Potential Field Research Articles

Microstructure Modeling of Transport and Kinetics in Solid State Batteries with LLZO Solid Electrolyte Fabricated Using the Freeze-Tape-Casting Method

In traditional Li-ion batteries with liquid electrolyte, the transport of lithium ions in the electrolyte and lithium in the active material within a porous electrode are studied extensively. The continuum-scale modeling of these transport phenomena considers a homogenized simulation domain consisting of liquid electrolyte and solid active material with effective transport properties accounting for the heterogeneity [1]. There have been fewer studies that consider digitized versions of the experimentally imaged microstructure in continuum-scale modeling studies, particularly to study mechanics [2]. Despite of the computational challenges, there are inherent advantages in using fully resolved microstructure modeling to improve fundamental understanding of the relevant physical and chemical phenomena in porous electrodes, such as localized degradation and stress concentration. In the case of solid electrolytes, specifically scaffold-type LLZO solid electrolytes, microstructure modeling is of even more relevance, particularly to study anisotropic transport, localized degradation phenomena, stress and deformation leading to loss of contact, and chemo-mechanics. Thus far, microstructure modeling studies on experimentally obtained microstructure consisting of solid electrolyte and active material have been quite limited [3]. The studies that have explored this realm have employed voxel-based mesh to discretize the computation domain [3]. While these methods are sufficient proof of concept, the lack of precision in defining the interface separating active material and electrolyte when using voxel-based mesh provides a source of potentially significant numerical error. Without a highly resolved interface mesh, the total interfacial area increases artificially which leads to inaccuracies in calculations of local reaction rate and overpotential. A highly resolved, smooth interface is also necessary when simulating mechanical deformation and stress field in the microstructure. In the present investigation, we model mass transport and charge transport in the active material/electrolyte microstructure to simulate concentration and potential fields with varying mesh types in order to compare their effectiveness and characterize the error that can be attributed to the mesh type chosen, particularly in the case of voxel-based meshes. Additionally, we study the effect of microstructure domain length chosen in the two directions perpendicular to the shortest length (electrode thickness) as well as boundary condition formulation on the boundaries in these axes on the simulation results. The microstructures studied in this work are derived from the image stacks of the bi/trilayer LLZO solid electrolyte fabricated using freeze-tape-casting method [4]. Overall, the present work aims to develop a better understanding of the best practices in microstructure modeling, particularly when using microstructure image stacks obtained from fabricated samples.

Phenotypic diversity in qualitative and quantitative traits for selection of high yield potential field pea genotypes

Field pea (Pisum sativum L.) needs improvement to increase productivity due to its high price and demand. However, the incidence of powdery mildew (PM) disease limits its production. This study aimed to analyze the diversity of qualitative and quantitative traits against powdery mildew resistance by utilizing cluster and principal component analysis to explore PM resistance high-yield potential field peas. Shannon–Weaver's diversity index (Hʹ) displayed high intra-genotype diversity for quantitative and qualitative aspects. Heterogeneity was identified for resistance against powdery mildew infections. Eighty-five genotypes were divided into five groups using Mohalanobis generalized distance (D2) statistics. The highest inter-cluster D2 value was observed between clusters 2 and 3 (11.89) while the lowest value was found between clusters 3 and 4 (2.06). Most of the genotypes had noticeable differences, so these could be employed in a crossing scheme. Twelve genotypes were extremely resistant, 29 genotypes were resistant, 25 genotypes were moderately resistant, 18 genotypes were fairly susceptible, and 1 genotype was susceptible to powdery mildew disease. Among 29 resistant genotypes, BFP77, BFP74, BFP63, BFP62, BFP43, and BFP80 were high yielders and, could be used directly and/or transferred through hybridization to high-yielding disease-susceptible genotypes. Among the 25 moderately resistant genotypes, BFP78, BFP45, BFP79, and BFP48 were found to be high yielders. In principal component analysis (PCA), the first four PCs with Eigen values > 1 accounted for 88.4% variability for quantitative traits. Clustering sorted genotypes into five groups, where groups 1 to 5 assembled 37, 28, 1, 8, and 11 genotypes, respectively. Genotypes of cluster 4 were identified as high yielders with its attributes. Pearson correlation significantly and positively correlated across all traits except for PM. This variation suggested that there is a mechanism to select promising genotypes for field pea breeding. Considering all features, BFP78, BFP77, BFP74, BFP63, BFP62, BFP45, BFP79, and BFP80 could be preferred as high yielders and PM resistance owing to longer pod lengths, seeds per pod and pods per plant.

