Input Trajectories Research Articles

A Novel WTG Method for Predicting Ship Trajectories in the Fujian Inshore Area Based on AIS Data

The increasing congestion in major global maritime routes poses significant threats to international maritime safety, exacerbated by the proliferation of large, high-speed vessels. To improve the detection of abnormal ship behavior, this research employed automatic identification system (AIS) data for ship trajectory forecasting. Traditional methods primarily focus on spatial and temporal correlations but often lack accuracy and reliability. In this study, ship path predictions were enhanced using the WTG model, which combines wavelet transform, temporal convolutional networks (TCN), and gated recurrent units (GRU). Initially, wavelet decomposition was applied to deconstruct the input trajectory time series. The TCN and GRU modules then extracted features from both the time series and the decomposed data. The predicted elements were reassembled using a multi-head attention mechanism and a pooling layer to produce the final predictions. Comparative experiments demonstrated that the WTG model surpasses other models in the accuracy of ship trajectory prediction. The model proposed in this study proves to be reliable for forecasting ship paths, which is crucial for marine traffic management and ensuring safe navigation.

Design of unmanned aerial vehicle formation keeping controller based on improved consistency algorithm design

This article conducts in-depth research on the problems of large gain convergence oscillation and simultaneous application of translational rotation in traditional consistency algorithms. First, based on the traditional consistent formation algorithm, a virtual long machine is introduced, and a new norm is defined to extend the original consistent algorithm to solve the problem of large gain convergence oscillation in the Linearizability algorithm. Secondly, in response to the issue of the inability to maintain the formation caused by the application of translational rotation in traditional consistent formation processes, the maximum distance is introduced as feedback to the reference controller to modify the reference input trajectory to ensure good tracking of the formation during translational rotation. Finally, a joint semi physical simulation was conducted using the open-source flight control Ardupilot and the open-source simulation platform Airsim to further validate the feasibility of the improved algorithm. The results of this study indicate that the proposed algorithm has significant improvements in solving the problems existing in traditional consistency protocols and has good feasibility in practical applications.

Parallelizing process model integration for model predictive control through oracle design and analysis for a Grover’s algorithm-inspired optimization strategy

In model predictive control (MPC), a process dynamic model is utilized to make predictions of the value of the objective function and constraints throughout a prediction horizon. In one method of solving this problem, the time required to find the optimal values of the decision variables depends on the time required to perform the arithmetic operations involved in computing the model predictions. Methods for attempting to reduce the computation time of an MPC could then include developing approximate (reduced-order or data-driven) models for a system that take less time to solve, or to parallelize the computations using, for example, multiple cores or CPU’s. However, an observation in all of these cases is that the values of the process states across the prediction horizon are not the values returned by the optimization problem; the manipulated input trajectory is the desired decision variable. An optimization strategy that cannot explicitly return the process states but can generate some representation of them that then leads to computation of the desired process input would thus be suitable for MPC. Quantum computers achieve their parallelism through creating values that cannot all be returned. They can then operate on this set of values to return a number that is meaningful with respect to that set of values that could not all be returned. Motivated by this, we wish to investigate an idea for utilizing quantum parallelism in developing a representation of an objective function that depends on the solution of a process dynamic model, but then only returning the control actions that minimize the objective function value dependent on those solutions. To do this, several steps are necessary. The first is to locate a quantum algorithm which has the desired characteristics for achieving the goals. In this work, we perform these steps using an amplitude amplification strategy based on Grover’s algorithm on a quantum computer. The second is to analyze the algorithm with respect to its ability to translate across problems in the MPC domain, with respect to both its ability to handle nonlinear systems and to handle a variety of different structures of the set of all possible objective function values given the allowable values of the decision variables. We thus evaluate the benefits and limitations of the algorithm from this perspective. We do not wish to imply that this algorithm is more computationally-tractable for use with MPC than classical optimization techniques traditionally applied in an MPC context. Rather, we wish to understand the manner in which such an algorithm would be designed and when it is appropriate for MPC problems (in the sense of returning the correct answers to the optimization problem), as a step toward better understanding the interactions of the quantum properties of quantum algorithms with control goals. We also see this as forming an important first step in algorithm design/analysis, which can then translate to future works in comparing this technique computationally with classical “competitor” algorithms to guide further work in searching for relevant quantum algorithms for control applications.

