and the measured hardware behavior tracks the analytical model closely. The results indicate that treating radio energy, processor energy, and shared edge resources as a single optimization domain is a practical route to extending the operating time of connected robot fleets. Full article

Autonomous field robots are increasingly used in precision agriculture for monitoring, phenotyping, spraying, and site-specific intervention, yet reliable autonomy remains difficult under vegetation occlusion, uneven terrain, variable illumination, and intermittent communications. This review provides a deployment-oriented synthesis of navigation and sensor-fusion methods for agricultural robots, with emphasis on what is practical under field conditions rather than only in laboratory settings. The literature was examined through a structured narrative-review workflow using Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and related citation tracking, with primary emphasis on studies published between 2015 and 2025. We compare global, local, and hybrid planning methods; motion-control strategies such as PID, Pure Pursuit, and MPC; and localization pipelines that combine GNSS, IMU, LiDAR, cameras, odometry, and SLAM or Kalman-family fusion. Beyond algorithm summaries, the review links method selection to agricultural deployment constraints, including GNSS degradation, dynamic obstacles, compute limits, ROS 2 integration, time synchronization, and coordinate-frame management. The synthesis shows that no single stack is optimal across all crop systems: lightweight GNSS/IMU-based solutions remain attractive in structured open fields, whereas orchards, vineyards, and other occluded environments benefit more from tighter multi-sensor fusion and SLAM-supported localization. Finally, the review distills design guidance for sensing, planning, validation, and digital-twin-supported testing, and identifies research gaps related to robustness, benchmarking, safety, and scalable on-farm deployment. Full article

This paper presents a universal, model-independent Adaptive PI (A-PI) control framework using multiple Recursive Least Squares (RLS) algorithms, primarily focused on the longitudinal velocity control of autonomous vehicles. To overcome the limitations of fixed-gain controllers, the proposed system self-tunes control parameters in real-time based on gradient descent and Lyapunov stability theories, requiring no complex vehicle dynamics. Particularly, to address the multivariable coupling effect during real-time estimation, a selective update rule is proposed, ensuring the theoretical validity of independent gain self-tuning. Furthermore, a novel error-based covariance scaling logic is introduced to dynamically and selectively update the proportional and integral scale factors across three distinct error areas. This mechanism ensures rapid initial convergence in the transient region and smooth settling without overshoot near the target. To evaluate the feasibility of the proposed universal and adaptive framework, longitudinal velocity tracking performance was analyzed through a MATLAB/Simulink version 2023b and CarMaker co-simulation environment. Simulation results demonstrate that the proposed A-PI controller significantly reduces the Root Mean Square (RMS) control error compared with conventional fixed PI controllers under dynamic scenarios, proving its robust adaptability and paving the way for future integrated longitudinal and lateral vehicle control. Full article

Photovoltaic (PV) pumping systems offer a sustainable alternative to diesel solutions, yet their nonlinearity, intermittent irradiation, and complex motor-pump dynamics challenge energy extraction and reliability. Currently, these systems predominantly rely on classical Maximum Power Point Tracking (MPPT) algorithms such as Perturb and Observe (P&O) and Incremental Conductance (INC), which suffer from slow convergence, steady-state oscillations, and an inability to track Global MPP (GMPP) under uniform irradiance variation conditions. Furthermore, existing studies typically address MPPT optimization and motor control in isolation, without considering their coupled interaction, and rarely incorporate economic viability assessments. To address these limitations, this paper proposes an innovative control architecture integrating four advanced metaheuristic MPPT techniques, namely the Genetic Algorithm (GA), Gray Wolf Optimizer (GWO), Cuckoo Search (CS) algorithm, and Horse Herd Optimization Algorithm (HOA), with Model Predictive Control (MPC) for a Brushless DC (BLDC) motor-driven pumping system, supplemented by battery storage. Comprehensive simulations were conducted under both constant and variable irradiance profiles (1000 to 500 to 1000 W/m²) to evaluate dynamic performance, tracking accuracy, and system robustness. The results demonstrate that HOA and GWO significantly outperform GA and CS, achieving superior DC bus voltage stability with ripple values below 2.4 V, faster convergence times, reduced electromagnetic torque oscillations, and enhanced MPPT efficiency exceeding 99%. Under variable irradiance, HOA exhibits the fastest stabilization with minimal overshoot and superior disturbance rejection, while GA suffers from severe oscillations and CS displays sawtooth ripple patterns. A techno-economic analysis further confirms the economic viability of the proposed system, with HOA and GWO strategies yielding lower lifecycle costs, extended converter lifespans from 5 to over 12 years, and improved return on investment compared to conventional approaches. This integrated framework offers a robust, efficient, and economically sustainable solution for autonomous PV water pumping applications. Full article

