Motion Scenarios Research Articles

Energy optimal path planning for wheeled mobile robots with circular obstacle

AbstractThe energy optimal motion planning algorithm for a differential drive robot with a circular obstacle in the maneuver space is introduced in this article. Rather than the traditional unicycle model, a non‐canonical state space model of the kinematics of a wheeled mobile robot is used to formulate the optimal control problem. The necessary conditions for optimality are formally derived using calculus of variations, resulting in constraints for the costates when the inequality constraint become active, revealing that the controls are required to be continuous. The optimality conditions permit decomposing the motion planning problem in two independent segments: prior to and after activation of the inequality constraints. Analytical expressions for the evolving states and control are found to be a function of Jacobi elliptic functions, permitting reducing the optimal control problem to a root‐finding problem. A comprehensive parametric study is included to demonstrate the impact on the optimal trajectories by varying the radius of the obstacle. The study shows that there are three distinct maneuver strategies with respect to the size of the obstacle. Furthermore, by including entering and exit time as optimization variables, a methodology for generalizing the development to any given motion scenario is developed and numerically illustrated.

Enhanced neural video compression for cloud gaming videos with aligned frame generation

The burgeoning popularity of cloud gaming makes it critical for efficient video compression to relieve the growing bandwidth pressure. While existing neural video coding approaches have demonstrated strong compression potential on natural videos, there is an absence of efficient neural codecs dedicated to gaming videos. To bridge this gap, in this paper, we propose an end-to-end neural video compression method designed specifically for cloud gaming videos. By effectively utilizing the unique camera motion information inherent to cloud gaming, the previous reconstructed frame is maximally aligned to the current frame through a learning-based module with multiple losses, which then replaces the previous reconstructed frame for optical flow estimation. By significantly reducing the displacement between two consecutive frames caused by camera motion, the motion estimation accuracy is enhanced, effectively handling the large and abrupt motion scenarios frequently present in gaming videos. Furthermore, the aligned tensor obtained in the previous step is used to enhance the latent prior of the entropy model, providing a superior temporal prior for coding. Extensive experimental results demonstrate the superior performance of our proposed method compared to one of the previous state-of-the-art approaches, DCVC-HEM, providing significant progress in end-to-end neural compression in cloud gaming videos.

Hydrogen bonds-pinned entanglement double network alginate hydrogel for electrical application

In response to prevailing challenges encountered in electrical applications, including insufficient mechanical strength, subpar tensile properties, and limited adaptability to dynamic motion environments, we engineered a pioneering hydrogel adhesive. Simultaneously, we presented a novel interpretation of the application of ZnO in hydrogels. Our innovative approach entailed the intertwining of polyvinyl alcohol (PVA) and flexible sodium alginate (SA) double networks (DN) through cross-linking mechanisms, resulting in the formation of a hydrogen-bonding pinned DN hydrogel. This groundbreaking design substantially amplified the cohesive and adhesive properties of the hydrogel, while the incorporation of zinc oxide (ZnO) through modification served to enhance its electrical conductivity. Our hydrogel sensor demonstrated exceptional capabilities in monitoring human motion, adeptly meeting the demands of diverse motion scenarios. Furthermore, meticulous consideration had been given to the influence of perspiration on sensor performance, rendering our sensor exceptionally well-suited for real-world applications.

Finite-time trajectory tracking control for unmanned ground vehicle based on finite-time disturbance observer

This paper studies the trajectory tracking problems of unmanned ground vehicle with model uncertainties and external disturbances. We propose a finite-time control method, in which the uncertainty and disturbance compensating is considered. To match the real motion scenarios of vehicles, the Frenet-coordinate system is used and the error dynamics equation is constructed. A new type of coordinate transformation is adopted to simplify the calculation of finite-time stability. Further, we design a new finite-time disturbance observer to deal with the model uncertainties and external disturbances. Based on this observer, the finite-time trajectory tracking controller is constructed by using a new integral-type Lyapunov function. It is theoretically proved that all tracking errors in the closed-loop system are finite-time stable. To verify the feasibility, simulation on the unmanned ground vehicle has been conducted.

