Potential Field Research Articles

Examining the performance of Laguerre-based and nonlinear predictive control models for wheeled mobile robots when facing obstacles

This focuses on the problem of controlling the formation of a team of rotating robots that are not homonomic and are not able to interact in a barrier environment. The virtual structure formation control strategy is obtained here to calculate each path of the robot separately while the reference path of the virtual center of the formation is generated by artificial potential fields. In this presentation, we will introduce a new control algorithm that utilizes Model predictive control and nonlinear system dynamics. Three traditional model prediction control controllers (MPC): logger-based MPC, nonlinear MPC, and traditional MPC are used to apply the control algorithm for the nonlinear system. Model predictive control is used to calculate the torques required to track the path by using the dynamic model of the Rotary moving robot. A proposed control rule is the most effective approach to solving the formation and tracking problems. Model Predictive Control will be introduced through the introduction of basic ideas and terms. Traditional MPC, logger-based MPC, and non-linear MPC are among the three MPC methods to be offered. The discrete time display of the power plant under control is taken into account by developing MPC methods for linear or nonlinear systems for practical implementation. The model predictor controller optimizes a cost function to calculate an optimal control sequence at any sampling point. While ignoring the rest, the system only achieves the first control action of this sequence. During subsequent sampling, the optimization problem is resolvable with the use of updated process measurements and a different horizon. The cost function formula is determined by the controller’s purpose. An error function often plays a role in determining the objective by revealing the difference between the desired and actual response. The shape of the formation can be preserved by the law of control by utilizing information from other robots, which can be assured through a new cost function. In the current conditions of disruption, the proposed controllers can also achieve the goal of tracking the path and maintaining formation, according to the results.

Subthalamic nucleus synchronization between beta band local field potential and single-unit activity in Parkinson's disease.

Local field potential (LFP) oscillations in the beta band (13-30 Hz) in the subthalamic nucleus (STN) of Parkinson's disease patients have been implicated in disease severity and treatment response. The relationship between single-neuron activity in the STN and regional beta power changes remains unclear. We used spike-triggered average (STA) to assess beta synchronization in STN. Beta power and STA magnitude at the beta frequency range were compared in three conditions: STN versus other subcortical structures, dorsal versus ventral STN, and high versus low beta power STN recordings. Magnitude of STA-LFP was greater within the STN compared to extra-STN structures along the trajectory path, despite no difference in percentage of the total power. Within the STN, there was a higher percent beta power in dorsal compared to ventral STN but no difference in STA-LFP magnitude. Further refining the comparison to high versus low beta peak power recordings inside the STN to evaluate if single-unit activity synchronized more strongly with beta band activity in areas of high beta power resulted in a significantly higher STA magnitude for areas of high beta power. Overall, these results suggest that STN single units strongly synchronize to beta activity, particularly units in areas ofhigh beta power.

Ultraflexible electrodes for recording neural activity in the mouse spinal cord during motor behavior

Ultraflexible electrodes for recording neural activity in the mouse spinal cord during motor behavior

APF-S2T: Steering to Target Redirection Walking Based on Artificial Potential Fields.

Redirected walking (RDW) enables users to walk naturally within a virtual environment that is larger than the physical environment. Recently, several artificial potential field (APF) and alignment-based redirected controllers have been developed and have been demonstrated to significantly outperform conventional controllers. APF Steer-to-Gradient (APF-S2G) and APF Redirected Walking (APF-RDW) utilize the negative gradient and the total force vector, respectively, which are localized to the user's position. These vectors usually point towards the opposite wall when the user is in corridors, resulting in frequent resets within those regions. This paper introduces the APF Steer-to-Target (APF-S2T), a redirected controller that first finds the target sample point with the lowest score in the user's walkable area in both physical and virtual environments. The score of a sample point is determined by the APF value at the point and the distance from the user's position. The direction from the user's position to the target point is then used as the steering direction for setting RDW gains. We conducted a simulation-based evaluation to compare APF-S2T, APF-S2G, APF-RDW, Visibility Polygon-based alignment (Vis.-Poly.) and Alignment-Optimized controllers in terms of the number of resets and the average distance between resets. The results indicated that APF-S2T significantly outperformed the state-of-the-art controllers.

