Search Results (138)

In modern vehicles, Autonomous Driving Systems (ADSs) are designed to operate partially or fully without human intervention. The ADS pipeline comprises multiple layers, including sensors, perception, localization, mapping, path planning, and control. The Robot Operating System (ROS) is a widely adopted framework that supports the modular development and integration of these layers. Among them, the path-planning and control layers remain particularly challenging due to several limitations. Classical path planners often struggle with non-smooth trajectories and high computational demands. Meta-heuristic optimization algorithms have demonstrated strong theoretical potential in path planning; however, they are rarely implemented in real-time ROS-based systems due to integration challenges. Similarly, traditional PID controllers require manual tuning and are unable to adapt to system disturbances. This paper proposes a ROS-based ADS architecture composed of eight integrated nodes, designed to address these limitations. The path-planning node leverages a meta-heuristic optimization framework with a cost function that evaluates path feasibility using occupancy grids from the Hector SLAM and obstacle clusters detected through the DBSCAN algorithm. A dynamic goal-allocation strategy is introduced based on the LiDAR range and spatial boundaries to enhance planning flexibility. In the control layer, a modified Pure Pursuit algorithm is employed to translate target positions into velocity commands based on the drift angle. Additionally, an adaptive PID controller is tuned in real time using the Differential Evolution (DE) algorithm, ensuring robust speed regulation in the presence of external disturbances. The proposed system is practically validated on a four-wheel differential drive robot across six scenarios. Experimental results demonstrate that the proposed planner significantly outperforms state-of-the-art methods, ranking first in the Friedman test with a significance level less than 0.05, confirming the effectiveness of the proposed architecture. Full article

Objective: This study aimed to investigate the efficacy of far-infrared (FIR) garments in enhancing recovery following resistance exercise in recreationally active individuals. Methods: Ten recreationally active adults (six females, four males; aged 20.7 ± 3.2 years) completed a resistance exercise protocol and were randomly selected to wear either FIR (n = 5) or placebo (n = 5) tights post-exercise. The FIR garments incorporated Celliant-based fibers emitting wavelengths in the 2.5–20 µm range. The participants’ recovery was assessed using countermovement jump (CMJ) metrics, including their jump height, takeoff velocity, and modified reactive strength index (mRSI), along with their fatigue biomarkers and subjective recovery perceptions. The CMJ performance was tested immediately post-exercise and at 24 and 48 h. Results: The FIR garments led to significant improvements in neuromuscular recovery, with greater increases in the jump height, takeoff velocity, and mRSI observed at 48 h post-exercise (p < 0.05). Notably, the mRSI showed earlier improvements at 24 h. The fatigue biomarkers did not differ between the groups (p > 0.05), suggesting localized rather than systemic recovery effects. The participants in the FIR group reported faster subjective recovery, with a readiness to resume activity perceived within 48 h, compared to slower recovery in the placebo group. Conclusions: FIR garments may enhance neuromuscular recovery and subjective recovery perceptions following resistance exercise, likely by improving the peripheral blood flow, metabolic clearance, and tissue oxygenation. These findings suggest that FIR garments may be effective in enhancing both neuromuscular and perceived recovery following resistance exercise, supporting their potential use as a post-exercise recovery tool. Full article

Outdoor cleaning robots must operate reliably across diverse and unstructured surfaces, yet many existing systems lack the adaptability to handle terrain variability. This paper proposes a terrain-aware cleaning framework that dynamically adjusts robot behavior based on real-time surface classification and slope estimation. A 128-channel LiDAR sensor captures signal intensity images, which are processed by a ResNet-18 convolutional neural network to classify floor types as wood, smooth, or rough. Simultaneously, pitch angles from an onboard IMU detect terrain inclination. These inputs are transformed into fuzzy sets and evaluated using a Mamdani-type fuzzy inference system. The controller adjusts brush height, brush speed, and robot velocity through 81 rules derived from 48 structured cleaning experiments across varying terrain and slopes. Validation was conducted in low-light (night-time) conditions, leveraging LiDAR’s lighting-invariant capabilities. Field trials confirm that the robot responds effectively to environmental conditions, such as reducing speed on slopes or increasing brush pressure on rough surfaces. The integration of deep learning and fuzzy control enables safe, energy-efficient, and adaptive cleaning in complex outdoor environments. This work demonstrates the feasibility and real-world applicability for combining perception and inference-based control in terrain-adaptive robotic systems. Full article

