Final Trajectory Research Articles

Becoming a student: Management of transition to higher education among first-generation students’

This study investigates the identity formation of first-generation students during their transition to university in the Czech Republic, while also reflecting the impact of COVID-19-related distance learning. Drawing upon qualitative interviews ( N = 25), the study explores their relationship with their studies. The findings, presented as identity trajectories, highlight how the formation of the student identity is based upon the interaction of multiple factors. The first trajectory represents students who resist the student identity, the significant reason is that they see being a student in opposition to being an adult. The second trajectory encompasses various student journeys: Some experienced significant shifts from their initial approach to studying, including those who overcommitted and burned out, some became distracted and found it hard to focus and still others found themselves unprepared for the demands of university scholarship. The final trajectory depicts students who smoothly adjusted, facilitated by disciplinary interest and lecturer support. Interestingly, the experience of COVID-19 played an unpredictable role, weakening learning engagement for some but providing time to focus on studies for others, shifting their identity trajectories. Overall, the study underscores the importance of tailored drop-out prevention interventions that acknowledge the diverse pathways through which students navigate their identity formation.

Delivery optimization for collaborative truck–drone routing problem considering vehicle obstacle avoidance

Drone participating in logistics delivery has gained much concern due to its potential superiority of operational efficiency and service costs. How to optimize the hybrid vehicle delivery trip is still a crucial issue. Besides, complex urban environment and regulatory no-through zones bring more rigorous challenges. The aim is to develop an efficient routing solution that considers obstacles encountered by the truck and drone, ensuring the timely delivery. This paper proposes an efficient deep reinforcement learning to optimize the collaborative truck–drone routing problem (C-TDRP) under vehicle obstacle avoidance trail. Specifically, the trucks deploy a fleet of drones to serve the random distributed consumers subject to minimum delivery cost. Homogeneous drones serve multiple demand nodes per trip with limited energy carrying capacity. We design the C-TDRP formulation with studying the feasibility of obstacle avoidance routing. Owing to the computational complexity of the proposed mathematical model, an intelligent optimization algorithm based on deep reinforcement learning is developed, named pointer network (Ptr-Net), which can capture the position weights associated with network sequence automatically. Through rigorous experimental validations, the results demonstrate that the vehicle energy consumption of final trajectory reduces by more than 42%, even for large-scale delivery scenarios. Our proposed method not only enhances the optimization performance but also lays the foundation for more intelligent and adaptable logistics delivery in complex urban environments.

SISGAN: A Generative Adversarial Network Pedestrian Trajectory Prediction Model Combining Interaction Information and Scene Information

Accurate pedestrian trajectory prediction is crucial in many fields. This requires the full use and learning of pedestrians’ social interactions, movements, and environmental information. In view of the current research on pedestrian trajectory prediction, wherein most of the pedestrian interaction information is explored from the level of overall interaction, this paper proposes the SISGAN model, which designs a social interaction module from the perspective of the target pedestrian, and takes four kinds of interaction information as the influencing factors of pedestrian interaction, so as to describe the influence mechanism of pedestrian–pedestrian interaction. In addition, in terms of environmental information, the index density of pedestrian historical trajectory in space is taken into account in the extraction of environmental information, which increases the potential correlation between environmental information and pedestrians. Finally, we integrate social interaction information and environmental information and make the final trajectory prediction based on GAN. Experiments on ETH and UCY datasets demonstrate the effectiveness of the SISGAN model proposed in this paper.

Optimal Path Planning Algorithm with Built-In Velocity Profiling for Collaborative Robot.