Fast finite-time formation control with obstacle avoidance of second-order multi-agent systems with input saturation

This paper proposes a leader-follower formation control protocol using fast finite-time (FFT) theory, based on second-order nonlinear multi-agent systems (MASs) with input saturation constraints. The artificial potential field method is addressed to implement the formation control with obstacle avoidance of the MASs. An adaptive FFT strategy is constructed that all the agents follow required formation performance. Neural networks are considered to approximate uncertain functions, which improved convergence and ensuring safety of distributed formation control. Finally, the validity of the theoretical approach is demonstrated by FFT stability theory validated by simulation examples.

Cushion gas effects on hydrogen storage in porous rocks: Insights from reservoir simulation and deep learning

Underground hydrogen (H2) storage (UHS) is crucial for the H2 economy, offering scalable and long-term solutions vital for reliable and sustainable H2-based energy systems. Cushion gas is a common component in UHS designs, but its impact on H2 storage performance is not yet fully understood. This study bridges this research gap by systematically investigating the effects of various cushion gas scenarios on UHS performance in porous rocks, specifically saline aquifers and depleted gas reservoirs, using reservoir simulations and deep learning. Firstly, we conducted 8,000 multiphase compositional reservoir simulations for UHS operations in two types of storage formations, considering four cushion gas scenarios: no cushion gas, methane (CH4), nitrogen (N2), and carbon dioxide (CO2). These simulations cover a wide range of geological and operational parameters relevant to practical UHS projects. From these simulations, we derived key insights into the physical mechanisms governing UHS processes and proposed critical storage performance metrics, including H2 withdrawal efficiency, produced H2 purity, produced gas–water ratio, and well injectivity. Then, we developed a unified reduced-order model (ROM) using a deep neural network (DNN) based on the comprehensive simulation results. The DNN architecture, with an input layer, five hidden layers, and an output layer, takes 12 geological and operational parameters as inputs, and forecasts the four performance metrics. The ROM accurately predicts the cyclic evolution of performance metrics and is over 5,000,000 times faster than traditional physics-based simulations, allowing for thorough uncertainty quantification of UHS performance prediction under various geological and operational conditions. Key findings of this study include: (a) UHS in porous rocks is technically promising, with improving storage performance over cycles; (b) UHS in saline aquifers has higher withdrawal efficiency and purity but lower gas–water ratio and injectivity compared to depleted gas reservoirs; (c) In saline aquifers, cushion gas reduces withdrawal efficiency and purity but significantly enhances gas–water ratio and injectivity, making it particularly beneficial in scenarios with high water production risks; (d) In depleted gas reservoirs, cushion gas is less important under the current operational conditions, as it barely affects withdrawal efficiency and purity, and these reservoirs inherently exhibit high gas–water ratio and injectivity due to initial/leftover native gas (mostly CH4); (e) The cushion gas type slightly affects storage performance, with N2 slightly outperforming CH4 and CO2 in withdrawal efficiency and purity, CO2 being better for gas–water ratio, and CH4 for injectivity. The findings of this study support cushion gas designs in future UHS projects in both saline aquifers and depleted gas reservoirs. Moreover, the developed ROM offers a highly efficient tool for conducting extensive sensitivity analysis and uncertainty quantification, optimizing operational conditions, and facilitating the screening of potential field sites for future UHS projects.

UAVs Path Planning based on Combination of Rapidly Exploring Random Tree and Rauch-Tung-Striebel Filter

Aiming at the problem of Unmanned Aerial Vehicle(UAV) formation path planning under complex constraints, a UAV formation path planning method based on the combination of Rapidly exploring Random Tree (RRT) and Rauch-Tung-Striebel (RTS) filter is proposed. Firstly, a path planning algorithm based on the improved RRT algorithm with adaptive step size is de-signed to solve the problem that the RRT algorithm is easy to fall into local optimum. Then, an RTS filter is introduced to smooth the trajectory planned by the improved RRT algorithm to achieve curvature continuity. Finally, taking the smooth trajectory as the reference, a UAV formation path planning algorithm over the Artificial Potential Field (APF) method is designed. The simulation results show that the designed UAV formation path planning algorithm can solve the planning problems of single trajectory and formation trajectories in complex constrained space, and can plan the formation trajectory with continuous curvature, to facilitate the UAV trajectory tracking control.

