Continuous Trajectory Research Articles

A new Contour-Based combined matching algorithm for underwater Terrain-Aided strapdown inertial navigation system

Terrain-aided strapdown inertial navigation system is a geophysical field positioning technique for long-endurance underwater vehicles, whose key technology is the matching algorithm. Aiming at transcending the limitations of current batch matching algorithms in accuracy due to local convergence and neglect of the SINS continuous trajectory information, we propose a two-stage combined matching algorithm. First, a coarse matching position is obtained based on the terrain mean factor search and the maximum likelihood estimation utilizing the multi-beam measured surface elevations. Then we implement the contour-based fine matching by introducing inertial constraint and proportionality factor, and propose a new numerical solution for the trajectory transformation matrix, where the proportionality factor and transformation matrix are both solvable independently. Finally, the simulations and field test results on real grid maps with 10 m resolution show that the proposed algorithm is superior to the previous algorithms and its feasibility in practical applications, and the matching accuracy reaches 1–2 grids.

Specific Gait Changes in Prodromal Hereditary Spastic Paraplegia Type 4: preSPG4 Study.

In hereditary spastic paraplegia type 4 (SPG4), subclinical gait changes might occur years before patients realize gait disturbances. The prodromal phase of neurodegenerative disease is of particular interest to halt disease progression by future interventions before impairment has manifested. The objective of this study was to identify specific movement abnormalities before the manifestation of gait impairment and quantify disease progression in the prodromal phase. Seventy subjects participated in gait assessment, including 30 prodromal SPAST pathogenic variant carriers, 17 patients with mild-to-moderate manifest SPG4, and 23 healthy control subjects. An infrared-camera-based motion capture system assessed gait to analyze features such as range of motion and continuous angle trajectories. Those features were correlated with disease severity as assessed by the Spastic Paraplegia Rating Scale, neurofilament light chain as a fluid biomarker indicating neurodegeneration, and motor-evoked potentials. Compared with healthy control subjects, we found an altered gait pattern in prodromal pathogenic variant carriers during the swing phase in the segmental angle of the foot (Dunn's post hoc test, q=3.1) and heel ground clearance (q=2.8). Furthermore, range of motion of segmental angle was reduced for the foot (q=3.3). These changes occurred in prodromal pathogenic variant carriers without quantified leg spasticity in clinical examination. Gait features correlated with neurofilament light chain levels, central motor conduction times of motor-evoked potentials, and Spastic Paraplegia Rating Scale score. Gait analysis can quantify changes in prodromal and mild-to-moderate manifest SPG4 patients. Thus, gait features constitute promising motor biomarkers characterizing the subclinical progression of spastic gait and might help to evaluate interventions in early disease stages. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Landing a quadrotor UAV on a moving platform with sway motion using robust control

Autonomous landing of quadrotor unmanned aerial vehicles (UAVs) is a critical and challenging issue, especially on a moving target under complex terrain. This paper proposes a practicable solution that consists of a reference trajectory and a robust control strategy. More specifically, a continuous reference trajectory is planned to ensure the safety, where a feasible landing constraint is provided by introducing the sway motion of the target platform. Then, a robust hierarchical control strategy that exploits a saturated force controller and an attitude constraint torque controller is developed, where a dynamics estimator is employed to counteract the uncertain dynamics. Stability analysis indicates that the overall closed-loop system is uniformly ultimately bounded. Since the global positioning system (GPS) fails to provide an acceptable measurement for the precise landing, an ultra-wide band (UWB) positioning system is designed which possesses a 20-centimeter level of accuracy. To simulate a realistic scenario over the rough ground or sea condition, a motion platform with sway motion is utilized in the experiment facility. Flight experiments are performed to validate and assess the proposed strategy.