Boundary Work in the Nordic Media Model: Metajournalistic Discourse on Alternative Media in Denmark, Norway, and Sweden

ABSTRACT The increasing prominence of alternative news media has been identified as a particular challenge to contemporary established journalism, next to collapsing financial models, platform dependency, falling levels of trust and increased competition. Against this background, this study examines how news editors from 23 established news organizations in Sweden, Norway, and Denmark view the rise and role of alternative news media and thereby the boundaries of the journalistic field. Combining strategic action field theory, boundary work and the concept of metajournalistic discourse, it identifies three metajournalistic discourses—We are the (real) journalists, We are in control (for now), and We are under pressure. Together, these metajournalistic discourses portray alternative news media as a challenge that largely has been overcome, and as currently insignificant actors that clearly do not represent “real journalism”. At the same time, the discourses also display concerns that alternative news media might represent potential future field ruptures via their association with broader potentially detrimental media developments, such as genre-confused media audiences or harassment of journalists.

Utilizing Potential Field Mechanisms and Distributed Learning to Discover Collective Behavior on Complex Social Systems

Utilizing Potential Field Mechanisms and Distributed Learning to Discover Collective Behavior on Complex Social Systems

Innovative imaging of iron deposits using cross‐gradient joint inversion of potential field data with petrophysical correlation

AbstractThis study demonstrates the application of the cross‐gradient joint inversion method to investigate iron mineralization zones within a volcano‐sedimentary environment. The presence of minerals with intense contrasts in density or magnetic susceptibility, such as hematite or magnetite, facilitates modelling the distribution of ore bodies with depth. Our approach involves establishing a unified interpretation of reconstructed density and susceptibility models through both independent and joint inversion with sparsity regularization in conjunction with a petrophysical model resulting from core data. This approach provides an ideal strategy to uncover the realistic geologic setting of iron ore deposits. We initially simulated a synthetic model closely resembling real‐case scenarios to assess the efficacy of the cross‐gradient joint inversion algorithm in comparison to independent inversion. Subsequently, the inversion algorithms were implemented on gravity and magnetic data, collected over an area of 500 × 600 m2 in Shavaz iron‐bearing deposits located in the central Iranian block. The primary iron oxide–apatite type mineralization in the study area is associated with the Nain–Dehshir–Baft fault as a NW–SE trending strike‐slip fault. Although both inversion methods yield satisfactory models, incorporating the cross‐gradient constraint in joint inversion resulted in a more constrained delineation of iron–oxide ore deposits in the fault system. This improvement facilitates the differentiation between hematite and a small percentage of magnetite, providing a more accurate estimation of ore depth. Inversion results suggest that the magnetite mineralization is coated with extensive hematite mineralization and both are positioned relatively within the same depth interval, covered by approximately a 15–25 m sequence of sediments.

Уравнения Максвелла в пространстве Лобачевского и моделирование среды со специальными свойствами

Lobachevsky geometry simulates a medium with special constitutive relations Di = ϵ0ϵikEk, Bi = μ0μikHk, where two matrices coincide: ϵik(x) = μik(x). The situation is specified in quasi-Cartesian coordinates (x, y, z) in Lobachevsky space, they are appropriate for modeling a medium nonuniform along the axis z. Exact solutions of the Maxwell equations in complex form of Majorana-Oppenheimer have been constructed. The problem reduces to a second-order differential equation for a certain primary function which can be associated with the one-dimensional Schrödinger problem for a particle in external potential field U(z) = U0e2z. In the frames of the quantum mechanics, Lobachevsky geometry acts as an effective potential barrier with reflection coefficient R = 1; in electrodynamic context, this geometry simulates a medium that effectively acts as an ideal mirror distributed in space. Penetration of the electromagnetic field into the effective medium along the axis z depends on the parameters of an electromagnetic waves ω, k2 1 + k2 2 and the curvature radius ρ of the used Lobachevsky model. The generalized quasi-plane wave solutions f(t, x, y, z) = E + iB and the relevant system of equations are transformed into the real form, which permit us to relate geometry characteristics with expressions for effective tensors of electric and magnetic permittivities.

