Trajectory Method Research Articles

Intelligent Optimization of Horizontal Wellbore Trajectory Based on Reinforcement Learning

Ultra-long horizontal wells are crucial for improving the production and developmental benefits of shale oil and gas wells. Currently, advanced downhole semi-closed-loop steering drilling technology depends on a dual communication mechanism between the surface and downhole, as well as the empirical decisions of human experts. However, it is difficult to acutely control the trajectory of ultra-long horizontal open hole drilling in a reservoir owing to geological uncertainties related to shale oil and gas, the uncertainty of the drilling tool’s building capacity, and the lag of measurement information while drilling. This report proposes an adaptive target detection and design method for ultra-long horizontal well trajectories based on reinforcement learning. The developed approach enables dynamic identification of reservoir sequences according to logging-while-drilling data and prediction of horizontal targets in real-time, with a recognition accuracy of 90.1%. Additionally, measurement-while-drilling data are used to accurately characterize the depth, inclination, and azimuth of the target-entering process. The dynamic interaction mechanism between the bit and the target environment is simulated by defining the bit action, reward function, and an update mechanism. The drilling engineering conditions restrict the interactions, such that the bit automatically decides the direction in which to drill the targets, demonstrating 100% accuracy for the wellbore trajectory toward the target. The experimental application results indicate that the developed method can be applied to determine the dynamic target area of a horizontal well in real time and make intelligent downhole decisions regarding ultra-long horizontal section borehole trajectory adjustments while drilling and measuring. The drilling rate of high-quality reservoirs is therefore significantly improved. The results discussed herein provide insights to support further developments of downhole full-closed-loop intelligent autonomous decision-making borehole trajectory control.

A fast calculation method for three-impulse contingency return trajectory during the near-moon phase based on differential algebra

A fast calculation method for three-impulse contingency return trajectory during the near-moon phase based on differential algebra

Theoretical Investigation of Rate Coefficients and Dynamical Mechanisms for N + N + N Three-Body Recombination Based on Full-Dimensional Potential Energy Surfaces

Three-body recombination reactions, in which two particles form a bound state while a third one bounces off after the collision, play significant roles in many fields, such as cold and ultracold chemistry, astrochemistry, atmospheric physics, and plasma physics. In this work, the dynamics of the recombination reaction for the N3 system over a wide temperature range (5000–20,000 K) are investigated in detail using the quasi-classical trajectory (QCT) method based on recently developed full-dimensional potential energy surfaces. The recombination products are N2(X) + N(4S) in the 14A″ state, N2(A) + N(4S) in the 24A″ state, and N2(X) + N(2D) in both the 12A″ and 22A″ states. A three-body collision recombination model involving two sets of relative translational energies and collision parameters and a time-delay parameter is adopted in the QCT calculations. The recombination process occurs after forming an intermediate with a certain lifetime, which has a great influence on the recombination probability. Recombination processes occurring through a one-step three-body collision mechanism and two distinct two-step binary collision mechanisms are found in each state. And the two-step exchange mechanism is more dominant than the two-step transfer mechanism at higher temperatures. N2(X) formed in all three related states is always the major recombination product in the temperature range from 5000 K to 20,000 K, with the relative abundance of N2(A) increasing as temperature decreases. After hyperthermal collisions, the formed N2(X/A) molecules are distributed in highly excited rotational and vibrational states, with internal energies mainly distributed near the dissociation threshold. Additionally, the rate coefficients for this three-body recombination reaction in each state are determined and exhibit a negative correlation with temperature. The dynamic insights presented in this work might be very useful to further simulate non-equilibrium dynamic processes in plasma physics involving N3 systems.

Imaging research on damage localization of concrete structure based on Lamb wave and Bayesian fusion algorithm

Concrete structures are critical materials in the field of industrial construction and are susceptible to invisible damage under the influence of a variety of factors. The method of increasing the number of sensors is usually used to obtain high-precision damage detection, but it increases the experimental cost and signal processing time, so it is especially important to perform high-precision and low-cost non-destructive testing for it. The article uses Bayes to combine the elliptical trajectory method with a probabilistic damage imaging algorithm, and utilizes the algorithm to achieve higher accuracy damage localization imaging results with a smaller number of sensors. The wavelet transform is used to calculate the time of flight to obtain the localization results of the elliptical trajectory method, the signal damage factor is calculated based on the amplitude information to obtain the localization results of the probabilistic damage imaging, the probability distribution of the damage location is obtained using Bayesian, and the damage localization results are obtained by solving the problem through the Markov chain Monte Carlo method. A dense sensor array based and probabilistic imaging algorithm is also used to localize the damage to the material and the results are compared with the results of the Bayesian fusion algorithm. The results show that the Bayesian fusion algorithm can obtain higher accuracy of damage localization with a significant reduction in the number of sensors, and at the same time greatly reduces the number of signals, simplifies the experimental process, and has high practical value.