Urban pluvial flooding (UPF) is an increasingly critical issue due to rapid urbanization and intensified precipitation driven by climate change that yet remains understudied in the Caribbean. This study analyzes the effects of UPF resulting from the transformation of a natural karstic landscape into an urbanized area considering a sub-watershed in Chetumal, Southern Mexican Caribbean, as a case study. Hydrographic numerical modeling was conducted using the IBER 2.5.1 software and the SCS-CN method to estimate surface runoff for a critical UPF event across three stages: (i) 1928—natural condition; (ii) 1998—semi-urbanized (78% coverage); and (iii) 2015—urbanized (88% coverage). Urbanization led to the orthogonalization of the drainage network, an increase in the sub-watershed area (20%) and mainstream length (33%), flow velocities rising 10–100 times, a 52% reduction in surface roughness, and a 32% decrease in the potential maximum soil retention before runoff occurs. In urbanized scenarios, 53.5% of flooded areas exceeded 0.5 m in depth, compared to 16.8% in non-urbanized conditions. Community-based knowledge supported flood extent estimates with 44.5% of respondents reporting floodwater levels exceeding 0.50 m, primarily in streets. Only 43.1% recalled past flood levels, indicating a loss of societal memory, although risk perception remained high among directly affected residents. The reported UPF effects perceived in the area mainly related to housing damage (30.2%), mobility disruption (25.5%), or health issues (12.9%). Although UPF events are frequent, insufficient drainage infrastructure, altered runoff patterns, and limited access to public shelters and communication increased vulnerability. Full article

The aim of this study was to investigate the accuracy and stability of both Perception of Velocity (PV) and Perception of Velocity Loss (PVL) over four weeks, without any feedback regarding velocity during training sessions. Fifteen subjects performed six training sessions: four sessions familiarized the athletes with PV and PVL and the final two sessions assessed the accuracy and stability of PV and PVL, with one conducted 48 h after the familiarization and the other after 4 weeks. To assess PV and PVL, two loads (60% 1RM and 80% 1RM) and two velocity losses (20% VL and 40% VL) were employed. PV accuracy was measured by the DeltaScore, the difference between perceived velocity (Vp) and the velocity provided by the encoder (Vr): DetlaScore = Vp − Vr. PVL was measured by the Vscore, the difference between repetitions where the subject perceived the target %VL (Np) and repetitions that actually met it (Nr): Vscore = Np − Nr. The analysis performed revealed no differences in DeltaScore nor in Vscore between the two test sessions performed 4 weeks apart (p > 0.05). On the other hand, the effect of load on both DeltaScore and Vscore was significant in both sessions (p < 0.05). PVL and PV accuracy are stable throughout time. PVL may be used to prescribe and monitor velocity-based training. Conversely, when prescribing training sessions based on PV, it is essential to pair PV with a perception scale and incorporate an encoder when possible. Full article