Induction of seizures and initiation of epileptogenesis by pilocarpine in zebrafish larvae.

Preclinical models of seizures and epilepsy in rodents contributed substantially to the discovery of currently available antiseizure medications. These were also broadly used for investigation of processes of epileptogenesis. Nevertheless, rodent models pose some limitations, thus, new models using alternative species are in high demand. The aim of this study was to describe a new model of seizures/epilepsy induced by the cholinomimetic agent, pilocarpine (PILO), in larval zebrafish. Local field potential (LFP) recordings were conducted to analyze electroencephalographic discharges and correlate it with larval behavior. Hematoxylin and eosin (H&E) staining, as well as TUNEL staining were performed to analyze morphology and apoptosis, respectively. Real-time quantitative polymerase chain reaction (qRT-PCR) was undertaken for gene expression analysis. Acute exposure to PILO, in a concentration-dependent manner, induces electroencephalographic discharges in larval zebrafish, which behaviorally manifest as decreased locomotion and moving time, but enhanced movement velocity. The PILO-induced seizure-like activity is behaviorally distinct from this induced by the application of chemoconvulsant pentylenetetrazole (PTZ). Zebrafish larvae previously exposed to PILO (2 h), after a washing out period, exhibit spontaneous, unprovoked discharges and apoptotic changes in their brains. Here, we comprehensively investigated a new model of PILO-induced seizures/epilepsy in larval zebrafish. We propose that this model may be used to study epileptogenesis and for antiseizure drug screening purposes.

Manipulation-Compliant Artificial Potential Field and Deep Q-Network: Large Ships Path Planning Based on Deep Reinforcement Learning and Artificial Potential Field

Enhancing the path planning capabilities of ships is crucial for ensuring navigation safety, saving time, and reducing energy consumption in complex maritime environments. Traditional methods, reliant on static algorithms and singular models, are frequently limited by the physical constraints of ships, such as turning radius, and struggle to adapt to the maritime environment’s variability and emergencies. The development of reinforcement learning has introduced new methods and perspectives to path planning by addressing complex environments, achieving multi-objective optimization, and enhancing autonomous learning and adaptability, significantly improving the performance and application scope. In this study, we introduce a two-stage path planning approach for large ships named MAPF–DQN, combining Manipulation-Compliant Artificial Potential Field (MAPF) with Deep Q-Network (DQN). In the first stage, we improve the reward function in DQN by integrating the artificial potential field method and use a time-varying greedy algorithm to search for paths. In the second stage, we use the nonlinear Nomoto model for path smoothing to enhance maneuverability. To validate the performance and effectiveness of the algorithm, we conducted extensive experiments using the model of “Yupeng” ship. Case studies and experimental results demonstrate that the MAPF–DQN algorithm can find paths that closely match the actual trajectory under normal environmental conditions and U-shaped obstacles. In summary, the MAPF–DQN algorithm not only enhances the efficiency of path planning for large ships, but also finds relatively safe and maneuverable routes, which are of great significance for maritime activities.

Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by ‘electrophysiology-invisible’ signals. These findings offer a novel perspective on our understanding of RSN interpretation.

Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals.

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by 'electrophysiology-invisible' signals. These findings offer a novel perspective on our understanding of RSN interpretation.

A Mid-Infrared Perfect Metasurface Absorber with Tri-Band Broadband Scalability.

Metasurfaces have emerged as a unique group of two-dimensional ultra-compact subwavelength devices for perfect wave absorption due to their exceptional capabilities of light modulation. Nonetheless, achieving high absorption, particularly with multi-band broadband scalability for specialized scenarios, remains a challenge. As an example, the presence of atmospheric windows, as dictated by special gas molecules in different infrared regions, highly demands such scalable modulation abilities for multi-band absorption and filtration. Herein, by leveraging the hybrid effect of Fabry-Perot resonance, magnetic dipole resonance and electric dipole resonance, we achieved multi-broadband absorptivity in three prominent infrared atmospheric windows concurrently, with an average absorptivity of 87.6% in the short-wave infrared region (1.4-1.7 μm), 92.7% in the mid-wave infrared region (3.2-5 μm) and 92.4% in the long-wave infrared region (8-13 μm), respectively. The well-confirmed absorption spectra along with its adaptation to varied incident angles and polarization angles of radiations reveal great potential for fields like infrared imaging, photodetection and communication.