This paper proposes a method for solving the path planning problem for a collaborative robot. The time-optimal, smooth, collision-free B-spline path is obtained by the application of a nature-inspired optimization algorithm. The proposed approach can be especially useful when moving items that are delicate or contain a liquid in an open container using a robotic arm. The goal of the optimization is to obtain the shortest execution time of the production cycle, taking into account the velocity, velocity and jerk limits, and the derivative continuity of the final trajectory. For this purpose, the velocity profiling algorithm for B-spline paths is proposed. The methodology has been applied to the production cycle optimization of the pick-and-place process using a collaborative robot. In comparison with point-to-point movement and the solution provided by the RRT* algorithm with the same velocity profiling to ensure the same motion limitations, the proposed path planning algorithm decreased the entire production cycle time by 11.28% and 57.5%, respectively. The obtained results have been examined in a simulation with the entire production cycle visualization. Moreover, the smoothness of the movement of the robotic arm has been validated experimentally using a robotic arm.

Feature Enhanced Spatial–Temporal Trajectory Similarity Computation

AbstractTrajectory similarity computation is a fundamental function in many applications of urban data analysis, such as trajectory clustering, trajectory compression, and route planning. In this paper, we study trajectory similarity computation on the road network. However, existing methods have been designed primarily for road network trajectories with spatial information, while ignoring the important temporal information in the real world. To solve this problem, we propose a Feature Enhanced Spatial–Temporal trajectory similarity computation framework FEST, which is a graph neural network (GNN) and sequence model pipeline. We first use the GNN model to capture global information on the road network. In particular, we enhance the process with multi-graph to learn multiple signals from the road network on different aspects. In addition to the original road network topology signal, we also take into account the content signal to learn spatial–temporal features from trajectory traffic, as well as the adaptive similarity signal of the road network to learn hidden features. From these three signals, we construct a multi-graph and use GCN to learn road intersection embedding jointly. Next, we propose a feature-enhanced Transformer with spatial–temporal information to learn correlation within the trajectory, and we further use mean-pooling to get the final trajectory embedding. We compare FEST with six trajectory similarity computation methods on two real-world datasets. The results show that FEST consistently outperforms all baselines and can improve the accuracy of the best-performing baseline.

Online trajectory anomaly detection model based on graph neural networks and variational autoencoder

Abstract To efficiently determine whether an entire trajectory exhibits abnormal behavior, we introduce an online trajectory anomaly detection model known as GeoGNFTOD, which employs graph neural networks for road segment representation, creating a directed graph by mapping trajectories onto the road network. The graph representation is constructed based on the road segments in this directed graph. By utilizing Transformer sequence encoding, the trajectory representation is derived and hierarchical geographic encoding captures the GPS mapping of the original trajectories. Merging these two representations produces the final trajectory representation, serving as input for an LSTM-based variational autoencoder to reconstruct the trajectory representation, facilitating rapid online anomaly detection. Experimental findings on a large-scale taxi trajectory dataset illustrate the superior performance of GeoGNFTOD compared to baseline algorithms.

Microelectrode Recording During Deep Brain Stimulation Does Not Consistently Represent Lead Trajectory.

Long-term outcomes in deep brain stimulation (DBS) depend on accuracy of lead placement. Microelectrode recording (MER) is a long-used adjunct to leverage neurophysiological information to confirm satisfactory trajectory of implanted electrodes. The goal of this study was to evaluate the consistency in which electrodes are placed in sampled microelectrode trajectories. This is a retrospective study using intraoperative computed tomography to measure final electrode deviation from MER probe placement during the DBS insertion targeting subthalamic nucleus. Fifteen patients had 29 DBS leads placed using MER assistance. Radial distance between the probe and the lead were measured for each patient using intraoperative imaging. In addition, the preoperative target to final lead error was measured in 14 patients undergoing subthalamic nucleus implants without the use of MER and compared with the 15 patients in which MER was used as an adjunct. There was no significant difference in the mean radial target error (1.2 vs 1.0 mm, P = .156) when comparing the leads placed with or without MER assistance, respectively. The mean difference in final position of microelectrode compared with DBS lead was 0.9 ± 0.1 (range 0.4-2.0 mm). Of all MER-assisted electrodes placed, 44.8% (13) of electrode final positions radially deviated 1.0 mm or more from the MER probe. Electrode placement may deviate significantly from MER trajectories. Given the concern that physiological data may not be representative of the final electrode trajectory, surgeons should consider using intraoperative imaging or other adjunctive techniques during DBS to confirm accuracy and satisfactory trajectory of DBS leads.