Biomechanical Effects of Titanium Alloy Based Single versus Dual Cage Fusion Devices

Degenerative disc disease is an increasing problematic complication following lumbar fusion surgeries. Posterior lumbar interbody fusion (PLIF) is a well-established surgical method for spine stability following intervertebral disc removal. The position and number of titanium cages in PLIF are remain contingent on individual surgeon experience. Thus, a systemic investigation of the efficacy of titanium single mega cage versus two cages in treating degenerative lumbar spinal diseases is imperative. A biomechanical study was aimed to compare the stability achieved in PLIF through interbody reconstruction using a single mega cage (32 mm) Vs. a dual cage (22 mm). Normal intact finite element model of L3–L4 was developed based on computed tomography images from a healthy 27-year-old male volunteer. The study tested the intact model (Model A) and its surgically operated counterparts using four PLIF implantation methods: single transverse cage (Model B), single transverse cage with bone graft (Model C), dual transverse cage (Model D), and dual transverse cage with bone graft (Model E). Combined loads simulating physiological motions—flexion, extension, axial rotation, and lateral bending —were applied across all loading directions. The assessment includes all model range of motion (ROM), micromotion between the cage and endplate, and stress on the cage and internal fixation system (screw and rod). The ROM between Models B, C, D and E were consistently reduced by over 71% compared to intact Model A under all motion scenarios. Model D exhibited the highest peak stress of 115 MPa on the cage during flexion, surpassing Model C and E (Flexion) by fourfold. Model E demonstrated the lowest cage stress (20 MPa) during extension, outperforming the other models. Notably, Model E exhibited minimal endplate stress (2 MPa), cage stress (21 MPa), micromotion (13 µm) during extension, and screw-rod stress (56 MPa) during flexion, making it superior to other implantation methods. In the context of PLIF, Model E showed enhanced biomechanical stability, reducing ROM, stress on the endplates, cage, screw-rod system and micromotion. Alternatively, Model C may be a viable alternative in standard PLIF, especially in cases with limited intervertebral space, providing efficient clinical outcomes with shorter operative times and reduced costs and ease of implantation. Also, this computational study provides valuable understandings into optimizing cage implantation strategies for improved outcomes during PLIF.

Evaluation of Load on Cervical Disc Prosthesis by Imposing Complex Motion: Multiplanar Motion and Combined Rotational-Translational Motion.

(1) Background: The kinematic characteristics of disc prosthesis undergoing complex motion are not well understood. Therefore, examining complex motion may provide an improved understanding of the post-operative behavior of spinal implants. (2) Methods: The aim of this study was to develop kinematic tests that simulate multiplanar motion and combined rotational-translational motion in a disc prosthesis. In this context, five generic zirconia-toughened alumina (BIOLOX®delta, CeramTec, Germany) ball and socket samples were tested in a 6 DOF spine simulator under displacement control with an axial compressive force of 100 N in five motion modes: (1) flexion-extension (FE = ± 7.5°), (2) lateral bending (LB = ± 6°), (3) combined FE-LB (4) combined FE and anteroposterior translation (AP = 3 mm), and (5) combined LB and lateral motion (3 mm). For combined rotational-translational motion, two scenarios were analyzed: excessive translational movement after sample rotation (scenario 1) and excessive translational movement during rotation (scenario 2). (3) Results: For combined FE-LB, the resultant forces and moments were higher compared to the unidirectional motion modes. For combined rotational-translational motion (scenario 1), subluxation occurred at FE = 7.5° with an incremental increase in AP translation = 1.49 ± 0.18 mm, and LB = 6° with an incremental increase of lateral translation = 2.22 ± 0.16 mm. At the subluxation point, the incremental increase in AP force and lateral force were 30.4 ± 3.14 N and 40.8 ± 2.56 N in FE and LB, respectively, compared to the forces at the same angles during unidirectional motion. For scenario 2, subluxation occurred at FE = 4.93° with an incremental increase in AP translation = 1.75 mm, and LB = 4.52° with an incremental increase in lateral translation = 1.99 mm. At the subluxation point, the incremental increase in AP force and lateral force were 39.17 N and 38.94 N in FE and LB, respectively, compared to the forces in the same angles during the unidirectional motion. (4) Conclusions: The new test protocols improved the understanding of in vivo-like behavior from in vitro testing. Simultaneous translation-rotation motion was shown to provoke subluxation at lower motion extents. Following further validation of the proposed complex motion testing, these new methods can be applied future development and characterization of spinal motion-preserving implants.

Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network

This paper introduces a novel approach to fault detection in the servo motor bearings of industrial robots within the context of Industry 4.0 prognostics and health management. The proposed solution leverages the innovative feature aggregation network for robotic fault detection in the application of smart factory. Overcoming challenges associated with traditional techniques that include handcrafted features, transfer learning, and deep learning models, the proposed approach offers a hierarchical information aggregation mechanism. The model is customized through hyperparameter tuning, resulting in a streamlined architecture with significantly fewer parameters. This parameter efficiency is notably distinct when compared to off-the-shelf transfer learning models that commonly feature extensive parameter counts in the range of hundreds of thousands or millions. The proposed model subjected to rigorous validation across diverse experimental scenarios that affirm its adaptability and robust performance. The model showcases accuracy in fault detection under both simple and welding motion scenarios, while its generalization capabilities are demonstrated as it successfully predicts health states in welding motion, showcasing versatility and reliability across various operational scenarios.

Animated VR and 360-degree VR to assess and train team sports decision-making: a scoping review.

In team sports, athletes' ability to make quick decisions plays a crucial role. Decision-making proficiency relies on the intricate balance of athletes' perceptual and cognitive abilities, enabling them to assess the competitive environment swiftly and select the most appropriate actions from various options. Virtual reality (VR) technology is emerging as a valuable tool for evaluating and refining athletes' decision-making skills. This study systematically examined the integration of VR technology into decision-making processes in team sports, aiming to identify more effective methods for presenting and interacting with virtual decision-making systems, thus enhancing the evaluation and refinement of athletes' decision making abilities. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, a thorough search of respected research databases, including Web of Science, PubMed, SPORTDiscus, ScienceDirect, PsycINFO, and IEEE, was conducted using carefully selected keywords. Twenty research papers meeting predefined inclusion criteria were included after careful evaluation. These papers were systematically analyzed to delineate the attributes of virtual decision-making task environments, the interactive dynamics inherent in motor decision-making tasks, and the significant findings. This review indicate that (1) the effectiveness of VR technology in assessing and improving athletes' decision-making skills in team sports; (2) the construction of virtual environments using the Head-Mounted Display (HMD) system, characterized by enhanced ease and efficiency; (3) the potential for future investigations to explore computer simulations to create more expansive virtual motion scenarios, thus efficiently generating substantial task scenario material, diverging from the constraints posed by 360-degree panoramic videos; and (4) the integration of motion capture technology for identifying and monitoring athletes' decision-making behaviors, which not only enhances ecological validity but also augments the transfer validity of virtual sports decision-making systems. Future research endeavors could explore integrating eye-tracking technology with virtual reality to gain insights into the intrinsic cognitive-action associations exhibited by athletes.

High-Resolution Two-Phase Velocimetry Of Aspherical Particles Using Wavelet-Based Optical Flow Velocimetry (WOFV) Benchmarked With Fully Resolved Direct Numerical Simulations

Several techniques exist to measure the carrier and dispersed phases in multi-phase flows. Particle image velocimetry (PIV) analyzes the carrier phase by cross-correlating particle images, but its resolution is limited by the size of the interrogation window, making it difficult to resolve fine-scale turbulent structures. Particle tracking algorithms capture translational motion of dispersed particles well, but struggle with rotational motion, especially for irregular and aspherical particles. Currently, there is no method that universally measures both the carrier phase velocity field and the translational and rotational motions of dispersed particles. This study evaluates wavelet-based optical flow velocimetry (wOFV) for motion estimation in multi-phase flows, focusing on dispersed ellipsoidal particles and their surrounding turbulent carrier flow using synthetic image data. The research proceeds in two phases: first, the rigid motion of ellipses, including translational and rotational components, is analyzed using wOFV-generated dense motion fields. The results highlight the critical role of the regularization weighting parameter λ. Higher λ values improve translational motion estimation, while an optimal λ avoids under-regularization and non-physical structures in rotational motion. wOFV maintains accuracy in combined motion scenarios with optimal λ values. In the second phase, wOFV captures the turbulent carrier flow around an aspherical particle, benchmarked against DNS data and compared to PIV. wOFV outperforms PIV in resolving finer structures near the particle surface and in accurately representing the wake region. Error analyses confirm wOFV’s superior performance, with optimal results within a specific λ range. In conclusion, wOFV is a highly effective tool for the analysis of multi-phase flow dynamics, providing greater resolution and accuracy than PIV, especially in complex scenarios involving aspherical particles.

