Velocity Information Research Articles

Tactile control for object tracking and dynamic contour following

We live in a constantly changing world. For robots to fully operate in our world, they need to work in dynamic environments where objects are not fixed in place or may be moved by humans or other agents. This work is based on tactile sensing, as it enables sufficiently responsive robotic systems for contact-based tasks in dynamic environments. Our proposed approach is divided into two parts: (1) a way to perform object following using a shear controller that minimises tactile shear deformation and (2) a switching controller that alternates between the shear controller and a tactile exploration controller that enables contour-following of a moving object. We find that during the object-following task, the robot follows the moving object to sub-millimetre accuracy over a ≈72 mm range for 5 different velocities in 2D. The switching controller successfully performs 2D contour following on several moving objects at various object speeds whilst keeping an almost constant speed of exploration. We expect our method for minimising sensor deformation using a simple controller will generalise over different kinds of contact scenarios for moving objects. Moreover, the switching controller provides an architecture where velocity information of moving objects is fused with another controller thereby enabling a more holistic use of tactile information to empower robotic systems to perform complex tactile tasks.

Dynamic Multi-Target Self-Organization Hunting Control of Multi-Agent Systems

In this paper, we present a novel coordinated method tailored to address the dynamic multi-target hunting control problem in multi-agent systems, offering significant practical value. Our approach encompasses several key components: initially, we introduce a task allocation model that integrates a fuzzy inference system with a particle swarm optimization algorithm. This hybrid model efficiently allocates hunting tasks for scattered evading targets, effectively transforming the dynamic multi-target hunting problem into multiple dynamic single-target-hunting problems. This transformation enhances the speed and efficacy of task allocation. Subsequently, we propose an attraction/repulsive model grounded in potential field theory. This model facilitates the coordinated hunting of each target by organizing agents into subgroups. Relying solely on relative position and velocity information between agents and targets, our model simplifies computation, while maintaining effectiveness. Furthermore, the coordination of hunting activities for each target is achieved through a series of agent subgroups, guided by our proposed motion model. This systematic approach ensures a cohesive and efficient hunting strategy. Finally, we validate the effectiveness and feasibility of our proposed method through simulation results. These results provide empirical evidence of the method’s efficacy and potential applicability in real-world scenarios.

Dynamic Occupancy Grid Map with Semantic Information Using Deep Learning-Based BEVFusion Method with Camera and LiDAR Fusion.

In the field of robotics and autonomous driving, dynamic occupancy grid maps (DOGMs) are typically used to represent the position and velocity information of objects. Although three-dimensional light detection and ranging (LiDAR) sensor-based DOGMs have been actively researched, they have limitations, as they cannot classify types of objects. Therefore, in this study, a deep learning-based camera-LiDAR sensor fusion technique is employed as input to DOGMs. Consequently, not only the position and velocity information of objects but also their class information can be updated, expanding the application areas of DOGMs. Moreover, unclassified LiDAR point measurements contribute to the formation of a map of the surrounding environment, improving the reliability of perception by registering objects that were not classified by deep learning. To achieve this, we developed update rules on the basis of the Dempster-Shafer evidence theory, incorporating class information and the uncertainty of objects occupying grid cells. Furthermore, we analyzed the accuracy of the velocity estimation using two update models. One assigns the occupancy probability only to the edges of the oriented bounding box, whereas the other assigns the occupancy probability to the entire area of the box. The performance of the developed perception technique is evaluated using the public nuScenes dataset. The developed DOGM with object class information will help autonomous vehicles to navigate in complex urban driving environments by providing them with rich information, such as the class and velocity of nearby obstacles.

Relating ciliary propulsion morphology and flow to particle acquisition in marine planktonic ciliates II: the oligotrich ciliate Strombidium capitatum

Abstract The marine oligotrich ciliate Strombidium capitatum is a cruise-feeder, relying on ciliary motion and propulsion flow to individually detect and capture particles. High-speed, high-magnification digital imaging revealed that the cell swims forward by sweeping its anterior adoral membranelles (AAMs) backward, achieving a mean path-averaged speed of U = 1.7 mm s−1 (31 cell-lengths per second). Particle detection occurs through either hydrodynamic signal perception or ciliary contact perception, with a mean reaction distance of R = 20.4 μm. While executing a ciliary reversal of AAMs to handle and capture a perceived particle, the cell coordinates the ciliary motion of ventral adoral membranelles (VAMs, the “lapel”) with the ciliary reversal of AAMs (the “collar”), causing a sudden halt of cell motion, thereby functioning as a motion “brake” that is crucial for effective particle capture. The encounter rate with small prey particles is calculated using πR2U (~8.0 μL h−1, equivalent to ~ 3.5 × 106 cell volumes per day). Based on hydrodynamic modeling results, it is hypothesized that spatial structures of the flow velocity vector and acceleration fields in front of the swimming cell are essential for pushing an embedded particle forward, creating a strong enough slip velocity and hydrodynamic signal for prey perception, even for a neutrally buoyant small particle.