A variational data assimilation approach for sparse velocity reference data in coarse RANS simulations through a corrective forcing term

The Reynolds-averaged Navier–Stokes (RANS) equations provide a computationally efficient method for solving fluid flow problems in engineering applications. However, the use of closure models to represent turbulence effects can reduce their accuracy. To address this issue, recent research has explored data-driven techniques such as data assimilation and machine learning.An efficient variational data assimilation (DA) approach is presented to enhance steady-state eddy viscosity based RANS simulations. To account for model deficiencies, a corrective force term is introduced in the momentum equation. In the case of only velocity reference data, this term can be represented by a potential field and is divergence-free. The DA implementation relies on the discrete adjoint method and approximations for efficient gradient evaluation.The implementation is based on a two-dimensional coupled RANS solver in OpenFOAM, which is extended to allow the computation of the adjoint velocity and pressure as well as the adjoint gradient. A gradient-based optimizer is used to minimize the difference between the simulation results and the reference data. To evaluate this approach, it is compared with alternative data assimilation methods for canonical stationary two-dimensional turbulent flow problems. For the data assimilation, sparsely distributed reference data from averaged high-fidelity simulation results are used.The results suggest that the proposed method achieves the optimization goal more efficiently compared to applying data assimilation for obtaining the eddy viscosity, or a field modifying the eddy viscosity, directly. The method works well for different reference data configurations and runs efficiently by leveraging coarse meshes.

Privacy-preserving federated transfer learning for defect identification from highly imbalanced image data in additive manufacturing

Defect identification is a crucial task for process monitoring and quality evaluation in additive manufacturing (AM). Deep learning (DL) has shown great potential for diverse fields, but some challenges have hindered the application in AM process monitoring. Firstly, DL-based methods are driven by big data and require a large number of training data. However, in reality, defective data is often rare, so different AM manufacturers can collaborate to train a global model for detecting defects. Nevertheless, due to privacy concerns and conflicts of interest, data-sharing between different manufacturers might be dangerous. Additionally, the heterogeneities among manufacturers’ data leads to the domain shift, making it difficult to obtain a well-generalized model. Moreover, the imbalance issue of powder-spreading defects seriously damages the performance of the defect recognition. In this paper, we proposed FTLAM, a federated transfer learning method, to address the above issues. Concretely, to solve the data privacy concerns, federated learning (FL) is utilized to collaboratively train a global model between different AM manufacturers without exchanging original data. Furthermore, a client-oriented transfer method is proposed to mitigate the heterogeneities across multiple clients and improve the model's generalization. Meanwhile, a multi-loss joint optimization approach is designed to alleviate the data imbalance. Extensive experiments on laser powder bed fusion (LPBF) image datasets of powder-spreading have demonstrated that our FTLAM can obtain satisfactory performance of powder spreading defect identification.

5th Argentinean Meeting on Evolutionary Biology (RABE V): Report on the "Evo-Devo" Extended Symposium.

Evolutionary developmental biology (Evo-Devo) is flourishing in Latin America, particularly Argentina, where researchers are leveraging this integrative field to unlock the secrets of the region's remarkable biodiversity. A recent symposium held at the 5th Argentinean Meeting on Evolutionary Biology (RABE V) showcased a vibrant Evo-Devo community and the diversity of its research endeavors. The symposium included 3 plenary talks, 3 short talks, and 12 posters, and spanned a range of organisms and approaches. Interestingly, the symposium highlighted a prevalence of "top-down" Evo-Devo studies in the region, where researchers first analyze existing diversity and then propose potential developmental mechanisms. This approach, driven in part by financial constraints and the region's historical focus on natural history, presents a unique opportunity to bridge disciplines like comparative biology, paleontology, and botany. The symposium's success underscores the vital role of Evo-Devo in Latin America, not only for advancing our understanding of evolution but also for providing valuable tools to conserve and manage the region's irreplaceable biodiversity. As Evo-Devo continues to grow in Latin America, fostering collaboration and knowledge exchange within the region and beyond will be crucial for realizing the full potential of this transformative field.