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

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

The Labyrinth Paradox: Shaping An Intricate Network of Pathways Through an Interlocking Domain Framework

The burgeoning of multiple domain subjects, generated through the existence of various management compartments, demonstrate not only the importance of shedding light on the core functionalities of each compartment, the value of its functional deliverables, the root causes for its regular and abnormal occurrences, the strength and impact of its final products towards the comprehensive mission of the organization, the multiple trajectories, whereby a convoluted conundrum can be resolved and demystified, but also focuses on the pertinence of forming reasonable and concrete grounds for explanation and clarification, by way of academic discourse and practice. This is crucial in the attempt to project the entwinement between core domain underpinnings and its interconnected unions with that of advanced knowledge practice. Although, these multiple domain subjects have made an effort to perform these objectives, there still remains a void existing between them. Domain subjects have yet to provide reasonable grounds for explanation as to how the core functionalities of each compartment, value of its deliverables, regular and abnormal occurrences, impact of final products and multiple trajectories effect compartments other than their own in terms of quality, technology, knowledge, innovation and research. This paper will discuss the labyrinth paradox, which exists within the burgeoning of multiple domain subjects, and how the concept of “Multi Congruent Domain Structuring (MCDS)” can shape an intricate network of pathways and key attributes through an interlocking and associative domain framework.

A neural network-based algorithm for the reconstruction and filtering of single particle trajectory in magnetic particle tracking.

Magnetic particle tracking (MPT) is a recently developed non-invasive measurement technique that has gained popularity for studying dense particulate or granular flows. This method involves tracking the trajectory of a magnetically labeled particle, the field of which is modeled as a dipole. The nature of this method allows it to be used in opaque environments, which can be highly beneficial for the measurement of dense particle dynamics. However, since the magnetic field of the particle used is weak, the signal-to-noise ratio is usually low. The noise from the measuring devices contaminates the reconstruction of the magnetic tracer's trajectory. A filter is then needed to reduce the noise in the final trajectory results. In this work, we present a neural network-based framework for MPT trajectory reconstruction and filtering, which yields accurate results and operates at very high speed. The reconstruction derived from this framework is compared to the state-of-the-art extended Kalman filter-based reconstruction.

Modelling long-term industry energy demand and CO2 emissions in the system context using REMIND (version 3.1.0)

Abstract. This paper presents an extension of industry modelling within the REMIND integrated assessment model to industry subsectors and a projection of future industry subsector activity and energy demand for different baseline scenarios for use with the REMIND model. The industry sector is the largest greenhouse-gas-emitting energy demand sector and is considered a mitigation bottleneck. At the same time, industry subsectors are heterogeneous and face distinct emission mitigation challenges. By extending the multi-region, general equilibrium integrated assessment model REMIND to an explicit representation of four industry subsectors (cement, chemicals, steel, and other industry production), along with subsector-specific carbon capture and sequestration (CCS), we are able to investigate industry emission mitigation strategies in the context of the entire energy–economy–climate system, covering mitigation options ranging from reduced demand for industrial goods, fuel switching, and electrification to endogenous energy efficiency increases and carbon capture. We also present the derivation of both activity and final energy demand trajectories for the industry subsectors for use with the REMIND model in baseline scenarios, based on short-term continuation of historic trends and long-term global convergence. The system allows for selective variation of specific subsector activity and final energy demand across scenarios and regions to create consistent scenarios for a wide range of socioeconomic drivers and scenario story lines, like the Shared Socioeconomic Pathways (SSPs).

Coalescence of limit cycles in the presence of noise.