Urbanization and climate change present increasing challenges to outdoor human thermal comfort, particularly in university campuses where academic, social, and recreational activities converge. This study assesses microclimatic risk factors along the main avenue of the NOVA FCT campus by analyzing outdoor human thermal comfort using the physiologically equivalent temperature (PET) and modified PET (mPET) indices. Field measurements of air temperature, humidity, wind velocity, and radiation were conducted at multiple Points Of Interest (POIs) to evaluate thermal stress levels and identify critical zones of discomfort. Results indicate significant spatial and temporal variations in thermal stress, with sun-exposed areas (G2) experiencing PET values exceeding 50 °C, during peak summer hours, while shaded locations (G1) showed substantial thermal relief (PET reductions up to 27 °C between G1 and G2 POIs). Wind velocity and urban morphology played crucial roles in modulating microclimatic conditions. Wind velocity above 2.0 m/s was associated with perceptible thermal relief (3–8 °C PET/mPET reduction), especially in narrow, shaded passages. Significant spatial variability was observed, linked to differences in urban morphology, surface materials, and vegetation coverage. This research provides actionable insights for urban planners and campus administrators, contributing to the development of more sustainable and thermally comfortable outdoor environments in educational settings. Full article

Cross-Track Interferometric Synthetic Aperture Radar (XTI-SAR) can utilize variations in interferometric phase to measure sea surface velocity along radar radial direction and sea surface height, which can be used for ocean wave parameter inversion. However, research on the imaging mechanisms of XTI-SAR systems for ocean waves remains understudied, and there are still some problems in its perception. To further study the imaging mechanism of XTI-SAR measurement systems for ocean waves, this paper describes research based on the nonlinear integral transform model and the quasi-linear integral transform model derived by Bao in 1999, which relate the XTI-SAR ocean wave spectrum to the phase spectrum. Firstly, this work derived another quasi-linear integral transform model based on the nonlinear integral transform model, and also optimized the quasi-linear integral transform model derived by Bao. The optimized quasi-linear integral transform model eliminates the need for complex calculations of cross-correlation functions between sea surface height and radar radial orbital velocity components of ocean waves, as well as the radar line-of-sight velocity transfer function, while maintaining high integral transform accuracy. Secondly, based on two-dimensional sea surface simulations, we analyzed the differences between the quasi-linear integral transform models and the nonlinear integral transform model corresponding to different XTI-SAR system configurations and different sea states. The numerical simulation results show that, for the XTI-SAR system, in general, the difference between the quasi-linear integral transform model derived in this work and the nonlinear integral transform model is greater than that of the quasi-linear integral transform model derived by Bao. However, the difference between the optimized quasi-linear integral transform model and the nonlinear integral transform model in this study is smaller, and it is more convenient when transforming the ocean wave spectrum to the phase spectrum. Full article

In this paper, we study the stability of the stochastically heterogeneous traffic flow involving connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Taking the stochasticity of vehicle arrivals and behaviors into account, a general robust $H_{\infty }$ platoon controller is proposed to address the communication delay and unexpected disturbances such as prediction or perception errors on HDV motions. To simplify the problem complexity from a stochastically heterogeneous traffic flow to multiple long vehicle control problems, three types of sub-platoons are identified according to the CAV arrivals, and each sub-platoon can be treated as a long vehicle. The car-following behaviors of HDVs and CAVs are simulated using the optimal velocity model (OVM) and the cooperative adaptive cruise control (CACC) system, respectively. Later, the robust $H_{\infty }$ platoon controller is designed for a pair of a CAV long vehicle and an HDV long vehicle. The time-lagged system and the closed-loop system are formulated and the $H_{\infty }$ state feedback controller is designed. The robust stability and string stability of the heterogeneous platoon system are analyzed using the $H_{\infty }$ norm of the closed-loop transfer function and the time-lagged bounded real lemma, respectively. Simulation experiments are conducted considering various settings of platoon sizes, communication delays, disturbances, and CAV penetration rates. The results show that the proposed $H_{\infty }$ controller is robust and effective in stabilizing disturbances in the stochastically heterogeneous traffic flow and is scalable to arbitrary sub-platoons in various CAV penetration rates in the heterogeneous traffic flow of road vehicles. The advantages of the proposed method in stabilizing heterogeneous traffic flow are verified in comparison with a typical car-following model and the linear quadratic regulator. Full article