In-Line Thermal Desorption and Dielectric Barrier Discharge Ionization for Rapid Mass Spectrometry Detection of Explosives.

Thermal desorption (TD) of wipe-based samples was coupled with an in-line dielectric barrier discharge ionization (DBDI) source and rugged compact time-of-flight mass spectrometer (MS) for the detection of explosives, propellants, and postblast debris. The chromatography-free TD-DBDI-MS platform enabled rapid and sensitive detection of organic nitramine, nitrate ester, and nitroaromatic explosives as well as black powder and black powder substitute propellants. Parametric investigations characterized the response to TD temperature and optimized DBDI voltage, aerodynamically assisted entrainment, and fragmentation through in-source collision induced dissociation (isCID). Excess nitrate generated by the DBDI source yielded predominantly nitrate-adduct formation. Subnanogram sensitivities were demonstrated for all explosives investigated, except for nitroglycerin, specifically due to its volatility. Further, most analytes/explosives exhibited tens of picograms sensitivities. The platform also demonstrated the detection of propellant and military explosives from postblast debris. The TD-DBDI-MS system performed well without the need for aerodynamically assisted entrainment (and the associated rough pump), which along with requiring no additional gases (i.e., N2 or He) or solvents, aid in potential field deployment. The ease of TD-DBDI attachment and removal added trace solid or liquid residue detection to the rugged mass spectrometer, designed primarily for the analysis of volatile organic and inorganic compounds.

IBPF-RRT*: An improved path planning algorithm with Ultra-low number of iterations and stabilized optimal path quality

Due to its asymptotic optimality, the Rapidly-exploring Random Tree star (RRT*) algorithm is widely used for robotic operations in complex environments. However, the RRT* algorithm suffers from poor path quality, slow convergence, and unstable generation of high-quality paths in the path planning process. This paper proposes an Improved Bi-Tree Obstacle Edge Search Artificial Potential Field RRT* algorithm (IBPF-RRT*) to address these issues. First, based on the RRT* algorithm, this paper proposes a new obstacle edge search artificial potential field strategy (ESAPF), which speeds up the path search and improves the path quality simultaneously. Second, a bi-directional pruning strategy is designed to optimize the bi-directional search tree branch nodes and combine the bi-directional search strategy to reduce the number of iterations for convergence speed significantly. Third, a novel path optimization strategy is proposed, which enables high-quality paths to be generated stably by creating an entirely new node between two path nodes and then optimizing the paths using a pruning strategy based on triangular inequalities. Experimental results in three different scenarios show that the proposed IBPF-RRT* algorithm outperforms other methods in terms of optimal path quality, algorithm stability, and the number of iterations when compared to the RRT*, Q-RRT*, PQ-RRT*, F-RRT* and CCPF-RRT* algorithms, and proves the effectiveness of the proposed three strategies.

Abstract Mo125: Translating ECG responses to drugs across sexes: model development and validation in the clinic

Background: Sex-based differences in cardiac electrophysiology and their potential to impact susceptibility to cardiac arrhythmia and response to treatment are widely recognized. For example, female sex is an independent risk factor for drug-induced Long QT syndrome, Torsade de Pointes, and sudden cardiac death. However, arrhythmia studies have been predominantly performed in males, thus limiting our understanding of sex-specific arrhythmia mechanisms and the development of sex-specific metrics for arrhythmia risk. Computational tools are a powerful approach for integrating sex differences across scales and studying such multi-level phenomena, accelerating challenging and costly human and animal experiments. Aim: To develop a quantitative tool that predicts drug-induced responses in female electrocardiogram (ECG) features based on ECG data collected in males (and vice versa). Methods: Male and female 1D cable models of ventricular cardiomyocytes were constructed by incorporating transmural heterogeneity and sex-specific parameterizations. We constructed populations of male and female models by randomly perturbing baseline parameters, and derived pseudo-ECGs from computed extracellular potentials. We then utilized lasso regression to map ECG features (QRS duration, QT interval, T-peak-end duration, and T-wave amplitude) extracted from the male pseudo-ECG onto the female ECG features (and vice versa) ( Fig 1 ). Results: Our simulated data-driven translator accurately predicted female pseudo-ECG features from male data in an independent simulated test set (R 2 >0.95). This predictive capability was further assessed in an independent dataset simulating the effects of 95 drugs at different concentrations ( Fig 2A ), exhibiting an average translation error <1% for the QT interval ( Fig 2B ). Proof-of-concept application to ECG data sourced from publicly available FDA-approved datasets, in patients of both sexes under various anti-arrhythmic drugs and drug combinations, demonstrates high accuracy ( Fig 3 ) and validates the translator's applicability to the clinic. Conclusions: Our findings underscore the translator's potential in conducting sex-specific cardiac safety and efficacy assessments, enhancing the precision of evaluations in females. We plan to expand our translator capability to predict sex-specific effects of perturbations, including physiological and pathophysiological stressors.