Real-time rear-end conflict prediction on congested highways sections using trajectory data

Predicting rear-end conflicts in advance can avoid potential crashes and significantly improve road safety, especially in congested road sections. Many existing studies adopt macroscopic aggregated traffic flow state features and or environment features for rear-end conflicts prediction, which seems to overlook the impact of the temporal trends of various features during the conflict process on the outcomes. Thus, this paper uses microscopic trajectory data of front and rear vehicles for conflict prediction and explored the impact of trajectory changes trend on conflicts formation. A Gated Recurrent Unit (GRU) is employed to learn and encode conflict and non-conflict trajectory data and perform binary classification. The model has a 93 % recall and a 1.41 % false alarm rate. The Local Interpretable Model-agnostic Explanations (LIME) tool also explains the relationships between predicted conflict probability and input microscopic trajectory data. From the time analysis of the input trajectory using LIME, the following conclusions can be drawn. In congested road segments, when the speed of the leading vehicle is below 3 m/s and the speed of the following vehicle is above 4 m/s, it has a significant positive effect on the occurrence of conflicts. And some aggressive acceleration behaviors of drivers have the positive effect also. In addition, the reasons for conflicts among most vehicles are identical Because their feature distributions are similar. These findings can provide targeted insights for the management of ATM in congested road segments.

NRIMD, a Web Server for Analyzing Protein Allosteric Interactions Based on Molecular Dynamics Simulation.

Long-range allosteric communication between distant sites and active sites in proteins is central to biological regulation but still poorly characterized, limiting the development of protein engineering and drug design. Addressing this gap, NRIMD is an open-access web server for analyzing long-range interactions in proteins from molecular dynamics (MD) simulations, such as the effect of mutations at distal sites or allosteric ligand binding at allosteric sites on the active center. Based on our recent works on neural relational inference using graph neural networks, this cloud-based web server accepts MD simulation data on any length of residues in the alpha-carbon skeleton format from mainstream MD software. The input trajectory data are validated at the frontend deployed on the cloud and then processed on the backend deployed on a high-performance computer system with a collection of complementary tools. The web server provides a one-stop-shop MD analysis platform to predict long-range interactions and their paths between distant sites and active sites. It provides a user-friendly interface for detailed analysis and visualization. To the best of our knowledge, NRIMD is the first-of-its-kind online service to provide comprehensive long-range interaction analysis on MD simulations, which significantly lowers the barrier of predictions on protein long-range interactions using deep learning. The NRIMD web server is publicly available at https://nrimd.luddy.indianapolis.iu.edu/.

Sensitivity of principal component analysis outcomes to data pre-processing conditions when quantifying trial-to-trial variability in whole-body kinematics

This study investigated whether modes of variance in trial-to-trial whole-body kinematic variability identified by principal component analysis (PCA) were consistent across data pre-processing conditions generated from a common dataset. Comparisons made included 1) when trajectory data was expressed in a global vs. local reference frame; 2) when the number of landmarks used to represent whole-body motion differed, and; 3) whether input trajectory data were normalized to participant stature. Varying data pre-processing conditions prior to PCA does not bias the total variance identified. However, it can influence how modes of variance are dispersed across PCs, which in turn, can influence interpretation.

Precision motion control of rotary flexible link manipulators using polynomial input trajectories and feedback control

This paper aims to develop a high-precision controller for a flexible link manipulator using polynomial reference, feedforward input, and feedback control. The control objective is to move the tip of the flexible link to a target position as quickly as possible without exciting its resonant modes. Upon a frequency response-based system identification, a frequency-domain feedback controller is designed for reference tracking. The controller includes a proportional-integral controller, a lead filter, and a set of notch filters to control the rigid body and the flexible link dynamics. A set of polynomial reference and feedforward input trajectories are then designed to improve the tracking performance and minimize residual vibrations. The conventional polynomial reference is generated based on the rigid body motion of the system. A revised polynomial reference generation method is then developed by adding flexible motions to the conventional reference. Simulations and experimental tests indicate significant improvements in the performance of the control system using the revised polynomial trajectories.

Research to Simulate the Ship’s Vibration Regeneration System using a 6-Degree Freedom Gough-Stewart Parallel Robot

This article presents research results on building a model to reproduce ship vibrations based on a Parallel robot with 6 degrees of freedom Gough – Stewart form. Vibration data at the ship’s center of gravity calculated by simulation software will be the input to the model. The regenerative control system uses a simple PID controller to control input trajectory tracking. Simulation results on Matlab/Simulink software have demonstrated the reproduction of ship vibrations within the allowable error.