Adaptive plane orthogonal to skin-tone vector for non-contact pulse rate monitoring in motion scenarios

Adaptive plane orthogonal to skin-tone vector for non-contact pulse rate monitoring in motion scenarios

Seismic vulnerability analysis of single-layer lattice shells with different rise-to-span ratios: Focusing on the selection of Sa(T1)-based intensity measures

This study conducted a detailed examination of seismic vulnerability across different limit states for Schwedler single-layer lattice shells with differing rise-to-span ratios. A comparative analysis of seismic intensity measures, particularly those centered around Sa(T1), was performed to evaluate their effectiveness in vulnerability analysis. To comprehensively address the inherent uncertainties in seismic actions, the study selected a dual set of ground motion scenarios, encompassing both far-field and pulse-type near-field events. A novel approach was employed, integrating a multi-indicator seismic performance assessment model based on the residual seismic safety framework, aiming to thoroughly quantify the cumulative seismic damage to lattice structures. Utilizing the multiple stripes analysis technique enabled the efficient generation of analytical results for vulnerability assessments at various damage thresholds. The findings revealed that lattice shells with larger rise-to-span ratios exhibit increased seismic robustness, notably with far-field seismic events causing more significant damage than their pulse-type near-field counterparts. Furthermore, the study determined that employing seismic intensity measures which consider the effects of higher modes significantly reduces variability in the vulnerability analysis when dealing with pulse-type near-field seismic activity. As a result, the use of the seismic intensity measure IM123 is recommended for assessing the vulnerability of lattice shells with varying rise-to-span ratios, under both far-field and near-field seismic conditions.

Safe Trajectory Planning for Incremental Robots Based on a Spatiotemporal Variable-Step-Size A* Algorithm.

In this paper, a planning method based on the spatiotemporal variable-step-size A* algorithm is proposed to address the problem of safe trajectory planning for incremental, wheeled, mobile robots in complex motion scenarios with multiple robots. After constructing the known conditions, the spatiotemporal variable-step-size A* algorithm is first used to perform a collision-avoiding initial spatiotemporal trajectory search, and a variable time step is utilized to ensure that the robot completes the search at the target speed. Subsequently, the trajectory is instantiated using B-spline curves in a numerical optimization considering constraints to generate the final smooth trajectory. The results of simulation tests in a field-shaped, complex, dynamic scenario show that the proposed trajectory planning method is more applicable, and the results indicate higher efficiency compared to the traditional method in the incremental robot trajectory planning problem.

Research on lateral stability control strategy for distributed drive electric vehicles considering driving style

To improve the handling stability of distributed drive electric vehicle (DDEV), a lateral stability control strategy considering driving style is proposed. Firstly, a driving style recognition model based on support vector machines (SVM) was constructed to identify driving styles in different lateral motion scenarios. Then, a layered architecture is used to design a driver/Active Front Steering (AFS)/Direct Yaw Control (DYC) stability coordination control strategy. The upper layer is based on the phase plane of the sideslip angle and extension control theory, dividing the vehicle operating range into classical, extension, and non domains. In the middle level, three control strategies are designed for different work domains. In the classical domain, a driver/AFS coordinated control strategy based on non-cooperative Nash games is constructed, and the weight coefficients in the AFS system cost function are modified based on driving characteristics; Construct a coordinated control strategy for AFS/DYC systems based on cooperative Pareto games in the extension domain; In non-domain, construct a direct yaw torque control strategy based on twin-delayed deep deterministic policy gradient (TD3) reinforcement learning. The lower layer aims to minimize tire utilization and optimizes the allocation of additional yaw moment. Finally, by establishing Hardware in the Loop (HIL) experiment platform, the effectiveness of the proposed control strategy is verified in lane changing scenarios of different styles of drivers. Compared to DYC stability control based solely on TD3, the maximum yaw rate and standard deviation of the lateral stability control strategy considering driving style reduce by 2.4 % and 10.82 %, respectively, and the maximum and standard deviation of the sideslip angle reduce by 29.29 % and 39.24 %, respectively, thereby improving the vehicle's handling stability.

TKO‐SLAM: Visual SLAM algorithm based on time‐delay feature regression and keyframe pose optimization

AbstractThis paper addresses the challenge of generating clear image frames and minimizing the loss of keyframes by a robot engaging in rapid large viewing angle motion. These issues often lead to detrimental consequences such as trajectory drifting and loss during the construction of curved motion trajectories. To tackle this, we proposed a novel visual simultaneous localization and mapping (SLAM) algorithm, TKO‐SLAM, which is based on time‐delay feature regression and keyframe position optimization. TKO‐SLAM uses a multiscale recurrent neural network to rectify object deformation and image motion smear. This network effectively repairs the time‐delay image features caused by the rapid movement of the robot, thereby enhancing visual clarity. Simultaneously, inspired by the keyframe selection strategy of the ORB‐SLAM3 algorithm, we introduced a grayscale motion‐based image processing method to supplement keyframes that may be omitted due to the robot's rapid large viewing angle motion. To further refine the algorithm, the time‐delay feature regression image keyframes and adjacent secondary keyframes were used as dual measurement constraints to optimize camera poses and restore robot trajectories. The results of experiments on the benchmark RGB‐D data set TUM and real‐world scenarios show that TKO‐SLAM algorithm achieves more than 10% better localization accuracy than the PKS‐SLAM algorithm in the rapid large viewing angle motion scenario, and has advantages over the SOTA algorithms.