Determination of borders of structure related to deep minerals based on potential field data in Ba Na area

This paper presents the research results of applying method two-dimensional energy filtering method in the «live window» to separate the high-frequency component of aeromagnetic data in order to eliminate residual flight line errors with amplitude ranges from -5÷5 nT and correct the data source before processing and analysis of data, identify potential field residual anomalies at depths h = 470 m, 1100 m, 1700 m, 2300 m. Application of the horizontal gradient of the vertical derivative of the potential field at various depths to determine the position of shallow fault systems, and determine the boundaries of local heterogeneous blocks related to the prospect of deep-hidden minerals at depths h = 470 m, 1100 m, 1700 m, 2300 m in Ba Na area. The results of the analysis of residual magnetic anomalies have built the distribution diagram of the shallow fault system buried by sediments and zoning the mineral prospect according to the potential anomaly in the study area. Fault system includes faults in sub-latitude, sub-longitude, and Northeast-Southwest directions, in which the sub-latitude fault system is dominant. The area of mineral prospect in Ba Na area to a depth h = 2300 m is determined by the maximum value of horizontal gradient vertical derivative of the closed loop residual gravity anomaly field with amplitude >0.007 mGal/m2, combined with residual gravity anomaly value, and location of mineral points have been revealed on the surface, prospective areas are mainly concentrated in the west, northwest, and southeast of the study area. The results of the analysis provided Geophysicists and Geologists with detailed research plans to evaluate specific types of minerals in the Ba Na area.

A 0.00179 mm2/Ch Chopper-Stabilized TDMA Neural Recording System With Dynamic EOV Cancellation and Predictive Mixed-Signal Impedance Boosting.

This article presents a digitally-assisted multi-channel neural recording system. The system uses a 16-channel chopper-stabilized Time Division Multiple Access (TDMA) scheme to record multiplexed neural signals into a single shared analog front end (AFE). The choppers reduce the total integrated noise across the modulated spectrum by 2.4 × and 4.3 × in Local Field Potential (LFP) and Action Potential (AP) bands, respectively. In addition, a novel impedance booster based on Sign-Sign least mean squares (LMS) adaptive filter (AF) predicts the input signal and pre-charges the AC-coupling capacitors. The impedance booster module increases the AFE input impedance by a factor of 39 × with a 7.1% increase in area. The proposed system obviates the need for on-chip digital demodulation, filtering, and remodulation normally required to extract Electrode Offset Voltages (EOV) from multiplexed neural signals, thereby achieving 3.6 × and 2.8 × savings in both area and power, respectively, in the EOV filter module. The Sign-Sign LMS AF is reused to determine the system loop gain, which relaxes the feedback DAC accuracy requirements and saves 10.1 × in power compared to conventional oversampled DAC truncation-error ΔΣ-modulator. The proposed SoC is designed and fabricated in 65 nm CMOS, and each channel occupies 0.00179 mm2 of active area. Each channel consumes 5.11 μW of power while achieving 2.19 μVrms and 2.4 μVrms of input referred noise (IRN) over AP and LFP bands. The resulting AP band noise efficiency factor (NEF) is 1.8. The proposed system is verified with acute in-vivo recordings in a Sprague-Dawley rat using parylene C based thin-film platinum nanorod microelectrodes.

A novel hybrid Wireless Integrated Sensing Detector for simultaneous EEG and MRI (WISDEM).