A trajectory optimization method for CAVs at intersections considering energy-efficiency balance

With the development of vehicle-to-everything (V2X) technology, connected automated vehicles (CAVs) will be an important part of urban road transportation. However, frequent acceleration, deceleration, and rapid stopping within intersections lead to increased energy consumption, reduced driving efficiency and balanced benefits. The study proposes an optimal control method for vehicle trajectories that considers energy-efficiency balance (OCM-EEB) based on the characteristics of V2X technology. The method first develops a vehicle trajectory control model based on the Intelligent Driver Model (IDM) by accounting for the features of V2X technology, and further, introduces the idea of balanced optimization, considering energy-efficiency balance, a vehicle trajectory optimal control model (OCM) is proposed. The proposed model was solved by design algorithms based on the non-dominated sorting genetic algorithm-II (NSGA-II). The experimental results showed that the vehicle trajectory optimization control method could increase the balanced driving efficiency of the vehicle within intersection, which is conducive to improving the robustness of the vehicle’s trajectory state, and then prove the effectiveness of the method proposed in this study. The method also provides an important technical basis for the vehicle trajectory optimization experiments under a real V2X environment in the future.

Exploring the use of deep learning models for accurate tracking of 3D zebrafish trajectories.

Zebrafish are ideal model organisms for various fields of biological research, including genetics, neural transmission patterns, disease and drug testing, and heart disease studies, because of their unique ability to regenerate cardiac muscle. Tracking zebrafish trajectories is essential for understanding their behavior, physiological states, and disease associations. While 2D tracking methods are limited, 3D tracking provides more accurate descriptions of their movements, leading to a comprehensive understanding of their behavior. In this study, we used deep learning models to track the 3D movements of zebrafish. Videos were captured by two custom-made cameras, and 21,360 images were labeled for the dataset. The YOLOv7 model was trained using hyperparameter tuning, with the top- and side-view camera models trained using the v7x.pt and v7.pt weights, respectively, over 300 iterations with 10,680 data points each. The models achieved impressive results, with an accuracy of 98.7% and a recall of 98.1% based on the test set. The collected data were also used to generate dynamic 3D trajectories. Based on a test set with 3,632 3D coordinates, the final model detected 173.11% more coordinates than the initial model. Compared to the ground truth, the maximum and minimum errors decreased by 97.39% and 86.36%, respectively, and the average error decreased by 90.5%.This study presents a feasible 3D tracking method for zebrafish trajectories. The results can be used for further analysis of movement-related behavioral data, contributing to experimental research utilizing zebrafish.

Online Trajectory Replanning for Avoiding Moving Obstacles Using Fusion Prediction and Gradient-Based Optimization

In this study, we introduce a novel method for an online trajectory replanning approach for fixed-wing Unmanned Aerial Vehicles (UAVs). Our method integrates moving obstacle predictions within a gradient-based optimization framework. The trajectory is represented by uniformly discretized waypoints, which serve as the optimization variables within the cost function. This cost function incorporates multiple objectives, including obstacle avoidance, kinematic and dynamic feasibility, similarity to the reference trajectory, and trajectory smoothness. To enhance prediction accuracy, we combine physics-based and pattern-based methods for predicting obstacle movements. These predicted movements are then integrated into the online trajectory replanning framework, significantly enhancing the system’s safety. Our approach provides a robust solution for navigating dynamic environments, ensuring both optimal and secure UAV operation.

Prediction method for 4D trajectory of large fixed wing UAV under multi-layer convolution and bidirectional gating model

Abstract With the increase of low-altitude flight tasks, the importance of research on low-altitude flight safety and efficient operation has gradually become prominent. Therefore, it is necessary to try to establish a low-altitude airspace flight path pre-planning method based on 4D trajectory prediction, which can effectively support the above research. In order to improve the integrity, accuracy and reliability of the long-range trajectory prediction results of large fixed wing UAV (LFW UAV), based on the standard neural network as the basic concept to form a fusion model of improved convolutional neural network (CNN) and bidirectional gated recurrent neural network (BIGRU). At the same time, the flight trajectory fitting preprocessing is added to build the completed multilayer cyclic convolutional improvement model (MCCI). The experimental results show that the MCCI model has obvious advantages in terms of prediction accuracy, deviation range and predictable duration when dealing with the LFW UAV 4D trajectory prediction problem, and better reflects the characteristics of 3D position and spatiotemporal elements. The comparative analysis shows that the error values of the MCCI model are smaller than those of the comparison prediction models in any dimension, and the prediction results have better fit and model performance.