Unraveling the kinematics of IZw18: A detailed study of ionized gas with MEGARA/GTC

This study offers an in-depth analysis of the kinematic behavior of ionized gas in IZw18, a galaxy notable for its extremely low metallicity and close proximity, utilizing data from MEGARA/GTC. We explored the structure and dynamics of the galaxy through Halpha line profiles, applying single and double Gaussian component fittings to create detailed maps of the luminosity, velocity, and velocity dispersion in the main body (MB) and halo regions. Additionally, we retrieved integrated spectra from various galactic regions to achieve a higher signal-to-noise ratio. In the MB, a rotational pattern is evident, yet a secondary, more complex kinematic pattern emerges from the double-component fitting, which is further enriched by the identification of a very broad component. Distinguished by a full width at half maximum of nearly 2000 km/s and a wide spatial extension, this component suggests a high-energy outflow and points toward large-scale, nonlocalized sources of high kinetic energy. Additionally, the observed significant velocity differences between the narrow and very broad components imply that these gases may occupy distinct spatial regions. This is potentially explained by high-density gas near the origin of the kinematic input, acting as a "wall" that reflects back the momentum of the gas. Regarding the halos, while the NE halo exhibits a tranquil state with low velocity dispersions, the SW halo presents higher velocities and more complex kinematics, indicative of diverse dynamic interactions. The identification of the very broad component across the MB and the high kinematic complexity in all regions of the galaxy points toward a scenario of widespread and subtle turbulent motion. This nuanced understanding of the kinematic behavior in IZw18, including the interplay of different gas components and the influence of internal structures, enhances our comprehension of the dynamics in blue compact dwarf galaxies. It may provide critical insights into early galaxy formation and the intricate kinematics characteristic of such environments.

Estimating event probabilities via signal temporal logic and first occurrence distributions

Abstract Estimating the probability of events is a significant challenge in many fields, often requiring a probabilistic model or additional labels and tasks for accurate prediction. However, those methods have limited scalability or unnecessary computational resource consumption due to predicting unrelated values. To address these issues, we propose a novel approach that estimates event probabilities based on the distributions of their first occurrence in the time domain. By using Signal Temporal Logic formulas to describe events and applying an algorithm that estimates complex events’ probabilities through simple event occurrence distributions, this study presents an efficient approach that does not depend on high-precision prediction. We evaluate the performance of our method on simulated scenarios of unmanned aerial vehicle motion and autonomous driving.

Modified Overset Method in OpenFOAM for Simultaneous Motion and Deformation: A Case Study of a Flexible Flapping Foil

The influence of wing deformation in animal propulsion and motion is an intriguing phenomenon that has contributed to a growing interest in biomimetics in both academia and industry. The study focuses on developing a robust numerical tool for analyzing the motion of biological systems in fluid flows, crucial to understanding fluid-structure interaction (FSI) phenomena. The research introduces a new mesh deformation solver, dynamicOversetZoneFvMesh, designed to address limitations in OpenFOAM's conventional Overset method. This solver enables the application of both rigid body motion and deformation at specified patches within certain zones, allowing for more complex motion scenarios. Through meticulous verification, parallelization, validation, and mesh convergence studies, the modified Overset solver's reliability is confirmed. Two cases are examined: a rigid-body flapping foil and a flexible foil with leading-edge deformation. Results demonstrate the adapted solver's proficiency in managing complex fluid dynamics and accommodating simultaneous motion and deformation. Furthermore, the study reveals that leading-edge flexibility improves the power extraction efficiency of flapping foils, aligning with previous literature. Overall, the research lays the foundation for advancing FSI simulations and opens avenues for addressing previously challenging problems in the field.