Dynamic Multi-Target Self-Organization Hunting Control of Multi-Agent Systems

In this paper, we present a novel coordinated method tailored to address the dynamic multi-target hunting control problem in multi-agent systems, offering significant practical value. Our approach encompasses several key components: initially, we introduce a task allocation model that integrates a fuzzy inference system with a particle swarm optimization algorithm. This hybrid model efficiently allocates hunting tasks for scattered evading targets, effectively transforming the dynamic multi-target hunting problem into multiple dynamic single-target-hunting problems. This transformation enhances the speed and efficacy of task allocation. Subsequently, we propose an attraction/repulsive model grounded in potential field theory. This model facilitates the coordinated hunting of each target by organizing agents into subgroups. Relying solely on relative position and velocity information between agents and targets, our model simplifies computation, while maintaining effectiveness. Furthermore, the coordination of hunting activities for each target is achieved through a series of agent subgroups, guided by our proposed motion model. This systematic approach ensures a cohesive and efficient hunting strategy. Finally, we validate the effectiveness and feasibility of our proposed method through simulation results. These results provide empirical evidence of the method’s efficacy and potential applicability in real-world scenarios.

NKCC1 in Neonatal Cochlear Support Cells Reloads Ions Necessary for Cochlear Spontaneous Activity.

In the auditory system, the spontaneous activity of cochlear inner hair cells (IHCs) is initiated by the release of ATP from inner supporting cells (ISCs). This ATP release sets off a cascade, activating purinergic autoreceptors, opening of Ca2+-activated Cl- channel TMEM16A, Cl- efflux and osmotic cell shrinkage. Then, the shrunken ISCs efficiently regain their original volume, suggesting the existence of mechanisms for refilling Cland K+, priming them for subsequent activity. This study explores the potential involvement of NKCCs (Na+-K+-Cl- cotransporters) and KCCs (K+-Cl- cotransporters) in ISC spontaneous activity, considering their capability to transport both Cl- and K+ ions across the cell membrane. Employing a combination of immunohistochemistry, pharmacological interventions, and shRNA experiment, we unveiled the pivotal role of NKCC1 in cochlear spontaneous activity. Immunohistochemistry revealed robust NKCC1 expression in ISCs, persisting until the 2nd postnatal week. Intriguingly, we observed a developmental shift in NKCC1 expression from ISCs to synaptophysin-positive efferent terminals at postnatal day 18, hinting at its potential involvement in modulating synaptic transmission during the post-hearing period. Experiments using bumetanide, a well-known NKCC inhibitor, supported the functional significance of NKCC1 in ISC spontaneous activity. Bumetanide significantly reduced the frequency of spontaneous extracellular potentials (sEP) and spontaneous optical changes (sOCs) in ISCs. NKCC1-shRNA experiments conducted in cultured cochlear tissues further supported these findings, demonstrating a substantial decrease in event frequency and area. Taken together, we revealed the role of NKCC1 in shaping the ISC spontaneous activity that govern auditory pathway development.

Examining electrical equivalent circuit models of insulators in transmission lines under pollution conditions: a comprehensive study and analysis

This research presents a comprehensive study investigating the behavior of insulators in transmission lines under various pollution conditions. Utilizing equivalent electrical circuits, the analysis aims to understand the impact of pollution on insulator performance, which is crucial for maintaining the reliability and efficiency of power transmission systems. By providing valuable insights into the relationship between pollution and insulator behavior, this study contributes to the development of improved strategies for mitigating the effects of pollution on transmission lines, ultimately enhancing the overall safety and stability of electrical power networks. The study focuses on a digital model that enables the visualization of potential and electric field distribution within a series of glass insulators utilized in Algerian electrical grids. Initially, a parallel RC network equivalent circuit was devised and its parameters were determined through finite element analysis using Comsol Multiphysics. This equivalent circuit was subsequently integrated into ATP/EMTP software to simulate leakage currents, yielding satisfactory outcomes. The model was then incorporated into the isolator's equivalent circuit. Results from various simulations indicate that the occurrence of discharge along the dry strip impacts leakage current. Furthermore, the re-initiation of discharge on the contaminated insulator's surface is influenced by the pollutant deposit's conductivity and distribution. Therefore, understanding the pollution level is vital for accurately evaluating and sizing the insulator chain at the installation site.