Complex dynamical systems may exhibit multiple steady states, including time-periodic limit cycles, where the final trajectory depends on initial conditions. With tuning of parameters, limit cycles can proliferate or merge at an exceptional point. Here we ask how dynamics in the vicinity of such a bifurcation are influenced by noise. A pitchfork bifurcation can be used to induce bifurcation behavior. We model a limit cycle with the normal form of the Hopf oscillator, couple it to the pitchfork, and investigate the resulting dynamical system in the presence of noise. We show that the generating functional for the averages of the dynamical variables factorizes between the pitchfork and the oscillator. The statistical properties of the pitchfork in the presence of noise in its various regimes are investigated and a scaling theory is developed for the correlation and response functions, including a possible symmetry-breaking field. The analysis is done by perturbative calculations as well as numerical means. Finally, observables illustrating the coupling of a system with a limit cycle to a pitchfork are discussed and the phase-phase correlations are shown to exhibit nondiffusive behavior with universal scaling.

Neural-network-based automatic trajectory adaptation for quality characteristics control in powder compaction

AbstractFuture manufacturing systems will have to become more intelligent to be able to guarantee a constantly high quality of products while simultaneously reducing labor-intensive quality-assurance tasks to address the shortage in workforce. In this work, we study the application of neural networks to the field of powder metallurgy and more specifically the production of green parts as part of a typical sintering process. More specifically, we explore the usage of neural-network-based predictions in closed-loop control. We train neural networks based on a series of produced workpieces, and use these networks in closed-loop production to predict quality characteristics like weight and dimensions of the workpiece in real-time. Based on these predictions an adaptive trajectory planner adjusts then trajectory key points and with this the final piston trajectories to bring and keep quality characteristics of workpieces within tolerance. We finally compare the control performance of this neural network-based approach with a pure sensor-based approach. Results indicate that both approaches are able to bring and keep quality characteristics within their tolerance limits, but that the neural network-based approach outperforms the sensor-based approach in the transient phase, whereas in steady state the neural network needed to be updated from time to time to reach the same high performance as the sensor-based approach. Since updating needs to be performed only from time to time, required expensive sensors can be shared among multiple machines and thus, costs can be reduced. At the same time the superior prediction performance of the neural-network-based approach in transient phases can be exploited to accelerate setting up times for new workpieces. Future work will target the automation of the recording of the training dataset, the exploration of further machine learning methods as well as the integration of additional sensor data to further improve predictions.

Fusion of Deep Sort and Yolov5 for Effective Vehicle Detection and Tracking Scheme in Real-Time Traffic Management Sustainable System

In recent years, advancements in sustainable intelligent transportation have emphasized the significance of vehicle detection and tracking for real-time traffic flow management on the highways. However, the performance of existing methods based on deep learning is still a big challenge due to the different sizes of vehicles, occlusions, and other real-time traffic scenarios. To address the vehicle detection and tracking issues, an intelligent and effective scheme is proposed which detects vehicles by You Only Look Once (YOLOv5) with a speed of 140 FPS, and then, the Deep Simple Online and Real-time Tracking (Deep SORT) is integrated into the detection result to track and predict the position of the vehicles. In the first phase, YOLOv5 extracts the bounding box of the target vehicles, and in second phase, it is fed with the output of YOLOv5 to perform the tracking. Additionally, the Kalman filter and the Hungarian algorithm are employed to anticipate and track the final trajectory of the vehicles. To evaluate the effectiveness and performance of the proposed algorithm, simulations were carried out on the BDD100K and PASCAL datasets. The proposed algorithm surpasses the performance of existing deep learning-based methods, yielding superior results. Finally, the multi-vehicle detection and tracking process illustrated that the precision, recall, and mAP are 91.25%, 93.52%, and 92.18% in videos, respectively.

Multiagent Soft Actor-Critic Based Hybrid Motion Planner for Mobile Robots.