Unmanned agricultural machinery can significantly reduce labor intensity while substantially enhancing operational efficiency and production benefits. However, the presence of various obstacles in complex farmland environments is inevitable. Accurate and efficient obstacle recognition technology, along with a reliable safety warning system, is a crucial prerequisite for ensuring the safe and stable operation of unmanned agricultural machinery. This study proposes a lightweight model for farmland obstacle detection by improving the YOLOv8n object detection algorithm. Specifically, we introduce the Context-Guided Block (CG Block) in the C2f module and the Context-Guide Fusion Module (CGFM) in the Feature Pyramid Network (FPN) to enhance the model’s contextual information perception during feature extraction and fusion. Additionally, we employ a Lightweight Shared Convolutional Separable Batch Normalization Detection Head in the detection head, which significantly reduces the number of parameters while improving detection accuracy. Experimental results demonstrate that our method achieves a mean average precision (mAP) of 92.3% at 59.1 frames per second (FPS). The improved model reduces parameter count and computational complexity by 31.9% and 33.4%, respectively, with a model size of only 4.2 MB. Compared to other algorithms, the proposed model maintains an optimal balance between parameter efficiency, computational cost, detection speed, and accuracy, exhibiting distinct advantages. Furthermore, we propose a safety warning strategy based on the relative velocity and distance between obstacles and the unmanned agricultural machinery. Field experiments conducted under this strategy reveal an overall warning accuracy of up to 86%, verifying the reliability of the safety warning system. This ensures that unmanned agricultural machinery can effectively mitigate potential safety risks during field operations. Full article

Methodologies to measure motion perception are vital for deepening our understanding of the vision system and the factors that influence it. While existing work has primarily focused on the fastest perceivable velocities, less attention has been paid to the lower threshold of motion (LTM; slowest perceivable velocities). In this study, we designed an optical system to measure LTM in a sample of healthy young adults and to assess the influence of retinal location (central vs. peripheral retina) and stimulus composition (broadband vs. mid-wave) on LTM. The system was based on a xenon light source and a fiber-optic cable that created a bright light stimulus that could be moved along a computer-controlled precision translation slide. The stimulus, exposed for one-second intervals at both a central (fovea) and a peripheral (33 deg) location, was moved at varying speeds to determine the slowest detectable speed. In all, 37 healthy young participants (M = 19.32 ± 1.97 years) were tested. We found substantial between-subject variability in LTM and an interaction between stimulus wavelength and retinal location. The measurement of LTM using this novel apparatus and methodology provides insights into the relationship between slow-moving, ecologically valid stimuli and perceptual detection at the slowest speeds. Full article

This study proposes an advanced intelligent vehicle path-following control system using deep reinforcement learning, with a particular focus on the role of road geometry perception in motion planning and control. The system is structured around a three-degree-of-freedom (3-DOF) vehicle model, which facilitates the extraction of critical dynamic features necessary for robust control. The longitudinal control architecture integrates a Deep Deterministic Policy Gradient (DDPG) agent to optimise longitudinal velocity and acceleration, while lateral vehicle control is handled by a Deep Q-Network (DQN). To enhance situational awareness and adaptability, the system incorporates key input variables, including ego vehicle speed, speed error, lateral deviation, lateral error, and safety distance to the preceding vehicle, all in the context of road geometry and vehicle dynamics. In addition, the influence of road curvature is embedded into the control framework through perceived acceleration (sensed by vehicle occupants), allowing for more accurate and responsive adaptation to varying road conditions. The vehicle control system is tested in a simulated environment with a lead car in front with realistic speed profiles. The system outputs continuous values for acceleration and steering angle. The results of this study suggest that the proposed intelligent control system not only improves driver assistance but also has potential applications in autonomous driving. This framework contributes to the development of more autonomous, efficient, safety-aware, and comfortable vehicle control systems. Full article