Detumbling motion planning for the space manipulator with grasped targets and flexible appendages in the post-capturing phase

This paper proposes a detumbling motion planning algorithm for free-flying space manipulator with a grasped tumbling target in the post-capturing phase. This algorithm can not only collision-freely guide the space manipulator and the target to terminal stationary states but also suppress the residual vibration of the flexible appendage on the space manipulator. First, considering the avoidances of self-collisions and motion singularities, a smooth detumbling path is planned for the space manipulator by a proposed smoothing rapid random tree star algorithm (SM-RRT*). Second, a quintic polynomial function is implemented to generate a continuous detumbling trajectory along the detumbling path. Then, with the object of minimizing the residual flexible vibrations and the constrains of joint acceleration limits, an optimization model is established to refine the detumbling trajectory. Finally, the optimization model is solved by an improved particle swarm optimization algorithm (PSO), where a potential field term is included in the generation of the particle velocity to enhance the computational efficiency. Simulation results validate the effectiveness of the proposed detumbling motion planning algorithm.

Preattentive facilitation of target trajectories in a dragonfly visual neuron

The ability to pursue targets in visually cluttered and distraction-rich environments is critical for predators such as dragonflies. Previously, we identified Centrifugal Small-Target Motion Detector 1 (CSTMD1), a dragonfly visual neuron likely involved in such target-tracking behaviour. CSTMD1 exhibits facilitated responses to targets moving along a continuous trajectory. Moreover, CSTMD1 competitively selects a single target out of a pair. Here, we conducted in vivo, intracellular recordings from CSTMD1 to examine the interplay between facilitation and selection, in response to the presentation of paired targets. We find that neuronal responses to both individual trajectories of simultaneous, paired targets are facilitated, rather than being constrained to the single, selected target. Additionally, switches in selection elicit suppression which is likely an important attribute underlying target pursuit. However, binocular experiments reveal these results are constrained to paired targets within the same visual hemifield, while selection of a target in one visual hemifield establishes ocular dominance that prevents facilitation or response to contralaterally presented targets. These results reveal that the dragonfly brain preattentively represents more than one target trajectory, to balance between attentional flexibility and resistance against distraction.

Trajectory Planning and Optimization for Minimizing Uncertainty in Persistent Monitoring Applications

This paper considers persistent monitoring of environmental phenomena using unmanned aerial vehicles (UAVs). The objective is to generate periodic dynamically feasible UAV trajectories that minimize the estimation uncertainty at a set of points of interest in the environment. We develop an optimization algorithm that iterates between determining the observation periods for a set of ordered points of interest and optimizing a continuous UAV trajectory to meet the required observation periods and UAV dynamics constraints. The interest-point visitation order is determined using a Traveling Salesman Problem (TSP), followed by a greedy optimization algorithm to determine the number of observations that minimizes the maximum steady-state eigenvalue of a Kalman filter estimator. Given the interest-point observation periods and visitation order, a minimum-jerk trajectory is generated from a bi-level optimization, formulated as a convex quadratically constrained quadratic program. The resulting B-spline trajectory is guaranteed to be feasible, meeting the observation duration, maximum velocity and acceleration, region enter and exit constraints. The feasible trajectories outperform existing methods by achieving comparable observability at up to 47% higher travel speeds, resulting in lower maximum estimation uncertainty.

THE STAGA-DATASET: STOP AND TRIP ANNOTATED GPS AND ACCELEROMETER DATA OF EVERYDAY LIFE

Abstract. Identifying stops and trips from raw GPS traces is a fundamental preprocessing step for most mobility research applications. Thus, ensuring the excellent accuracy of such systems is of high interest to researchers designing such analysis pipelines. While there are plenty of GPS datasets available, these usually do not provide annotations and thus cannot be used for benchmarking stop/trip classifiers easily. This manuscript introduces a GPS & accelerometer dataset, including accurate stop/trip annotations. It contains 122,808 GPS samples as one continuous trajectory, spanning over 126 days. The recorded time frame includes working days, vacations, travelling, everyday life and all regular modes of transportation. During recording, a detailed mobility diary was conducted to capture each dwelling period’s exact beginning and end. The position and diary data combined contain 78,900 labelled stops and 43,908 labelled trips. This serves as ground truth for stop/trip classification algorithms to test existing tools or develop new analysis methods. The introduced dataset is freely available under a CC-By Attribution 4.0 International license, the annotation tool under the BSD 3-Clause license.