In this article, a novel hybrid multirobot motion planner that can be applied under no explicit communication and local observable conditions is presented. The planner is model-free and can realize the end-to-end mapping of multirobot state and observation information to final smooth and continuous trajectories. The planner is a front-end and back-end separated architecture. The design of the front-end collaborative waypoints searching module is based on the multiagent soft actor-critic (MASAC) algorithm under the centralized training with decentralized execution (CTDE) diagram. The design of the back-end trajectory optimization module is based on the minimal snap method with safety zone constraints. This module can output the final dynamic-feasible and executable trajectories. Finally, multigroup experimental results verify the effectiveness of the proposed motion planner.

A novel trajectory similarity measurement method based on node‐sequence hierarchical digraph

AbstractTrajectory similarity measurement is a basic and vital task in trajectory data mining, which has attracted extensive research in the past decades. Recent works focused on the sequence and hierarchy property of trajectories to construct similarity measurements. However, these methods ignore the user information on the visiting locations, such as semantic and time distribution. In light of this, a novel trajectory similarity measurement based on Node‐Sequence Hierarchical Digraph (NSHD) framework is proposed in this article. We first propose a Time‐Weighted Stay Point Detection (TWSPD) method to extract real visiting locations of users more accurately. Then, the nodes of digraph are obtained by clustering users' stay points and the edges of digraph are sequence information that users move between these nodes. An Advanced Earth Mover's Distance (AEMD) is proposed to measure the node similarity between users, considering visiting time distribution and semantic information simultaneously. Both node and sequence similarities are used to calculate the similarity score to obtain the final trajectory similarity measurement. Experiments on Geolife and T‐Drive datasets show that our proposed method offers competitive performance with mean reciprocal rank values reaching 96.01 and 81.26%, which outperforms related trajectory similarity measurements by more than 10 and 15%.

Multimodal Global Trajectory Planner for Autonomous Underwater Vehicles

The underwater environment introduces many limitations that must be faced when designing an autonomous underwater vehicle (AUV). One of the most important issues is developing an effective vehicle movement control and mission planning system. This article presents a global trajectory planning system based on a multimodal approach. The trajectory of the vehicle’s movement has been divided into segments between introduced waypoints and calculated in parallel by advanced path planning methods: modified A* method, artificial potential field (APF), genetic algorithm (GA), particle swarm optimisation (PSO), and rapidly-exploring random tree (RRT). The shortest paths in each planned segment are selected and combined to give the resulting trajectory. A comparison of the results obtained by the proposed approach with the path calculated by each method individually confirms the increase in the system’s effectiveness by ensuring a shorter trajectory and improving the system’s reliability. Expressing the final trajectory in the form of geographical coordinates with a specific arrival time allows the implementation of calculation results in mission planning for autonomous underwater vehicles used commercially and in the military, as well as for autonomous surface vehicles (ASVs) equipped with trajectory tracking control systems.

A Three-Dimensional Hail Trajectory Clustering Technique

Abstract Recent advances in hail trajectory modeling regularly produce datasets containing millions of hail trajectories. Because hail growth within a storm cannot be entirely separated from the structure of the trajectories producing it, a method to condense the multidimensionality of the trajectory information into a discrete number of features analyzable by humans is necessary. This article presents a three-dimensional trajectory clustering technique that is designed to group trajectories that have similar updraft-relative structures and orientations. The new technique is an application of a two-dimensional method common in the data mining field. Hail trajectories (or “parent” trajectories) are partitioned into segments before they are clustered using a modified version of the density-based spatial applications with noise (DBSCAN) method. Parent trajectories with segments that are members of at least two common clusters are then grouped into parent trajectory clusters before output. This multistep method has several advantages. Hail trajectories with structural similarities along only portions of their length, e.g., sourced from different locations around the updraft before converging to a common pathway, can still be grouped. However, the physical information inherent in the full length of the trajectory is retained, unlike methods that cluster trajectory segments alone. The conversion of trajectories to an updraft-relative space also allows trajectories separated in time to be clustered. Once the final output trajectory clusters are identified, a method for calculating a representative trajectory for each cluster is proposed. Cluster distributions of hailstone and environmental characteristics at each time step in the representative trajectory can also be calculated. Significance Statement To understand how a storm produces large hail, we need to understand the paths that hailstones take in a storm when growing. We can simulate these paths using computer models. However, the millions of hailstones in a simulated storm create millions of paths, which is hard to analyze. This article describes a machine learning method that groups together hailstone paths based on how similar their three-dimensional structures look. It will let hail scientists analyze hailstone pathways in storms more easily, and therefore better understand how hail growth happens.

