Global Trajectory Research Articles

Intersection movements delay modelling based on crowd-sensed global positioning system trajectory data

Developing accurate large-scale transportation models, used to guide policy adoption and evaluate infrastructure alternatives or changes in sociodemographic conditions, is data and resource intensive. This research proposes a novel method for modeling intersection movement delay using crowd-sensed Global Positioning System (GPS) data. This is achieved by providing a general definition of turning movements and extracting travel times thought GPS trajectory data analysis. Additionally, a straightforward method is proposed to integrate the observed delays per movement type into volume-delay functions. The spatial definition provided for turning movements captured distinct speed profiles per turn type. The significant differences in mean speeds for different turn types highlights the importance of integrating turn penalty functions based on real observations and underscore the importance of crowd-sensed GPS data. A simple technique is also proposed to integrate the proposed method into the volume-delay functions used in large scale transport models.

Human skill knowledge guided global trajectory policy reinforcement learning method.

Traditional trajectory learning methods based on Imitation Learning (IL) only learn the existing trajectory knowledge from human demonstration. In this way, it can not adapt the trajectory knowledge to the task environment by interacting with the environment and fine-tuning the policy. To address this problem, a global trajectory learning method which combinines IL with Reinforcement Learning (RL) to adapt the knowledge policy to the environment is proposed. In this paper, IL is proposed to acquire basic trajectory skills, and then learns the agent will explore and exploit more policy which is applicable to the current environment by RL. The basic trajectory skills include the knowledge policy and the time stage information in the whole task space to help learn the time series of the trajectory, and are used to guide the subsequent RL process. Notably, neural networks are not used to model the action policy and the Q value of RL during the RL process. Instead, they are sampled and updated in the whole task space and then transferred to the networks after the RL process through Behavior Cloning (BC) to get continuous and smooth global trajectory policy. The feasibility and the effectiveness of the method was validated in a custom Gym environment of a flower drawing task. And then, we executed the learned policy in the real-world robot drawing experiment.

ConvTEBiLSTM: A Neural Network Fusing Local and Global Trajectory Features for Field-Road Mode Classification

Field-road mode classification (FRMC) that identifies “in-field” and “on-road” categories for Global Navigation Satellite System (GNSS) trajectory points of agricultural machinery containing geographic information is essential for effective crop improvement. Most previous studies utilize local trajectory features (i.e., the relationships between a point and its neighboring points), but they ignore global trajectory features (i.e., the relationships between the point and all points of the trajectory), leading to difficulty in improving the overall classification performance. The global trajectory features are useful for FRMC because they contain rich trajectory information (e.g., mode switching and motion tendency). Therefore, a ConvTEBiLSTM network-based method is proposed to improve the overall performance. Firstly, nine statistical features (e.g., speed and direction) are extracted from the original data and fed into the ConvTEBiLSTM network. Then, the ConvTEBiLSTM network combining the Bidirectional Long Short-Term Memory network, 1D Convolution network, and Transformer-Encoder network is used to extract and fuse local and global trajectory features. Finally, a linear classifier is applied to identify the “field” and “road” categories of GNSS points based on the fused features. Experimental results show that compared with the baselines, our method achieves the best accuracy and F1-score of 97.38% and 92.74% on our Harvester dataset, respectively.

Warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography

High speed photography technique is potentially the most effective way to measure the motion parameter of warhead fragment benefiting from its advantages of high accuracy, high resolution and high efficiency. However, it faces challenge in dense objects tracking and 3D trajectories reconstruction due to the characteristics of small size and dense distribution of fragment swarm. To address these challenges, this work presents a warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography. Firstly, background difference algorithm is utilized to extract the center and area of each fragment in the image sequence. Subsequently, a multi-object tracking (MOT) algorithm using Kalman filtering and Hungarian optimal assignment is developed to realize real-time and robust trajectories tracking of fragment swarm. To reconstruct 3D motion trajectories, a global stereo trajectories matching strategy is presented, which takes advantages of epipolar constraint and continuity constraint to correctly retrieve stereo correspondence followed by 3D trajectories refinement using polynomial fitting. Finally, the simulation and experimental results demonstrate that the proposed method can accurately track the motion trajectories and reconstruct the spatio-temporal distribution of 1.0×103 fragments in a field of view (FOV) of 3.2 m×2.5 m, and the accuracy of the velocity estimation can achieve 98.6%.