Aiming at the problem of reduced positioning accuracy of unmanned swarm navigation systems due to dynamic abrupt noise in a complex electromagnetic environment, this paper proposes an adaptive Kalman filtering positioning and calibration method based on dynamic mutation perception and collaborative correction. This method optimizes the performance of Kalman filtering by monitoring the mutation of acceleration and velocity in real time, designing a dynamic threshold detection mechanism, adaptively adjusting the covariance matrix, and using multidimensional scaling analysis to calculate the similarity of trajectories and collaboratively correct the current state. The experiment uses simulation and real scene data and compares algorithms such as the traditional extended Kalman filter to verify the effectiveness of the proposed method, providing an effective solution for the collaborative positioning of an unmanned swarm under complex electromagnetic interference. Full article

This paper presents the design and implementation of the motion controller and adaptive interface for the second generation of the UCF-MANUS intelligent assistive robotic manipulator system. Based on extensive user studies of the system, several features were implemented in the interface that could reduce the complexity of the human–robot interaction while also compensating for the deficits in different human factors, such as working memory, response inhibition, processing speed, depth perception, spatial awareness, and contrast sensitivity. To effectively and safely control the robotic arm, we designed several new features, including an adaptive human–robot interaction framework. To provide the user with a less complex and safer interaction with the robot, we added new functionalities such as ‘One-click mode’, ‘Move suggestion mode’, and ‘Gripper Control Assistant’. Furthermore, to equip our assistive robotic system with an adaptive User Interface, we designed and implemented compensators such as ‘Contrast Enhancement’, ‘Object Proximity Velocity Reduction’, and ‘Orientation Indicator’. Results from a multitude of experiments show that the system is indeed robust, safe, and computationally efficient in addition to addressing the user’s highly desired capabilities. Full article

As an anti-predation behavior, shoaling enhances survival among prey species by reducing individual predation risk through mechanisms like the dilution effect and collective vigilance. Zebrafish—a highly social and genetically tractable species—are valuable for studying these behaviors. The present study examined zebrafish’s social preferences in a 3-chamber open-tank free-swim task, assessing whether visual cues alone could distinguish between an intact and an alarmed shoal exposed to the synthetic alarm substance H3NO. Subjects were allowed to freely associate with either shoal while their behaviors were recorded and analyzed. The results reveal a significant preference for proximity to the intact shoal, indicating zebrafish’s ability to visually discern threat levels. Subjects spent nearly twice as much time in the zone near the intact shoal, with reduced freezing and faster movement velocities compared to the alarmed shoal zone. Males exhibited more freezing behavior than females, consistent with sex-specific strategies in threat response. These findings underscore zebrafish’s reliance on visual cues for social responding under predatory threat and highlight sex-based differences in threat perception. This research expands the understanding of zebrafish’s social dynamics and provides a robust framework for future exploration of the neural mechanisms underlying social behavior and threat assessment in zebrafish. Full article

The user’s experience is critical in spatial design, particularly in outdoor spaces like university campuses, where the physical environment significantly influences students’ relaxation and stress relief. This study investigates the combined impact of thermal, luminous, and auditory environments on students’ perceptions within recreational areas at Bordj Bou Arreridj University Campus. A mixed-method approach combined field surveys and on-site measurements across eleven locations within three distinct spatial configurations. The findings from this study indicate that the auditory environment had the most substantial influence on overall perceptions, surpassing luminous and thermal factors. The open courtyard (Area 1) was perceived as less comfortable due to excessive heat and noise exposure. The shaded zone (Area 2) was identified as the most vulnerable, experiencing significant thermal stress and noise disturbances. In contrast, the secluded patio (Area 3) achieved the highest comfort rating and was perceived as the most cheerful and suitable space. Correlation analysis revealed significant interrelationships between physical and perceptual dimensions, highlighting the critical role of factors such as wind velocity, sky view factor, and illuminance in shaping thermal, luminous, and acoustic perceptions. A fuzzy logic model was developed to predict user perceptions of comfort, suitability, and mood based on measured environmental parameters to address the complexity of multisensory interactions. This study highlights the importance of integrating multisensory evaluations into spatial design to optimize the quality of outdoor environments. Full article