Astrodynamics-Informed Kinodynamic Sampling-Based Motion Planning for Relative Spacecraft Motion

This paper presents a sampling-based spacecraft relative motion planning algorithm to reconfigure a spacecraft from a given initial state to a desired final state, subject to kinodynamic (simultaneous kinematic and dynamics) constraints. We leverage theoretical advancement in the field of sampling-based motion planning in robotics and extend their application to achieve motion planning in astrodynamics. We present an astrodynamics-informed kinodynamic motion planning (AIKMP) algorithm that takes advantage of a spacecraft’s natural motion to compute a safe, fuel-efficient motion plan for spacecraft reconfiguration in a cluttered environment. This algorithm is developed in the relative orbital element space of the deputy spacecraft around a chief spacecraft that provides very useful geometric insight to relative spacecraft motion, as well as helps in generating an overall smooth final transfer trajectory without demanding additional postprocessing of the transfer solution. Through several example scenarios, we demonstrate that the AIKMP algorithm is regularly able to compute safe and fuel-efficient solutions to relative transfer problems. We also show that this algorithm iteratively improves on its computed solutions and thus holds the capability of finding near-optimal transfer solutions given a sufficient number of algorithm iterations—without requiring an initial guess of the solution.

Analytical Dependence of the Probability of Masking Objects on the Density and Dispersion of the Aerosol

With the existing method for evaluating the effectiveness of masking objects with aerosols, two parameters are used: the length and width of an invisible smoke screen with a probability of at least 50 %. Both parameters were obtained during the practical tests of aerosol masking means. However, they are insufficient for assessing the masking ability of an aerosol cloud as a spatial formation. The purpose of this work is to reveal the analytical dependence of the probability of objects masking on the density and dispersion of the aerosol. Мaterials and methods. An extended approach was used to estimate the probability of masking at any theoretical value of the flux density (integral concentration, g/m2) of aerosol along the line of sight, taking into account its dispersion, by calculating the formation of a probability field from 5 to 95 % over the entire spatial structure of the aerosol cloud. The method used is the simulation on a PC of the dependence of the share of space occlusion for the observer (the eyepiece of an optical device) by aerosol particles of a given dispersion and flux density, which we took as the probability of masking. Discussion. It is shown in the article, that the resulting analytical expression as a result of processing the accumulated simulation results on a PC fully corresponds to the Bouguer-Lambert-Beer law, which is a generalization of many years of practical field and laboratory experiments with aerosols in the air and dispersed particles in solutions. The obtained probability values allow us to obtain a generalized efficiency criterion in the form of a new concept - the reduced masking zone. This term is mathematically analogous to the reduced impact zone, which is used to assess damage caused by munitions. Conclusion. For a full assessment of the effectiveness of aerosol countermeasures, the reduced masking zone must be calculated for all possible lines of sight (observation of an object): horizontally, vertically, and along inclined paths. This condition reflects the method of using modern weapons such as Javelin anti-tank systems, which are aimed at the target mainly horizontally, and the final trajectory before the impact is a «hill». The theoretical difference between the values of vertical and horizontal masking screens, obtained by the authors using a new method for calculating the parameters of an aerosol cloud, is presented in the illustrations to the article.