Unmanned mobile robot in unknown obstacle environments for multi switching control tracking using adaptive nonlinear sliding mode control method

This paper focuses on a wheeled mobile robot that utilizes the Adaptive Nonlinear Sliding mode control technique for trajectory tracking and obstacle avoidance control. A parallel wheeled differential drive Mobile robot’s trajectory tracking control problem is investigated. For diverse initial conditions, the robot must follow a given course to reach it’s destination. To monitor and identify the obstacles in the path, an obstacle micro-controller is fixed to decide quick crash avoidance and follow the obstacle limit at a predetermined distance. It depends on the robot’s vector connections. An Adaptive Nonlinear Sliding Mode (ANSM) control concept is used for continuous trajectory tracking or object monitoring in the path to avoid it. A Lapunov function control gives stability to each controller. The proposed simulation results demonstrate that the mobile robot can be applied to guarantee its protected development in an obscure obstacle environment. In detail, the proposed control gives another, more straightforward methodology with application esteems for tracking critical thinking in an obscure obstacle environment. Finally, based on these above characteristics, the proposed control strategy’s efficiency, simplicity, and accuracy prove. The steady-state error and mean squared error of the proposed ANSM are 6% and 0.07db, respectively.

LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures

Over the past 30 years, magnetic resonance imaging has become a ubiquitous tool for accurately visualizing the change and development of the brain’s subcortical structures (e.g., hippocampus). Although subcortical structures act as information hubs of the nervous system, their quantification is still in its infancy due to many challenges in shape extraction, representation, and modeling. Here, we develop a simple and efficient framework of longitudinal elastic shape analysis (LESA) for subcortical structures. Integrating ideas from elastic shape analysis of static surfaces and statistical modeling of sparse longitudinal data, LESA provides a set of tools for systematically quantifying changes of longitudinal subcortical surface shapes from raw structure MRI data. The key novelties of LESA include: (i) it can efficiently represent complex subcortical structures using a small number of basis functions and (ii) it can accurately delineate the spatiotemporal shape changes of the human subcortical structures. We applied LESA to analyze three longitudinal neuroimaging datasets and showcase its wide applications in estimating continuous shape trajectories, building life-span growth patterns, and comparing shape differences among different groups. In particular, with the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we found that Alzheimer’s Disease (AD) can significantly speed the shape change of the lateral ventricle and the hippocampus from 60 to 75 years olds compared with normal aging. Supplementary materials for this article are available online.

Learning-based method for k-space trajectory design in MRI.

Variable density sampling of the k-space in MRI is an integral part of trajectory design. It has been observed that data-driven trajectory design methods provide a better image reconstruction as compared to trajectories obtained from a fixed or a parametric density function. In this paper, a data-driven strategy has been proposed to obtain non-Cartesian continuous k-space sampling trajectories for MRI under the compressed sensing framework (greedy non-Cartesian (GNC)). A stochas-tic version of the algorithm (stochastic greedy non-Cartesian (SGNC)) is also proposed that reduces the computation time. We compare the proposed trajectory with a traveling salesman problem (TSP)-based trajectory and an echo planar imaging-like trajectory obtained by a greedy method called stochastic greedy-Cartesian (SGC) algorithm. The training images are taken from knee images of the fastMRI dataset. It is observed that the proposed algorithms outperform the TSP-based and the SGC trajectories for similar read-out times.