Developing autonomous navigation systems for small fishing vessels is required to improve path-tracking robustness and mitigate severe wave-induced roll motions. This study proposes a Deep Q-Network (DQN)-based operational path planning methodology that explicitly incorporates roll motion reduction into the reward function, combining a maneuvering model with hydrodynamic analyses. Simulation results under varying wave directions and heights demonstrate that the vessel actively adjusts its heading to minimize the roll response. Based on statistical evaluations across five independent runs, the proposed model effectively reduced the average and maximum roll responses by an average of 3% and 2%, respectively, under the evaluated wave headings at a wave height of 1.0 m, while maintaining operational path tracking, despite a slight increase in the total operational path length. Future research will focus on integrating complex environmental conditions with wind and current, and performing the model test for the validation of the established DQN model. Full article

ROVs are essential for marine exploration and underwater operations, yet conventional teleoperation relies heavily on skilled human operators, and many autonomous methods stop at high-level planning rather than low-level actuation, limiting robustness in disturbed and cluttered environments. This paper proposes ODARRL, an obstacle- and disturbance-aware sensor-to-thruster (ST) end-to-end residual reinforcement learning framework for safe trajectory execution of underwater robots. Using a three-stage curriculum, ODARRL first acquires a basic policy from MPC demonstrations in a static obstacle-free environment, then improves disturbance-robust tracking under random currents, and finally extends to scenarios involving both currents and obstacles. A Dual-Horizon Attention Disturbance Encoder is further designed to capture current-related features from long- and short-term histories, which are fused with robot states and reference information as the input to the ST end-to-end policy. Experiments in Marine Gym with BlueROV2 Heavy demonstrate that ODARRL achieves more stable and robust trajectory tracking under random currents, reducing the mean total tracking error by 69.3%, 31.9%, 45.8%, 73.0% and 25.8% relative to the MPC-imitation policy, PPO, SAC, A2C and VNRS-SAC, respectively. With obstacles introduced, curriculum-initialized policies also exhibit higher path progress and more stable task completion during obstacle-avoidance training. Full article

Global path planning for autonomous orchard robots must balance obstacle clearance, path smoothness, and trajectory trackability. This balance is difficult to achieve in unstructured orchards, where irregular tree rows, scattered trunks and ground obstacles, and narrow inter-row passages can cause conventional planners to generate low-clearance paths with frequent local turns. This study proposes SDF-Theta*, a safety- and smoothness-aware global path planning framework for orchard robots in unstructured environments. The framework constructs a Euclidean signed distance field (ESDF) from a two-dimensional occupancy planning map and defines a safe navigable domain using the robot width and a grid-discretization approximation margin. Within this domain, the bidirectional SDF-Theta* search performs candidate-node screening, applies Safe LOS checks along candidate connection segments, and uses safety–geometry multi-criteria state selection based on minimum clearance, mean clearance, path length, and turning cost. The resulting initial discrete path is processed through path skeleton refinement and local Bézier curve smoothing. Differential-flatness-based time parameterization then converts the smoothed geometric path into a time-indexed motion reference. In the Orchard Field Experiment, SDF-Theta* increased the minimum obstacle clearance by 16.6% compared with Theta* (ESDF) and achieved a safe path ratio of 100.00%. It also reduced the 99th-percentile curvature, maximum curvature, and total curvature variation by 54.3%, 85.5%, and 82.1%, respectively. Trajectory Tracking Validation yielded a path-overlap ratio of 94.65% at a nearest-path distance threshold of 0.03 m, with no physical collision or map-boundary violation. These results show that SDF-Theta* improved path safety and geometric smoothness and demonstrated trajectory trackability under the tested orchard conditions. Full article