Concurrent recording of EEG/fMRI signals reveals cross-scale neurovascular dynamics that are crucial for elucidating fundamental linkage between function and behaviors. However, MRI scanners generate tremendous artifacts for EEG detection. Despite existing denoising methods, cabled connections to EEG receivers are susceptible to environmental fluctuations inside MRI scanners, creating baseline drifts that complicate EEG signal retrieval from the noisy background. Here, a Wireless Integrated Sensing Detector for simultaneous EEG and MRI (WISDEM) is developed to encode fMRI and EEG signals on distinct sidebands of the detector oscillation carrier wave for detection by a standard MRI console over the entire duration of fMRI sequence. Local field potential (LFP) and fMRI maps are retrieved through low-pass and high-pass filtering of frequency-demodulate signals. From optogenetically-stimulated somatosensory cortex, the positive correlation between evoked LFP and fMRI signals validates strong neurovascular coupling, enabling cross-scale brain mapping with this 2-in-1 transducer as a research and diagnostic tool.

Learning a Contact Potential Field for Modeling the Hand-Object Interaction.

Estimating and synthesizing the hand's manipulation of objects is central to understanding human behaviour. To accurately model the interaction between the hand and object (referred to as the "hand-object"), we must not only focus on the pose of the hand and object, but also consider the contact between them. This contact provides valuable information for generating semantically and physically plausible grasps. In this paper, we propose an explicit contact representation called Contact Potential Field (CPF). In CPF, we model the contact between a pair of hand-object vertices as a spring-mass system. This system encodes the distance of the pair, as well as a likelihood of that contact being stable. Therefore, the system of multiple extended and compressed springs forms an elastic potential field with minimal energy at the optimal grasp position. We apply CPF to two relevant tasks, namely, hand-object pose estimation and grasping pose generation. Extensive experiments on the two challenging tasks and three commonly used datasets have demonstrated that our method can achieve state-of-the-art in several reconstruction metrics, allowing us to produce more physically plausible hand-object poses even when the ground-truth exhibits severe interpenetration or disjointedness.

The path planning algorithm for UUV based on the fusion of grid obstacles of artificial potential field

The artificial potential field approach (APFA) has the advantages of short computation time and easy implementation, widely used in path planning. The problems of local oscillation and susceptibility to local minima for APFA are considered in three dimensions (3D) path planning application of unmanned underwater vehicle (UUV), and the artificial potential field grid method (APFG) is proposed. The core principles of APFG include three key methods: firstly, obstacles are rasterized in a 3D map, and repulsive points are attached to the grid surface to alter the range of repulsive influence, thereby compensating for the issue of longer planned paths caused by the idealized modeling in traditional APFA; secondly, the repulsion function of the grid surface is modified to ensure that the potential field is parallel to the grid surface, thus addressing the problem of local oscillation in UUV path planning. Additionally, the introduction of virtual target points helps prevent the UUV from getting trapped in local optima, effectively overcoming the drawbacks of local oscillation and convergence to local minima in 3D path planning for UUV. The simulations show that APFG effectively overcomes the shortcomings of local oscillations and the tendency to fall into local minima for UUV in 3D path planning, and the path planning is smooth and efficient.

Time-Delay Following Model for Connected and Automated Vehicles Considering Multiple Vehicle Safety Potential Fields

Connected and automated vehicles (CAVs) represent a significant development in the transport industry owing to their intelligent and interconnected features. Potential field theory has been extensively used to model CAV driving behaviour owing to its objectivity, universality, and measurability. However, existing car-following models do not consider the impact of time delays and the influence of information from multiple vehicles ahead and behind. This paper focuses on the driving-safety risks associated with CAVs, aiming to enhance vehicle safety and reliability during travelling. We developed a multi-vehicle car-following model based on safety potential fields (MIDM-SPF), taking into account the characteristics of multi-vehicle connected information and time delays. To enhance the model’s precision, real-world data from urban roads were employed, alongside an improved optimisation algorithm to fine-tune the car-following model. The simulation experiment revealed that MIDM-SPF significantly reduces stop-and-go traffic, thereby improving traffic flow stability in urban areas. Additionally, we validated the stability of our model under varying market penetration rates in large-scale mixed traffic. Our findings indicate that increasing the CAV proportion improves the stability of mixed traffic flows, which has important implications for alleviating traffic congestion and guiding the large-scale implementation of autonomous driving in the future.