Research on obstacle avoidance of multi-AUV cluster formation based on virtual structure and artificial potential field method★

The formation of autonomous underwater vehicle (AUV) in the three-dimensional environment is very important for carrying out complex ocean tasks. During formation sailing, it is necessary to consider avoiding obstacles while maintaining the formation. In this paper, an AUV formation obstacle avoidance method based on virtual structure and artificial potential field method is proposed. Firstly, a complete mathematical modeling of AUV is carried out, and a sliding mode position tracker based on virtual position points is designed based on this model. Then the tracking points of each AUV in the formation are calculated according to the expected trajectory and formation feature structure to ensure that the spacing between AUVs meets the requirements of the formation. Finally, the artificial potential field method is introduced into the position tracker to avoid obstacles between the obstacle and the AUV. Five groups of simulation experiments of parallel formation, vertical formation, cross formation, triangular formation and unknown complex environment are designed. The formation scalability of the algorithm is simulated and compared with the virtual leader method in related references. Finally, KKSwarm platform is used to conduct a real simulation in 2D environment to verify the effectiveness of the proposed algorithm.The experimental results show that the proposed method can effectively realize the formation navigation of multiple underwater vehicles and successfully avoid obstacles, which provides an effective method to solve the problem of the formation obstacle avoidance of underactuated AUV.

Why the racing industry and equestrian disciplines need to implement population pharmacokinetics: To learn, explain, summarize, harmonize, and individualize.

Population pharmacokinetics (POP PK) is a powerful pharmacokinetic tool, which measures quantitatively, and explains the variability in drug exposure and drug effect between individuals. POP PK uses an observational (nonexperimental) approach; it is conducted in the target population living in its normal environment (e.g., farm and race-track). The strength of the POP PK approach lies in its greater relevance for the population studied in its different natural environments than experimental studies carried out in more or less biased laboratory conditions. In clinical settings, it is commonly necessary to restrict the number of samples per subject collected for analysis and the derived data cannot be analyzed using traditional individual data analytical methods; rather data are merged and analyzed with an appropriate statistical tool: the nonlinear mixed effect model (NLMEM). POP PK modeling is frequently used with the objective of adjusting drug dosage, and hence drug exposure, not only for the whole population but also for subgroups of animals (e.g., for a given breed, sex, and age). It can also have application at the individual subject level, in the context of precision medicine. For horses, the use of the POP PK/PD model will allow prescribers to estimate an individual Withdrawal Time for a given horse whose treatment they are supervising. Another potential field of application will be meta-analysis of existing data to generate new knowledge on a drug or to collate and synthesize, in an objective and transparent manner, existing data; this will facilitate harmonization of screening limits at an international level.

Dynamic path planning for mobile robots based on artificial potential field enhanced improved multiobjective snake optimization (APF‐IMOSO)

AbstractWith the widespread adoption of mobile robots, effective path planning has become increasingly critical. Although traditional search methods have been extensively utilized, meta‐heuristic algorithms have gained popularity owing to their efficiency and problem‐specific heuristics. However, challenges remain in terms of premature convergence and lack of solution diversity. To address these issues, this paper proposes a novel artificial potential field enhanced improved multiobjective snake optimization algorithm (APF‐IMOSO). This paper presents four key enhancements to the snake optimizer to significantly improve its performance. Additionally, it introduces four fitness functions focused on optimizing path length, safety (evaluated via artificial potential field method), energy consumption, and time efficiency. The results of simulation and experiment in four scenarios including static and dynamic highlight APF‐IMOSO's advantages, delivering improvements of 8.02%, 7.61%, 50.71%, and 12.74% in path length, safety, energy efficiency, and time‐savings, respectively, over the original snake optimization algorithm. Compared with other advanced meta‐heuristics, APF‐IMOSO also excels in these indexes. Real robot experiments show an average path length error of 1.19% across four scenarios. The results reveal that APF‐IMOSO can generate multiple viable collision‐free paths in complex environments under various constraints, showcasing its potential for use in dynamic path planning within the realm of robot navigation.