How Mobility and Temporal Contexts May Affect Environmental Exposure Measurements: Using Outdoor Artificial Light at Night (ALAN) and Urban Green Space as Examples

Temporal contexts are essential for the derivation of causally relevant environmental exposure in environmental and public health studies. We argue that the proper temporal contexts need further emphasis in the mobility-oriented research paradigm and articulate this issue using a study in Hong Kong. We use people’s exposure to outdoor artificial light at night (ALAN) and green space as two essential examples. We recruited 208 participants from two representative communities in Hong Kong and derived their mobility-oriented environmental exposures from high-resolution remote sensing data and Google Street View imagery. We employed one-standard-deviational ellipses to quantitatively represent participants’ activity spaces, and we further used participants’ seven-day Global Positioning System trajectories to derive their spatiotemporally weighted exposures to green space and outdoor ALAN in different temporal contexts. Multiple t tests were used to examine the disparities in activity spaces and measured exposures in different temporal contexts, and these exposure measurements were then used to predict people’s health outcomes. We found that people’s activity spaces are significantly different in size between day and night, for both weekdays and weekends, and in both geographic contexts. We further observed that improper temporal contexts could lead to significantly different environmental exposure levels and causally irrelevant modeling results.

Direct trajectory optimization of macro-micro robotic system using a Gauss pseudospectral framework

Trajectory planning is a crucial aspect of macro-micro robotic systems (MMRSs), especially when the system has high degrees of freedom (DOFs). In the field of robotic polishing, the MMRS is usually composed of an industrial robot and an end-effector, which is responsible for polishing force control. Therefore, the compliance of the macro-robot can be minimized by trajectory planning to reduce its impact on the micro-robot. This study proposes a trajectory planning strategy based on Gauss pseudospectral method for a 9-DOF MMRS. Different from traditional sequential solution strategies, it can be used to obtain an approximate global optimal trajectory. Firstly, the velocity-level kinematics model of MMRS is built, which comprehensively considers the workpiece placement pose and task redundancy. Secondly, an optimal control model for trajectory planning is developed through an effective variable allocation. On the premise of considering traditional trajectory smoothness constraints, a constraint on manipulability is additionally analyzed to avoid reaching a singular configuration during compliance optimization. Thirdly, a Gauss pseudospectral framework based on the optimal control model is proposed, and the costate mapping theorem is proved. The latter provides a theoretical basis for the efficiency and accuracy of the proposed method. Finally, comparison results demonstrate the effectiveness of the proposed method.

Designing small batteries and adaptive charging strategies for operation on rough terrain

The global trajectory is swiftly progressing towards the adoption of electric vehicles (EVs). Given that batteries constitute a pivotal component within the domain of EV technology, their performance and life have become of utmost importance. Battery life is affected by multiple internal and external factors. This paper addresses real-time challenges associated with using battery packs in dynamic on-road conditions characterized by variable discharge rates, temperatures, depth of discharge, storage conditions, and rough terrains. The study involves designing 2.8kWh battery packs and using them to power e-rickshaws in on-road conditions in places like India. The study shows how minor issues, which are often overlooked during the initial pack design phase, can impact the long-term operational performance of the batteries.In real-world scenarios, users lack control over the discharge of a battery. However, the charging process can be effectively controlled. In this paper, we propose an adaptive slow-charging strategy to prolong the life of the battery pack. The suggested charging algorithm regulates the charging current based on parameters such as battery temperature, state of health, and terminal voltage. To assess the impact of this algorithm, we conducted tests on 2.8kWh battery packs used in e-rickshaws for over two years under real-world driving conditions. The effectiveness of the proposed algorithm is demonstrated by comparing the battery degradation with the standard constant current constant voltage charging. The study also introduces a novel balancing technique involving temporary suspension of balancing and charging currents during voltage measurements, mitigating errors linked to ohmic drops. Additionally, the research examines the degradation caused by parking vehicles for extended periods.