Dual-mode Antenna Tracking System for Rocket Launch Applications

Rocket launches are complex events that require tracking antennas to maintain a communication link. This study introduces a hybrid tracking strategy that combines manual and program modes by utilizing a predetermined trajectory of the rocket. Automatic switching between tracking modes ensures ongoing monitoring, even during unexpected trajectory changes with the monopulse approach. The dual parabolic antenna arrangement enables this switching. The system estimates the monopulse ratio from the signal strength of each antenna, allowing automatic program tracking to shift to manual mode when reception concerns arise. Performance evaluations included manual, programmable, and dual-mode tests. The system responded to human input and automatically aligned the antenna with slight elevation errors during the initial phase. Adjusting the initial elevation reduced the error. The mode transition was examined by measuring the antenna radiation patterns and monopulse ratio. The system’s performance was evaluated in rocket launches, with the rocket trajectory input into the graphical user interface. The antenna exhibited an azimuthal movement of up to 10 °, and the ratio fluctuation values remained within the antenna’s field of view. After 8.8 seconds, the mode switched from program to manual, indicating that the functioning of the system’s functioning was stable.

Fall prediction, control, and recovery of quadruped robots

When legged robots perform complex tasks in unstructured environments, falls are inevitable due to unknown external disturbances. However, current research mainly focuses on the locomotion control of legged robots without falling. This paper proposes a comprehensive decision-making and control framework to address the falling over of quadruped robots. First, a capturability-based fall prediction algorithm is derived for planar single-contact and 3D multi-contact locomotion with a predefined gait sequence. For safe fall control, a novel contact-implicit trajectory optimization method is proposed to generate both state and input trajectories and contact mode sequences. Specifically, incorporating uncertainty into the system and terrain models enables mitigating the non-smoothness of contact dynamics while improving the robustness of the resulting trajectories. Furthermore, a model-free deep reinforcement learning-based approach is presented to achieve fall recovery after the robot completes a fall. Experimental results demonstrate that the proposed fall prediction algorithm accurately predicts robot falls with up to 95% accuracy approximately 395ms in advance. Compared to classical locomotion controllers, which often struggle to maintain balance under significant pushes or terrain perturbations, the presented framework can autonomously switch to the fall controller approximately 0.06s after the perturbation, effectively preventing falls or achieving recovery with a threefold reduction in touchdown impact velocity. These findings highlight the effectiveness of the proposed framework in enhancing the stability and safety of legged robots in unstructured environments.

Pneumatic servo position control optimization using adaptive-domain prescribed performance control with evolutionary mating algorithm

Pneumatic servo systems face challenges such as friction, compressibility, and nonlinear dynamics, necessitating advanced control techniques. Research suggests model-based, model-free, hybrid, and optimization-based methods have their strengths. Therefore, this study presents an optimal control strategy using Adaptive Domain Prescribed Performance Control (AD-PPC) cascaded with PID and optimized using the Evolutionary Mating Algorithm (EMA) for a pneumatic servo system (PSS). The goal is to achieve faster transient control and stable rod-piston positioning with minimal friction through the hysteresis phenomenon of the targeted proportional valve-controlled double-acting pneumatic cylinder (PPVDC) representing the PSS. The novel EMA optimizes the cascaded controller based on the tracking error as its objective function. Simulation studies verify the proposed AD-PPC-PID controller with the PPVDC model plant, iteratively optimized by the EMA. The analytical study compares this setup's control system and optimization model with the same control system model using alternative optimization methods. The testing employs step and multi-step signals for PPVDC's rod-piston position input. Results show that the EMA-tuned AD-PPC-PID outperforms AD-PPC-PID controller with other optimizers. For both input trajectory tests, EMA-tuned AD-PPC-PID shows faster response times, with average improvements of 30 % in settling times and 70 % in tracking performance metrics compared to other optimizers, making it robust for nonlinear system applications like PPVDC rod-piston positioning.

Convex distributed robust model predictive control for collision and obstacle avoidance

AbstractThe article proposes a convex distributed robust model predictive control algorithm for collision avoidance in multi‐agent systems with additive disturbances, which utilizes the concept of tube model predictive control to address the disturbances. To tackle the coupled collision avoidance constraints, each agent integrates assumed nominal position and input trajectories from its neighbors, rather than relying on actual ones. Compatibility constraints are then designed using the normal vector of the constructed separating hyperplanes for collision avoidance and the residual collision avoidance margin of the optimal solutions at the last time step to restrict deviations between assumed and actual trajectories and ensure consistency among the agents. The nonconvex collision and obstacle avoidance constraints are convexified using the concept of safe sets, transforming them into time‐varying closed polyhedral constraints while accounting for the impact of disturbances. Particularly, the residual collision avoidance margin is incorporated into the construction of safe sets for the satisfaction of coupled collision avoidance constraints. Consequently, the original collision avoidance optimal control problems can be efficiently and simultaneously solved for all agents using standard quadratic programming techniques. Under the design of local terminal constraint sets for each agent, the proposed algorithm ensures a rigorous analysis of robust constraint satisfaction for collision avoidance constraints, as well as an assessment of recursive feasibility and closed‐loop stability. The effectiveness of the algorithm is demonstrated through an illustrative example, and simulation results validate its ability to successfully achieve collision and obstacle avoidance even in the presence of disturbances.