Stochastic Parareal: An Application of Probabilistic Methods to Time-Parallelization

Parareal is a well-studied algorithm for numerically integrating systems of time-dependent differential equations by parallelising the temporal domain. Given approximate initial values at each temporal sub-interval, the algorithm locates a solution in a fixed number of iterations using a predictor-corrector, stopping once a tolerance is met. This iterative process combines solutions located by inexpensive (coarse resolution) and expensive (fine resolution) numerical integrators. In this paper, we introduce a stochastic parareal algorithm aimed at accelerating the convergence of the deterministic parareal algorithm. Instead of providing the predictor-corrector with a deterministically located set of initial values, the stochastic algorithm samples initial values from dynamically varying probability distributions in each temporal sub-interval. All samples are then propagated in parallel using the expensive integrator. The set of sampled initial values yielding the most continuous (smoothest) trajectory across consecutive sub-intervals are fed into the predictor-corrector, converging in fewer iterations than the deterministic algorithm with a given probability. The performance of the stochastic algorithm, implemented using various probability distributions, is illustrated on low-dimensional systems of ordinary differential equations (ODEs). We provide numerical evidence that when the number of sampled initial values is large enough, stochastic parareal converges almost certainly in fewer iterations than the deterministic algorithm, maintaining solution accuracy. Given its stochastic nature, we also highlight that multiple simulations of stochastic parareal return a distribution of solutions that can represent a measure of uncertainty over the ODE solution.

An Online Time-Optimal Trajectory Planning Method for Constrained Multi-Axis Trajectory With Guaranteed Feasibility

Online time-optimal trajectory planning exists in a wide range of applications such as computer numerical control (CNC) manufacturing, robotics and autonomous vehicles. Generally, the methods to generate such time-parameterized trajectory can be categorized as offline methods and online methods. Offline methods tend to generate time-optimal trajectory while it is computationally prohibitive for high frequency real-time applications. Current online methods are computationally efficient but either deal with the sparse waypoint trajectories or sacrifice the time optimality a lot. This letter innovatively proposed a time-optimal switching trajectory index coordination (TOS-TIC) strategy to solve the online time-optimal planning problem for continuous multi-axis trajectories. The proposed method is able to generate time-optimal trajectory for continuous geometric paths while considering high order kinematic constraints. The time-optimality of the trajectory planned by TOS-TIC is nearly the same as the offline planned optimal results. Meanwhile, the proposed method is computationally efficient for online applications. The proposed TOS-TIC planning strategy is an universal approach and firstly able to solve the online time-optimal trajectory planning problem for constrained continuous multi-axis trajectories, which has excellent application prospect in modern automatic industry and intelligent robots/vehicles.

Jacobian Computation for Cumulative B-Splines on SE(3) and Application to Continuous-Time Object Tracking

In this paper we propose a method that estimates the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$SE(3)$</tex-math></inline-formula> continuous trajectories (orientation and translation) of the dynamic rigid objects present in a scene, from multiple RGB-D views. Specifically, we fit the object trajectories to cumulative B-Splines curves, which allow us to interpolate, at any intermediate time stamp, not only their poses but also their linear and angular velocities and accelerations. Additionally, we derive in this work the analytical <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$SE(3)$</tex-math></inline-formula> Jacobians needed by the optimization, being applicable to any other approach that uses this type of curves. To the best of our knowledge this is the first work that proposes 6-DoF continuous-time object tracking, which we endorse with significant computational cost reduction thanks to our analytical derivations. We evaluate our proposal in synthetic data and in a public benchmark, showing competitive results in localization and significant improvements in velocity estimation in comparison to discrete-time approaches.

Five-year weight loss, physical activity, and eating style trajectories after bariatric surgery.