A Greedy-Strategy-Based Iterative Optimization Method for Articulated Vehicle Global Trajectory Optimization in Complex Environments

This paper considers the problem of trajectory planning for articulated vehicles in complex environments. We formulate this problem as an optimal control problem (OCP) and propose a greedy-strategy-based planner. This planner consists of three stages. In stage 1, an IAA* algorithm is proposed to identify the homotopy class. In stage 2, the collision-free tunnels are constructed along the guiding trajectory generated in stage 1 to simplify the intractable collision-avoidance constraints. In stage 3, a greedy-strategy-based iterative optimization (GSIO) framework is designed, which contributes to escaping from local optimums, making the optimization process more targeted, and converging to the global optimum solution quickly, especially in complex tasks. One feature of the proposed planner is that it is suitable for any type of articulated vehicle, and the proposed optimization framework can be used as an open framework to optimize any criterion that can be described explicitly by a polynomial. Furthermore, in the set simulation cases, our work shows significant competitiveness, under the premise of ensuring moderate CPU processing time, our algorithm achieves approximately a 40% performance improvement in optimization effects compared to selected comparative algorithms.

Person group detection with global trajectory extraction in a disjoint camera network

Person group detection refers to the grouping of people with similar spatio-temporal trajectories. In this work, we address the problem of automatically detecting groups of people from disjoint camera views, which has an essential application in public safety but has not been seriously studied. The main challenge of this task is that the sparse distribution of cameras in a large surveillance area makes it difficult to infer and match people’s trajectories across cameras. To address this challenge, we propose a CCRF (Cyclic Conditional Random Fields) based model for cross-camera trajectory extraction, which takes both visual appearance and heterogeneous spatio-temporal information (including camera locations, video capture times, and the map of the surveillance area) as input and infers multiple candidate cross-camera trajectories for each person. Then, for each pair of people, we propose to use a dynamic trajectory warping (DTW) method to measure the similarity of their trajectories. DTW uses visual features to optimize the selection of trajectories and addresses the problem of trajectory length matching. Since there is no existing dataset that can directly support our research, we enrich our previously built Person Trajectory Dataset by adding the person group annotation and then verify the effectiveness of the proposed method on this dataset. The dataset and code are released at https://github.com/zhangxin1995/PTD_GROUP.

Orthogonal Mixed-Effects Modeling for High-Dimensional Longitudinal Data: An Unsupervised Learning Approach.

The linear mixed-effects model is commonly utilized to interpret longitudinal data, characterizing both the global longitudinal trajectory across all observations and longitudinal trajectories within individuals. However, characterizing these trajectories in high-dimensional longitudinal data presents a challenge. To address this, our study proposes a novel approach, Unsupervised Orthogonal Mixed-Effects Trajectory Modeling (UOMETM), that leverages unsupervised learning to generate latent representations of both global and individual trajectories. We design an autoencoder with a latent space where an orthogonal constraint is imposed to separate the space of global trajectories from individual trajectories. We also devise a cross-reconstruction loss to ensure consistency of global trajectories and enhance the orthogonality between representation spaces. To evaluate UOMETM, we conducted simulation experiments on images to verify that every component functions as intended. Furthermore, we evaluated its performance and robustness using longitudinal brain cortical thickness from two Alzheimer's disease (AD) datasets. Comparative analyses with state-of-the-art methods revealed UOMETM's superiority in identifying global and individual longitudinal patterns, achieving a lower reconstruction error, superior orthogonality, and higher accuracy in AD classification and conversion forecasting. Remarkably, we found that the space of global trajectories did not significantly contribute to AD classification compared to the space of individual trajectories, emphasizing their clear separation. Moreover, our model exhibited satisfactory generalization and robustness across different datasets. The study shows the outstanding performance and potential clinical use of UOMETM in the context of longitudinal data analysis.