Investigation of Aircraft Conflict Resolution Trajectories under Uncertainties.

As air traffic intensity increases and stochastic uncertainties, such as wind direction and speed, continue to impact air traffic controllers' workload significantly, airlines are increasingly pressured to reduce costs by flying via straighter/more direct trajectories. Due to these changes, it is important to search for new means/solutions for aircraft conflict resolution to ensure the required level of safety and rational flight trajectory. Such a solution could be the implementation of Dubin's method of flight trajectories. This paper aims to propose and deeply analyze a new mathematical model for two-aircraft conflict resolution where the Dubins method is applied in a dynamic conflict scenario. In this model, at a certain moment, the flight trajectory of one aircraft follows a path similar to a moving circle's tangential line. Upon that, the conflict detection and resolution (CDR) model considers wind uncertainty. The proposed CDR method could be applied when uncertainty such as wind direction and speed are inconstant (stochastic) throughout the simulation.

Large‐signal stability analysis for hybridized vehicular power supply systems of rolling stocks

AbstractHybridized vehicular power supply (HVPS) are acquiring widespread adoption in various applications such as providing power for passenger or heavy‐duty electric vehicles, more‐electric ships, electrified rolling stocks, and more. Stability analysis is a critical issue for HVPS, but it presents significant challenges due to their prominent high‐dimensional and nonlinear characteristics. This paper presents a large‐signal stability analysis scheme for HVPS of rolling stocks, enabling the assessment of system stability under sudden load changes. Based on the state‐space averaging method, a mathematical model for the large‐signal behavior of the system is established. Subsequently, the small‐signal stability parameter range of the system is determined by analyzing the eigenvalues of the Jacobian matrix. To further analyze the stability of the system during large‐signal disturbances, a nonlinear decoupling transformation approach is employed to decompose the original high‐order state equations into multiple lower‐order equations. For the decoupled lower‐order subsystems, their stability is analyzed using phase portrait, and the region of attraction (ROA) is determined using the inverse trajectory method. Based on the stability analysis results, the controller is enhanced to bring the points outside the ROA back within it, thereby mitigating the transient instability phenomenon of the system. The effectiveness of the proposed method was demonstrated using two case studies.

Characteristics, Impact Factors and Potential Sources of PM2.5 Pollution for Hainan Island

Based on the environmental monitoring data and meteorological observational data in Hainan Island from 2015 to 2021, the PM2.5-polluted characteristics, influencing factors, and potential contributing regions were analyzed using the backward trajectory simulation, cluster analysis, potential source analysis function （PSCF）, and concentration weight trajectory （CWT） methods. The results showed that PM2.5 in Hainan Island had an obvious seasonal variation, with the highest in winter （22.6 μg·m-3）, followed by that in autumn and spring （17.38 and 16.53 μg·m-3, respectively）, with the lowest in summer （9.79 μg·m-3）. In the past seven years, there were 30 days in Hainan Island in which PM2.5 concentration exceeded the standard. The annual average and four seasons of PM2.5 showed a significant downward trend, and the climatic change rates were -0.97 （annual mean）, -1.09 （spring）, -0.61 （summer）, -0.83 （autumn）, and -1.25 （winter） μg·（m3·a）-1. PM2.5 in Hainan Island was highly correlated with gaseous pollutants, with correlation coefficients of 0.471 （SO2）, 0.633 （NO2）, 0.479 （CO）, and 0.773 （O3-8h）, all passing a significance level of 0.01. PM2.5 was positively correlated with average wind speed and atmospheric pressure and negatively correlated with precipitation, relative humidity, sunshine duration, average temperature, and total solar radiation. Among them, average temperature, relative humidity, and total solar radiation were the main dominant meteorological factors on PM2.5 in Hainan Island. Backward trajectory and potential source analysis revealed that PM2.5 concentration was high （≥20 μg·m-3） in winter and autumn, which was influenced by airflow from inland regions, and Fujian, Zhejiang, Hunan, Jiangxi, Guangdong, and Guangxi provinces were the main potential sources of PM2.5 in Hainan Island.