Little research has taken individual variability in weight loss into account. Furthermore, physical activity (PA) and eating style (ES) have been linked only sporadically to weight loss longitudinally. Identify and describe latent classes of weight loss, change of PA, and change of ES up to 5 years after surgery and investigate whether these trajectories are interrelated. Multicenter outpatient clinic. This is a retrospective study of data collected during standard treatment before and up to 5 years after surgery. Latent class growth analysis was used to identify trajectories of weight loss (percent total weight loss), PA (Baecke questionnaire), and ES (Dutch Eating Behavior Questionnaire). A total of 2785 patients were included. Follow-up rate was 84% at 1 year and 34% at 5 years. Analyses revealed 5 weight loss trajectories. Most patients followed an average, fairly stable weight loss trajectory (48%) or an above-average partial-regain trajectory (36%). Other patients followed a low-responder trajectory (9%), a rapid weight loss and weight regain trajectory (6%), or a continued weight loss trajectory (2%). Patients in the most favorable weight loss trajectory were more likely to also follow the most favorable ES trajectories. Patients following the most unfavorable weight loss trajectory were never also in the PA trajectory with an initial great increase in PA. This study distinguishes demographic and behavioral factors that may influence long-term weight loss trajectories after bariatric surgery. Trajectories varied mainly in magnitude and less in the pattern of weight loss over time, suggesting that very deviant patterns are rare.

A pedestrian POS for indoor Mobile Mapping System based on foot-mounted visual–inertial sensors

The mobile mapping system (MMS) is an enabling technology for indoor location-based services. The position and orientation system (POS) is one of the cores of the mobile mapping system. This paper proposes a pedestrian POS solution for infrastructure-free environments based on the fusion of foot-mounted IMU and stereo camera. The structure from motion (SFM) constructs the trajectory and environment under the condition with rich visual textures; the stereo camera detects loop-closure for mitigating the accumulated error; the foot-mounted pedestrian dead reckoning (Foot-PDR) provides reliable continuous trajectories using the IMU when the visual-based system degrades or crashes. The feasibility and reliability of the proposed system were verified by an office building test and an underground parking lot test. The proposed system achieved 0.237 m and 0.227 m position errors in these two scenarios, respectively. Moreover, the system can maintain the average position accuracy of 0.3 m when the camera shortly failed in some areas.

Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging

Previous literature has focused on predicting a diagnostic label from structural brain imaging. Since subtle changes in the brain precede a cognitive decline in healthy and pathological aging, our study predicts future decline as a continuous trajectory instead. Here, we tested whether baseline multimodal neuroimaging data improve the prediction of future cognitive decline in healthy and pathological aging. Nonbrain data (demographics, clinical, and neuropsychological scores), structural MRI, and functional connectivity data from OASIS-3 (N = 662; age = 46–96 years) were entered into cross-validated multitarget random forest models to predict future cognitive decline (measured by CDR and MMSE), on average 5.8 years into the future. The analysis was preregistered, and all analysis code is publicly available. Combining non-brain with structural data improved the continuous prediction of future cognitive decline (best test-set performance: R2 = 0.42). Cognitive performance, daily functioning, and subcortical volume drove the performance of our model. Including functional connectivity did not improve predictive accuracy. In the future, the prognosis of age-related cognitive decline may enable earlier and more effective individualized cognitive, pharmacological, and behavioral interventions.

Gene expression signatures of individual ductal carcinoma in situ lesions identify processes and biomarkers associated with progression towards invasive ductal carcinoma

Ductal carcinoma in situ (DCIS) is considered a non-invasive precursor to breast cancer, and although associated with an increased risk of developing invasive disease, many women with DCIS will never progress beyond their in situ diagnosis. The path from normal duct to invasive ductal carcinoma (IDC) is not well understood, and efforts to do so are hampered by the substantial heterogeneity that exists between patients, and even within patients. Here we show gene expression analysis from > 2,000 individually micro-dissected ductal lesions representing 145 patients. Combining all samples into one continuous trajectory we show there is a progressive loss in basal layer integrity heading towards IDC, coupled with two epithelial to mesenchymal transitions, one early and a second coinciding with the convergence of DCIS and IDC expression profiles. We identify early processes and potential biomarkers, including CAMK2N1, MNX1, ADCY5, HOXC11 and ANKRD22, whose reduced expression is associated with the progression of DCIS to invasive breast cancer.