Trajectory classification to support effective and efficient field-road classification.

Field-road classification, which automatically identifies in-field activities and out-of-field activities in global navigation satellite system (GNSS) recordings, is an important step for the performance evaluation of agricultural machinery. Although several field-road classification methods based only on GNSS recordings have been proposed, there is a trade-off between time consumption and accuracy performance for such methods. To obtain an optimal balance, it is important to choose a suitable field-road classification method for each trajectory based on its GNSS trajectory quality. In this article, a trajectory classification task was proposed, which classifies the quality of GNSS trajectories into three categories (high-quality, medium-quality, or low-quality). Then, a trajectory classification (TC) model was developed to automatically assign a quality category to each input trajectory, utilizing global and local features specific to agricultural machinery. Finally, a novel field-road classification method is proposed, wherein the selection of field-road classification methods depends on the trajectory quality category predicted by the TC model. The comprehensive experiments show that the proposed trajectory classification method achieved 86.84% accuracy, which consistently outperformed current trajectory classification methods by about 2.6%, and the proposed field-road classification method has obtained a balance between efficiency and effectiveness, i.e., sufficient efficiency with a tolerable accuracy loss. This is the first attempt to examine the balance problem between efficiency and effectiveness in existing field-road classification methods and to propose a trajectory classification specific to these methods.

End-to-end model-based trajectory prediction for ro-ro ship route using dual-attention mechanism.

With the rapid increase of economic globalization, the significant expansion of shipping volume has resulted in shipping route congestion, causing the necessity of trajectory prediction for effective service and efficient management. While trajectory prediction can achieve a relatively high level of accuracy, the performance and generalization of prediction models remain critical bottlenecks. Therefore, this article proposes a dual-attention (DA) based end-to-end (E2E) neural network (DAE2ENet) for trajectory prediction. In the E2E structure, long short-term memory (LSTM) units are included for the task of pursuing sequential trajectory data from the encoder layer to the decoder layer. In DA mechanisms, global attention is introduced between the encoder and decoder layers to facilitate interactions between input and output trajectory sequences, and multi-head self-attention is utilized to extract sequential features from the input trajectory. In experiments, we use a ro-ro ship with a fixed navigation route as a case study. Compared with baseline models and benchmark neural networks, DAE2ENet can obtain higher performance on trajectory prediction, and better validation of environmental factors on ship navigation.

Data-Driven Inverse Reinforcement Learning Control for Linear Multiplayer Games.

This article proposes a data-driven inverse reinforcement learning (RL) control algorithm for nonzero-sum multiplayer games in linear continuous-time differential dynamical systems. The inverse RL problem in the games is solved by a learner reconstructing the unknown expert players' cost functions from demonstrated expert's optimal state and control input trajectories. The learner, thus, obtains the same control feedback gains and trajectories as the expert, only using data along system trajectories without knowing system dynamics. This article first proposes a model-based inverse RL policy iteration framework that has: 1) policy evaluation step for reconstructing cost matrices using Lyapunov functions; 2) state-reward weight improvement step using inverse optimal control (IOC); and 3) policy improvement step using optimal control. Based on the model-based policy iteration algorithm, this article further develops an online data-driven off-policy inverse RL algorithm without knowing any knowledge of system dynamics or expert control gains. Rigorous convergence and stability analysis of the algorithms are provided. It shows that the off-policy inverse RL algorithm guarantees unbiased solutions while probing noises are added to satisfy the persistence of excitation (PE) condition. Finally, two different simulation examples validate the effectiveness of the proposed algorithms.

Space Robot Sensor Noise Amelioration Using Trajectory Shaping.