A Direct Contour Control Method for Free-Form Surface Machining Trajectories Based on Coordinate Transformation

The requirement for high-speed and high-precision contouring with free-form surfaces in the CNC machining process is increasing. The contour error is an essential criterion for the quality of CNC machining. For the problem of contour error control, the present research is mainly based on position tracking, and the control method directly aiming at contour control has not been well studied. This paper proposed a new type of coordinate transformation-based direct contour control method (CT-DCC) for free-form surface machining trajectories. By real-time monitoring of the contour error and the foot point of the free-form parametric trajectory, the contouring task is decomposed into the contour error control task in the normal direction and the velocity control problem in the feed direction. Using coordinate transformation, the output commands of the two tasks are converted to the axial motion command. CT-DCC completely eliminates position tracking in the traditional control to achieve direct control of the contour error, which provides a new solution for the contour control problem of free-form surface machining. Both the axial velocity control and the axial torque control-based CT-DCC scheme were studied, and the two-axis and three-axis direct contour controllers were tested. The simulation and experiment results show the feasibility of the proposed method.

TPANet: A novel triple parallel attention network approach for remaining useful life prediction of lithium-ion batteries

Accurate remaining useful life (RUL) prediction for lithium-ion batteries (LIBs) is important for proper equipment operation. Among the numerous existing studies on battery research, data-driven approaches have gained significant attention because they obviate the need for complex chemical and physical modeling of battery processes. However, in most current data-driven methods, the sliding window size values are determined empirically, which affects both the time required for model prediction and the prediction accuracy. To address this issue, this paper proposes a two-stage prediction method for battery capacity aging trajectories. In the first stage, a false nearest neighbor (FNN) technique is utilized to infer the sliding window size of the battery, reducing the error associated with manually selecting the sliding window size. In the second stage, a novel triple parallel attention network (TPANet) based on convolutional neural networks (CNNs) and attention units is developed, which extracts the input battery capacity degradation features through a parallel mechanism. The introduction of attention units in each parallel branch allows the model to enhance the capture of capacity regeneration phenomena as well as long-term dependence on input data. The proposed approach is validated on two publicly available datasets as well as a battery developed by ourselves. Diverse simulation results demonstrate the ability of the proposed approach to accurately predict the RUL of LIBs in a short time with broad generalization capabilities.

Research on Submersible Safety Operations Based on the Basic Information 3D Motion Trajectory and Entropy Weight Method Models

Research on Submersible Safety Operations Based on the Basic Information 3D Motion Trajectory and Entropy Weight Method Models

Research on the Optimal Trajectory Planning Method for the Dual-Attitude Adjustment Mechanism Based on an Improved Multi-Objective Salp Swarm Algorithm

In this study, an optimization method for the motion trajectory of attitude actuators was investigated in order to improve assembly efficiency in the automatic docking process of large components. The self-developed dual-attitude adjustment mechanism (2-PPPR) is used as the research object, and the structure is symmetrical. Based on the modified Denavit–Hartenberg (MDH) parameter description method, a kinematic model of the attitude mechanism is established, and its end trajectory is parametrically expressed using a five-order B-spline curve. Based on the constraints of the dynamics and kinematics of the dual-posture mechanism, the total posturing time, the degree of urgency of each joint, and the degree of difficulty of the mechanism’s posturing are selected as the optimization objectives. The Lévy flight and Cauchy variation algorithms are introduced into the salp swarm algorithm (SSA) to solve the parameters of the multi-objective trajectory optimization model. By combining the evaluation method of the multi-objective average optimal solution, the optimal trajectory of the dual-tuning mechanism and the motion trajectory of each joint are obtained. The simulation and experiment results show that the trajectory planning method proposed in this paper is effective and feasible and can ensure that the large-part dual-posture mechanism can complete the automatic docking task smoothly and efficiently.

Contrasted trends of intertidal macroalgal communities and sharp decline of canopy-forming species across two decades

Macroalgal communities are essential to coastal ecosystems, yet increasing effects of global change and anthropogenic pressures are leading to their global decline. Investigating the long-term dynamics of these communities across different localities appears crucial to better understand their responses to such pressures, as our knowledge of spatial heterogeneities in macroalgal trajectories remains elusive. To fill this gap, the community trajectory analysis framework provides a set of innovative multivariate metrics to characterize and quantitatively compare the temporal dynamics of different communities. Using long-term monitoring data (2004-2022), this method was applied to intertidal macroalgal communities across 10 locations distributed over more than 500 km of coastline in Brittany, France. Three distinct temporal dynamics were identified. High-shore communities exhibited minimal changes over time, while low-shore communities were characterized by a fluctuating understorey species composition but a general stability pattern. In contrast, the mid-shore community dominated by Ascophyllum nodosum underwent conspicuous changes in composition and structure. Further analysis of the latter community unveiled clear spatial patterns, with a significant deterioration of the structural state attributed to canopy loss in eastern Brittany, negatively impacting understorey species. This decline may ultimately lead to massive changes in coastal ecosystem functioning and services. This study emphasizes the importance of maintaining long-term ecological monitoring as well as the pertinence of temporal trajectories methods to identify and understand community changes at various spatial scales.