Automatic Intersection Extraction Method for Urban Road Networks Based on Trajectory Intersection Points

Automatic intersection identification and extraction are an important foundation for urban road network updates and traffic network analysis and modeling. Existing intersection extraction methods based on steering angles and stopping points suffer from inadequate sampling amounts and threshold settings. To address this problem, we propose a road network intersection automatic extraction method based on vehicle trajectory intersection clustering. First, the continuous trajectory segments are extracted from trajectory data based on the sampling interval. Second, the maximum reconstruction error method is developed to extract straight-line trajectory segments from continuous trajectory segments. The overlapped straight-line trajectory segments belonging to the same direction are merged to reduce the number of segments and enhance road network patterns. To further improve the calculation efficiency of the intersection points of straight-line segments, bounding box filtering and orthogonal filtering are used to filter the straight-line trajectory segments that do not have an intersection relationship. Finally, the obtained straight-line segment intersection points are clustered using a density peak clustering algorithm. The road intersections are automatically extracted using the clustering center. The experimental results on real vehicle trajectories in Lianyungang City show that the proposed method performs well on intersection recognition and calculation efficiency.

Rapid optimization of continuous trajectory for multi-target exploration propelled by electric sails

As an innovative spacecraft propellantless propulsion technology, the electric sail can generate electric fields that interact with the solar wind, generating continuous propulsive thrust. However, unlike conventional electric thrusters, the thrust vector of the electric sail is constrained, and the attitude cannot be adjusted quickly. Consequently, the position and velocity of each flight trajectory at the connecting node and the direction of propulsion acceleration must be continuous. In this study, the continuous trajectory optimization of the electric sail–based probe in multi-target exploration is investigated. An integrative Bezier shaping approach (IBSA) is proposed to realize the rapid optimization of a multi-target exploration trajectory with continuous acceleration. The relationship between boundary constraints, intermediate constraints, and Bezier basis function coefficients is derived, transforming the original continuous optimal control problem of multiple stages into a problem of nonlinear programming that naturally satisfies the boundary and intermediate constraints. Numerical simulation results demonstrate that the IBSA can obtain a better interplanetary trajectory with a shorter calculation time than that obtained with the traditional Bezier shaping approach using piecewise optimization. Furthermore, the simulation results are successfully applied to the initial value estimation of the Gauss pseudospectral method (GPM). Compared with the GPM, the proposed shaping approach differs from the objective function by approximately 2%–6%, but requires approximately 1.5% of the computational time. During the preliminary mission design stage, the IBSA can be used for rapid feasibility assessments of different electric sail–based probe mission profiles.

Distributed adaptive optimization-based formation tracking with double parameter projections for multi-agent systems

In this paper, a distributed adaptive optimization-based formation tracking strategy with double parameter projections for multi-agent systems is addressed under a centralized task allocation and distributed task execution (CTA-DTE) framework. Since a pre-described formation strategy is unable to adapt to a complex and dynamic environment, an optimization-based approach is proposed to transfer the formation tracking problem into a time-varying optimization one, subject to some constraints with several time-varying parameters which determine the rule of formation configuration change adaptively. These parameters are computed by a centralized unit and allocated to each agent as a global mission. Furthermore, each agent cooperates with others to execute this mission under a distributed optimization-based strategy, which combines a geometric center observer technology and a novel double parameter projections technology. The former ensures accurate tracking of a continuous reference trajectory. The latter guarantees that all agents enter into a time-varying security region and never escape from it, and meanwhile, all agents are attracted towards the best time-varying formation configuration via a gradient descent with a compensation. Finally, some simulation results are illustrated to verify the effectiveness of the strategy.