Robots in space are necessarily extremely light and lack structural stiffness resulting in natural frequencies of resonance so low as to reside inside the attitude controller's bandwidth. A variety of input trajectories can be used to drive a controller's attempt to ameliorate the control-structural interactions where feedback is provided by low-quality, noisy sensors. Traditionally, step functions are used as the ideal input trajectory. However, step functions are not ideal in many applications, as they are discontinuous. Alternative input trajectories are explored in this manuscript and applied to an example system that includes a flexible appendage attached to a rigid main body. The main body is controlled by a reaction wheel. The equations of motion of the flexible appendage, rigid body, and reaction wheel are derived. A benchmark feedback controller is developed to account for the rigid body modes. Additional filters are added to compensate for the system's flexible modes. Sinusoidal trajectories are autonomously generated to feed the controller. Benchmark feedforward whiplash compensation is additionally implemented for comparison. The method without random errors with the smallest error is the sinusoidal trajectory method, which showed a 97.39% improvement when compared to the baseline response when step trajectories were commanded, while the sinusoidal method was inferior to traditional step trajectories when sensor noise and random errors were present.

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.

Controller Design for Air Conditioner of a Vehicle with Three Control Inputs Using Model Predictive Control

Fuel consumption optimization is a critical field of research within the automotive industry to meet consumer expectations and regulatory requirements. A reduction in fuel consumption can be achieved by reducing the energy consumed by the vehicle. Several subsystems contribute to the overall energy consumption of the vehicle, including the air conditioning (A/C) system. The loads within the A/C system are mainly contributed by the compressor, condenser fan, and underhood aerodynamic drag, which are the components targeted for overall vehicle energy use reduction in this paper. This paper explores a new avenue for A/C system control by considering the power consumption due to vehicle drag (regulated by the condenser fan and active grille shutters (AGS)) to reduce the energy consumption of the A/C system and improve the overall vehicle fuel economy. The control approach used in this paper is model predictive control (MPC). The controller is designed in Simulink, where the compressor clutch signal, condenser fan speed, and AGS open-fraction are inputs. The controller is connected to a high-fidelity vehicle model in Gamma Technologies (GT)-Suite (which is treated as the real physical vehicle) to form a software-in-the-loop simulation environment, where the controller sends actuator inputs to GT-Suite and the vehicle response is sent back to the controller in Simulink. Quadratic programming is used to solve the MPC optimization problem and determine the optimal input trajectory at each time step. The results indicate that using MPC to control the compressor clutch, condenser fan, and AGS can provide a 37.6% reduction in the overall A/C system energy consumption and a 32.7% reduction in the error for the air temperature reference tracking compared to the conventional baseline proportional integral derivative control present in the GT-Suite model.

Inverse Value Iteration and Q -Learning: Algorithms, Stability, and Robustness.

This article proposes a data-driven model-free inverse Q -learning algorithm for continuous-time linear quadratic regulators (LQRs). Using an agent's trajectories of states and optimal control inputs, the algorithm reconstructs its cost function that captures the same trajectories. This article first poses a model-based inverse value iteration scheme using the agent's system dynamics. Then, an online model-free inverse Q -learning algorithm is developed to recover the agent's cost function only using the demonstrated trajectories. It is more efficient than the existing inverse reinforcement learning (RL) algorithms as it avoids the repetitive RL in inner loops. The proposed algorithms do not need initial stabilizing control policies and solve for unbiased solutions. The proposed algorithm's asymptotic stability, convergence, and robustness are guaranteed. Theoretical analysis and simulation examples show the effectiveness and advantages of the proposed algorithms.

GraphMM: Graph-Based Vehicular Map Matching by Leveraging Trajectory and Road Correlations

Map matching of sparse vehicle trajectories is a fundamental problem in location-based services, such as traffic flow analysis and vehicle routing. Existing literature mainly relies on sequence-to-sequence (Seq2Seq) models to capture the intra-trajectory correlation of an input trajectory and to sequentially predict the matched road segments. Due to the limited expressive capability of sequential models, these methods fall short of extracting inter-trajectory and trajectory-road correlations as well as correlation between road segments. We present <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GraphMM</b> , a graph-based approach that explicitly utilizes all aforementioned correlations. Our model exploits the graph nature of map matching and incorporates graph neural networks and conditional models to leverage both road and trajectory graph topology, while manages to align road segments and trajectories in latent space. We formally analyze the expressive power of our model in capturing various correlations and propose efficient algorithms for model training and inference. In particular, our optimization techniques dramatically reduce the computational complexity, making our model feasible on datasets with thousands of road segments. Extensive experiments show that our model significantly enhances prediction accuracy, while improving training and inference efficiency by up to an order of magnitude over both the industrial implementation of the hidden Markov model and state-of-the-art Seq2Seq-based methods.