Almost Global Asymptotic Trajectory Tracking for Fully-Actuated Mechanical Systems on Homogeneous Riemannian Manifolds

Almost Global Asymptotic Trajectory Tracking for Fully-Actuated Mechanical Systems on Homogeneous Riemannian Manifolds

Global asymptotic stability and trajectory structure rules of high-order nonlinear difference equation

<p>In this article, global asymptotic stability and trajectory structure of the following high-order nonlinear difference equation</p><p><disp-formula> <label/> <tex-math id="FE1"> \begin{document}$ z_{n+1} = \frac{z_{n-1}z_{n-2}z_{n-4}+z_{n-1}+z_{n-2}+z_{n-4}+b}{z_{n-1}z_{n-2}+z_{n-1}z_{n-4}+z_{n-2}z_{n-4}+1+b}, \quad n\in N, $\end{document} </tex-math></disp-formula></p><p>are studied, where $ b\in[0, \infty) $ and the initial conditions $ z_{i}\in(0, \infty), i = 0, -1, -2, -3, -4. $ Using the semi-cycle analysis method, in a prime period, a continuous length of positive and negative semi-cycles of any nontrivial solution appears periodically: 2, 3, 4, 6, 12. Moreover, two examples are given to illustrate the effectiveness of theoretic analysis.</p>

An Efficient Global Trajectory Planner for Highly Dynamical Nonholonomic Autonomous Vehicles on 3-D Terrains

An Efficient Global Trajectory Planner for Highly Dynamical Nonholonomic Autonomous Vehicles on 3-D Terrains

English Metamorphosis: Solemnizing a Comparative Post-nationalistic and Modernist Global Trajectory in Grandeur Fictional Works from India, Ireland, and Sweden

English Metamorphosis: Solemnizing a Comparative Post-nationalistic and Modernist Global Trajectory in Grandeur Fictional Works from India, Ireland, and Sweden

Model Predictive Control for Quadcopters with Almost Global Trajectory Tracking Guarantees

Model Predictive Control for Quadcopters with Almost Global Trajectory Tracking Guarantees

Exploiting Equivariance in the Design of Tracking Controllers for Euler-Poincare Systems on Matrix Lie Groups

The trajectory tracking problem is a fundamental control task in the study of mechanical systems. Hamiltonian systems are posed on the cotangent bundle of configuration space of a mechanical system, however, symmetries for the full cotangent bundle are not commonly used in geometric control theory. In this paper, we propose a group structure on the cotangent bundle of a Lie group and leverage this to define momentum and configuration errors for trajectory tracking, drawing on recent work on equivariant observer design. We show that this error definition leads to error dynamics that are themselves “Euler-Poincare like” and use these to derive simple, almost global trajectory tracking control for fully-actuated Euler-Poincare systems on a Lie group state space.

Real-time Trajectory Planning and Tracking Control of Bionic Underwater Robot in Dynamic Environment.

In this article, we study the trajectory planning and tracking control of a bionic underwater robot under multiple dynamic obstacles. We first introduce the design of the bionic leopard cabinet underwater robot developed in our lab. Then, we model the trajectory planning problem of the bionic underwater robot by combining its dynamics and physical constraints. Furthermore, we conduct global trajectory planning for bionic underwater robots based on the temporal-spatial Bezier curves. In addition, based on the improved proximal policy optimization, local dynamic obstacle avoidance trajectory replanning is carried out. In addition, we design the fuzzy proportional-integral-derivative controller for tracking control of the planned trajectory. Finally, the effectiveness of the real-time trajectory planning and tracking control method is verified by comparative simulation in dynamic environment and semiphysical simulation of UWSim. Among them, the real-time trajectory planning method has advantages in trajectory length, trajectory smoothness, and planning time. The error of trajectory tracking control method is controlled around 0.2 m.