Mass-selective instability and resonance ejection modes for DIT with rectangular asymmetric wave shape.

We consider the operation of a digital linear ion trap with resonance radial ejection and mass selective instability modes. Periodic wave shape has a positive part with amplitude and duration and negative part with amplitude and duration , where T is the period. The mapping of the stability diagram, calculations of the well's depth and ion oscillations spectra are presented. The process of resonant excitation of ion oscillations by a dipole sinusoidal signal is studied, as well as ion ejection at the stability boundary. The trajectory method is used for this purpose. It is shown that the mass selectivity of dipole excitation is twice as large for rectangular wave shape compared to sinusoidal wave shape. Increasing the diameter of the round rods of the linear trap gives an increase in the resolving power. The possibility of DIT operation in mass-selective instability mode at the boundary point is discussed.

Rapid and Near-Analytical Planning Method for Entry Trajectory under Time and Full-State Constraints

A rapid trajectory-planning method based on an analytical predictor–corrector design of drag acceleration profile and a bank-reversal logic based on double-stage adaptive adjustment is proposed to solve the entry issue under time and full-state constraints. First, an analytical predictor–corrector algorithm is used to design the profile parameters to satisfy the terminal of altitude, velocity, range, time, and flight-path angle constraints. Subsequently, an adaptive lateral planning algorithm based on heading adjustment and maintenance is proposed to achieve the flight stage adaptive division and determination of the bank-reversal point, thereby satisfying the terminal position and heading angle constraints. Concurrently, a rapid quantification method is proposed for the adjustable capacity boundary of the terminal heading angle. On this basis, a range-and-time correction strategy is designed to achieve high precision and the rapid generation of a three-degree-of-freedom entry trajectory under large-scale lateral maneuvering. The simulation results demonstrated that compared with the existing methods, the proposed method can adaptively divide flight stages, ensuring better multitask applicability and higher computational efficiency.

Research on intelligent nesting algorithm for irregular ship parts based on no-fit-polygon

In order to improve the utilization ratio of steel plates for parts nesting in ship manufacturing, this paper proposes a new intelligent nesting algorithm for irregular parts based on NFP, which can effectively solve the three difficult problems of selection, locating and collision for irregular parts nesting, and further improve the utilization rate of steel plates. The new method directly uses irregular parts as nested objects, establishes the NFP generating algorithm based on the trajectory and Minkowski method to solve the collision problem, proposes a hybrid strategy of the TOPOS principle and BL principle to solve the locating problem, and builds ISAGA to optimize the sequence and rotation angle of the irregular part to solve the selection problem. Finally, the feasibility and effectiveness of the new method are verified by comparing with the existing nesting softwares of Nest Lib and Sigma Nest based on 10 standard test cases of irregular parts in the ESICUP, the experimental results show that the plate utilization of the proposed new method increased on average by 9.76 % and 13.56 %. And compared with the actual packing results of irregular parts in a shipyard, the plate utilization rate of the new method increased by 5.04 % on average.The new method will play an important role in further improving the utilization rate of steel plate in the shipbuilding industry.

Foraging trail traffic rules: a new study method of trajectories of the harvester ants.

Harvester ants are one of the most extensively studied groups of ants, especially the group foraging ants, Messor barbarus (Linnaeus, 1767), which construct long-lasting trunk trails. Limited laboratory investigations have delved into head-on encounters along foraging trails involving workers moving in opposing directions, with fewer corresponding studies conducted in the natural environment. To address this gap, we devised an in-field experimental design to induce lane segregation on the foraging trunk trail of M. barbarus. Using an image-based tracking method, we analyzed the foraging behavior of this species to assess the costs associated with head-on encounters and to figure out the natural coexistence of outgoing and returning workers on a bidirectional route. Our results consistently reveal heightened straightness and speed in unidirectional test lanes, accompanied by an elevated foraging rate compared to bidirectional lanes. This suggests a potential impact of head-on collisions on foraging behavior, especially on foraging efficiency. Additionally, Kinematic analysis revealed distinct movement patterns between outbound and inbound flows, particularly low speed and sinuous trajectories of inbounding unladen workers. The study of encounter rates in two traffic systems hints at the plausible utilization of individual memory by workers within trails, underscoring the pivotal role of encounters in information exchange and load transfer.