Trajectory Planning for Cooperative Double Unmanned Surface Vehicles Connected with a Floating Rope for Floating Garbage Cleaning

Double unmanned surface vehicles (DUSVs) towing a floating rope are more effective at removing large floating garbage on the water’s surface than a single USV. This paper proposes a comprehensive trajectory planner for DUSVs connected with a floating rope for cooperative water-surface garbage collection with dynamic collision avoidance, which takes into account the kinematic constraints and dynamic cooperation constraints of the DUSVs, which reflects the current collection capacity of DUSVs. The optimal travel sequence is determined by solving the TSP problem with an ant colony algorithm. The DUSVs approach the garbage targets based on the guidance of target key points selected by taking into account the dynamic cooperation constraints. An artificial potential field (APF) combined with a leader–follower strategy is adopted so that the each USV passes from different sides of the garbage to ensure garbage capturing. For dynamic obstacle avoidance, an improved APF (IAPF) combined with a leader–follower strategy is proposed, for which a velocity repulsion field is introduced to reduce travel distance. A fuzzy logic algorithm is adopted for adaptive adjustment of the desired velocities of the DUSVs to achieve better cooperation between the DUSVs. The simulation results verify the effectiveness of the algorithm of the proposed planner in that the generated trajectories for the DUSVs successfully realize cooperative garbage collection and dynamic obstacle avoidance while complying with the kinematic constraints and dynamic cooperation constraints of the DUSVs.

Human local field potentials in motor and non-motor brain areas encode upcoming movement direction

Limb movement direction can be inferred from local field potentials in motor cortex during movement execution. Yet, it remains unclear to what extent intended hand movements can be predicted from brain activity recorded during movement planning. Here, we set out to probe the directional-tuning of oscillatory features during motor planning and execution, using a machine learning framework on multi-site local field potentials (LFPs) in humans. We recorded intracranial EEG data from implanted epilepsy patients as they performed a four-direction delayed center-out motor task. Fronto-parietal LFP low-frequency power predicted hand-movement direction during planning while execution was largely mediated by higher frequency power and low-frequency phase in motor areas. By contrast, Phase-Amplitude Coupling showed uniform modulations across directions. Finally, multivariate classification led to an increase in overall decoding accuracy (>80%). The novel insights revealed here extend our understanding of the role of neural oscillations in encoding motor plans.

A self-supervised framework for cross-modal search in histopathology archives using scale harmonization

The exponential growth of data across various medical domains has generated a substantial demand for techniques to analyze multimodal big data. This demand is particularly pronounced in fields such as computational pathology due to the diverse nature of the tissue. Cross-modal retrieval aims to identify a common latent space where different modalities, such as image-text pairs, exhibit close alignment. The primary challenge, however, often lies in the representation of tissue features. While language models can be trained relatively easily, visual models frequently struggle due to the scarcity of labeled data. To address this issue, the innovative concept of harmonization has been introduced, extending the learning scheme distillation without supervision, known as DINO. The harmonization of scale refines the DINO paradigm through a novel patching approach, overcoming the complexities posed by gigapixel whole slide images in digital pathology. Experiments conducted on diverse datasets have demonstrated that the proposed approach significantly enhances cross-modal retrieval in tissue imaging. Moreover, it exhibits vast potential for other fields that rely on gigapixel imaging.

An Improved Artificial Potential Field Method for Ship Path Planning Based on Artificial Potential Field—Mined Customary Navigation Routes

In recent years, the artificial potential field has garnered significant attention in ship route planning and traffic flow simulation. However, the traditional artificial potential field method faces challenges in accurately simulating a ship’s customary route and navigating experience, leading to significant deviations in prediction results. To address these issues, in this study, we propose an innovative method for simulating and predicting ship traffic flow, building upon the artificial potential field approach. We introduce an AIS track heat map based on the kernel density function and enhance the artificial potential field model by incorporating factors, such as ship navigation habits and ship size. Through a comparison of traffic flow changes before and after the construction of a wind farm, the optimized model demonstrates its effectiveness in improving the accuracy of prediction results.