Methodological recommendations for entering foreign markets for small and medium-sized agricultural enterprises

The focus of the article is the problem of development of recommendations for small and medium-sized agricultural enterprises on the use of models, approaches and strategies for entering foreign markets in order to improve the performance and competitiveness of their core business. Based on a theoretical review and a set of interviews with a focus group of 36 representatives of Russian agricultural enterprises companies responsible for business internationalization, the study suggests a number critical parameters and aspects which can be reflected in agricultural enterprises’ strategies and approaches to entering international markets. The conducted research generally confirms the applicability of three common models of entering foreign markets for agricultural enterprises – the organic path, the joint growth path and the born global trajectory, however, the interviewed experts note a number of important aspects that are more inherent to agricultural enterprises and should be taken into account in internationalization strategies and models. The study also found that for small and medium-sized agricultural enterprises, the choice between direct and indirect export strategies presents a particular dilemma, since the comparison of the advantages and disadvantages of these two strategies is very contextual due to the high specificity of both the agro-industrial industry itself and the configuration of opportunities and constraints in the domestic and international markets. The author emphasizes the significance of helping small and medium-sized agricultural enterprises to understand determinants and factors of internationalization.

BRICS and the Race to Net- Zero Emissions by 2050: Is COVID-19 a Barrier or an Opportunity?

The emerging economies of Brazil, Russia, India, China and South Africa (BRICS) have significant influence on the global economic and environmental trajectories. They have carbon intensive economic systems, which contribute significantly to total global greenhouse gas (GHG) emissions, leading to climate change. However, BRICS have joined the race to net-zero emissions by 2050 in the quest for a climate neutral and sustainable global economy. The journey, however, is not without challenges and opportunities. The proliferation of the coronavirus disease 2019 (COVID-19) had mixed reactions from scientists regarding its implications on net-zero trajectories. While statistical data show a correlation between COVID-19 and a decrease in total emissions, it is envisaged that COVID-19 compromised the efforts to develop carbon neutral economies. Hence, there is still a need for more scientific examination of COVID-19's impact on net-zero ambitions, especially in the emerging economies. This study focuses on India and South Africa's trajectories. Statistical analysis of secondary data from authentic interactive web-based dashboards for COVID-19 data repositories, namely Our World in Data and Climate Action Tracker was performed in conjunction with the document analysis approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Some of the COVID-19 challenges as indicated by the results of the study include curtailing the technology transfer staircase in the energy sector, retaliatory emissions for recovery and resource diversion. The opportunities presented by COVID-19 in the quest for carbon neutrality include behavioral changes in investment, production and consumption patterns with a focus on low carbon options. Governments and stakeholders need to focus on addressing the barriers whilst riding on the opportunities presented by the pandemic to achieve net-zero emissions by 2050.

Multi-objective trajectory planning for segment assembly robots using a B-spline interpolation- and infeasible-updating non-dominated sorting-based method

The rapid and smooth functioning of segment assembly robots, which is always conflicting, is critical to improving efficiency and ensuring safety during tunneling construction, particularly for the series-actuated robots employed in non-circular shield machines. However, the trade-off between the aforementioned goals has not been explored for trajectory planning in joint space. Less is known about how to acquire superior trade-off Pareto solutions for this constrained multi-objective optimization problem. To fill this gap, this paper proposes a B-spline interpolation- and infeasible-updating non-dominated sorting-based method for multi-objective trajectory planning of shield machine segment assembly robots. In particular, a multi-objective optimization model is detailed in terms of time, acceleration, and jerk of joint trajectories while accounting for operational efficiency and motion smoothness. The given objective functions can be determined using B-spline interpolation and time information based on the known via-points of hydraulic joints. Meanwhile, the constraints are transformed into a limited number of control point-derived forms. Following that, the infeasible-updating non-dominated sorting-based evolutionary algorithm (INSEA) is introduced to solve this problem and find Pareto-optimal solutions. The main improvement is that the multi-objective function information of infeasible solutions is utilized so as to update the non-dominated sorting process, which adaptively applies population division and individual replacement. The findings indicate that the proposed method is capable of executing multi-objective trajectory planning during different stages of the assembly procedure, and the computed metrics are all greater than serval advanced algorithms. Furthermore, on the basis of the proposed INSEA, multi-degree B-spline interpolation provides lower acceleration peaks and smoother global trajectories than common cubic spline curves throughout the process. Therefore, this method can provide researchers with a wide range of alternatives to achieve the optimum trade-off for multi-objective trajectory planning.