Heat transfer analysis for magnetohydrodynamic peristalsis of Reiner–Philippoff fluid: Application of an artificial neural network

Present communication explores a novel application of the computational intelligence technique, namely, the Levenberg–Marquardt scheme under a Backpropagated Neural Network (LM-BNN) to solve the mathematical model for the magnetohydrodynamic peristaltic transport of Reiner–Philippoff (R–Ph) pseudoplastic fluid considering the influences of Ohmic heating, mixed convection, and viscous dissipation through a symmetric channel. The R–Ph fluid model is used in this investigation to elucidate the non-Newtonian behavior of the fluid under consideration. The Reiner–Philippoff fluid model delineates the intricate relationship between stress and deformation rate within the fluid. There are a few studies available on the peristaltic transport of the Reiner–Philippoff fluid that do not incorporate Joule heating, mixed convection, and magnetic field effects. Therefore, a novel mathematical model is developed to employ an artificial neural network technique with a different approach that has not been examined before. The governing equations of the problem are simplified using long wavelength and low Reynolds number approximations, and the resulting system is numerically solved using the BVP4c scheme in MATLAB based on the shooting algorithm. Furthermore, a dataset is constructed through the BVP4c technique for the proposed LM-BNN, considering eight scenarios of peristaltic motion of the Reiner–Philippoff fluid model by varying the Bingham number, the Brinkman number, the Grashof number, the R–Ph fluid parameter, and the Hartmann number. The numerical dataset is divided into 15% for testing, 15% for training, and 70% for validation, which are utilized in LM-BNN to analyze the numerical solutions and Levenberg–Marquardt neural networks (LM-NNs) predicted results. The consistency and effectiveness of LM-BNN are validated through regression analysis, stresses at the wall, error histogram, correlation index, heat transfer, and mean squared error based fitness curves, which vary from 10−3→10−11. Variations in several flow parameters affecting temperature and velocity profiles are explained physically through graphs. Additionally, an analysis of heat transfer and stresses at the wall, including absolute errors, is provided through tables. The outcomes reveal that the improving Grashof number and the Hartmann number tend to increase the temperature profile. Tabular results indicate that rates of heat transfer improve when assigning higher values to the Hartmann number, the Brinkman number, and the Grashof number, whereas stresses at the wall decrease for the Reiner–Philippoff fluid parameter and the Bingham number. The error analysis of numerical simulations is a valuable step in determining whether the data obtained are reliable and accurate. In terms of absolute error, the disagreement between numerical solutions and those predicted by LM-NNs is approximately 10−5→10−11. It is clear from this error analysis that LM-NNs predicted results are consistent and reliable.

Graphically representing covariational functional situations in an interactive embodied digital learning environment

The aim of this study is to investigate how students graphically represent qualitative and quantitative aspects of co-varying quantities in an embodied digital learning environment that provides feedback in the form of an animation showing what kind of motion their graph represents and how this feedback helped them to overcome typical graphing mistakes. Fifty-nine 11-year-old students participated in an intervention programme including exploring co-varying quantities in real-life motion scenarios. In this context, they worked on four tasks that required them to represent distance-time scenarios. Results showed that the embodied learning environment facilitated students to graphically represent motion scenarios. Most of the students provided a correct graph in their first attempt or after revising it based on the provided feedback, managed to overcome typical mistakes and coordinated the variation of the involved measures. This study identified categories of how students used the provided feedback as well as express their related covariational reasoning.

Thermoelectric energy extraction in motion scenarios: Self-powered temperature and pressure detector in an automobile tire

Worldwide, there's a push for carbon reduction through energy conservation. Yet, the potential of employing thermoelectric technology to recover waste heat from moving vehicles remains largely unexplored. This study employs precise simulations to analyze temperature distribution in car tires, unveiling possibilities for energy recovery. At 20 ℃ and 120 km/h, tire temperature peaks at 91.2 ℃ due to friction and rubber deformation. A micro thermoelectric generator (micro-TEG) on the wheel hub, serving as a heat sink, reveals a 3.9 ℃ effective temperature difference. A roadmap, considering factors like ambient temperature, speed, and TE leg size, guides exploration of micro-TEG's maximum power. This leads to a designed module with 8 micro-TEG units (weight: 5.3 g, area: 16 × 4 mm2, fill factor: 20 %, TE leg: 0.4 × 0.4 × 1.2 mm3) in series. Exposed to a 20 ℃ temperature difference, it shows open circuit voltage and maximum power of 1.47 V/1.41 V and 12.06 mW/11.58 mW in flat and curved states. Rigorous testing confirms stability. The 8.96 g module integrates with energy management and Bluetooth circuits. During simulated driving, mobile software records signals with over 94 % consistency. This advances dynamic waste heat recovery, enabling passive tire sensing and innovative links to the Internet of Things (IoT), contributing to field progress.