Adaptive neural network control of robotic manipulators with input constraints and without velocity measurements

AbstractThis paper addresses the trajectory tracking problem for a class of uncertain manipulator systems under the effect of external disturbances. The main challenges lie in the input constraints and the lack of measurements of joint velocities. An extend‐state‐observer is utilized to estimate the velocity signals; then, a neural‐network‐based adaptive controller is proposed to solve the problem, where a term based on the nominal model is included to enhance the tracking ability, and the effect of uncertainties and disturbances are compensated by a neural‐network term. Compared with the existing methods, the main distinctive features of the presented approach are: (i) The control law is guaranteed to be bounded by design, instead of directly bounded by a saturation function. (ii) The trade‐off between the performance and robustness of the presented controller can be easily tuned by a parameter that depends on the size of model uncertainties and external disturbances. By virtue of the Lyapunov theorem, the convergence properties of the proposed controller are rigorously proved. The performance of the controller is validated via both simulations and experiments conducted on a two‐degree‐of‐freedom robot manipulator.

Finite-time control of uncertain Euler–Lagrange systems using ELM-based velocity observer

This paper is concerned with the finite-time trajectory tracking control of a class of Euler–Lagrange (EL) systems under unknown velocity information, dynamic uncertainties and disturbances. An extreme learning machine (ELM) algorithm is employed to approximate the uncertainties, while an adaptive algorithm is proposed to tune output weights of the ELM, as well as to eliminate the negative effects of residual errors and disturbances. Then an adaptive finite-time ELM-based velocity observer is developed to estimate the unavailable velocity states. Further, based on the estimations and the approximations of model uncertainties, an adaptive finite-time observer-based nonsingular terminal sliding mode (TSM) control strategy is constructed to guarantee the finite-time bounded tracking of the uncertain EL system by using the Lyapunov stability theorem. Simulation results on a robotic manipulator platform demonstrate the efficiency of the developed finite-time observation and control methods.

Novel deep learning methods for 3D flow field segmentation and classification

Flow field segmentation and classification help researchers to understand vortex structure and thus turbulent flow. Existing deep learning methods are mainly based on global vorticity information and focused on 2D circumstance. In this paper, based on flow field theory, criteria for flow field segmentation and classification in 3D space using local velocity information and the physical relationship between local vorticity and vortex wake are conducted, and then novel physical local information-embedded flow field segmentation and classification methods in 3D space using Multilayer Perceptron (MLP) are proposed. Our methods consist of three modules: a preprocessing module, a flow field segmentation module and a flow field classification module. The preprocessing module calculates input for succeeding modules based on the above criteria. The segmentation and classification modules process corresponding input to identify vortex structure, and further classify the type of vortex wakes in a 3D flow field. Experimental results show that our method can identify vortex area with an accuracy rate that reaches 90% while reducing time consumption by more than 90% compared with existing segmentation methods; our classification method reaches an accuracy rate of 99.7% while reducing time consumption by more than 90% compared with existing classification methods. Our proposed methods proved to achieve good performance both on accuracy and efficiency.

A flow-reconstruction based approach for the computation of hydrodynamic stresses on immersed body surface

This study focuses on obtaining reliable hydrodynamic stresses on the surface of rigid bodies in the simulation of fluid-solid interactions using the diffuse-interface immersed boundary method (IBM). In order to obtain the pressure and velocity gradient information on the immersed boundary, we employ a model to reconstruct the near-boundary fluid flow along the surface normal direction. Different from most existing flow reconstruction models in diffuse-interface IBMs based on the boundary layer approximation, we propose a new reconstruction model relying only on the non-slip velocity and local flow information. With this improvement, the present reconstruction model is theoretically applicable to fluid flows with intermediate Reynolds numbers (with the existence of a laminar boundary layer) and low Reynolds numbers (in the absence of a boundary layer). To examine the validity of the present reconstruction model, we conduct a series of simulations, including the uniform flow past a fixed body and the settling of a rigid body in a quiescent fluid. It is demonstrated that the present reconstruction model has a better performance than existing models in predicting hydrodynamic stresses on the immersed boundary within a wide range of Reynolds numbers from O(1) to O(100). The capability and robustness of the proposed model demonstrate its potential to address fluid-solid interaction problems with a-priori unknown or time-varying Reynolds numbers, such as fully resolved simulations of particle laden flows.

Experimental investigation of closed-loop active control to modulate coherent structures by mu-level method

Abstract The experimental research on zero-net-mass-flux jet closed-loop active control was conducted in the wind tunnel.The mu-level method successfully detected burst events of the coherent structures. The streamwise velocity signals in the turbulent boundary layer were measured by HWA. The drag reduction rate of 16.7% is obtained comparable to that of the open-loop control and saves 75% of the input energy at the ASYN 100V/160 Hz control case, which reflects the advantages of the closed-loop control.The experimental findings indicate that the intensity increases in the near-wall region. The perturbation of the PZT vibrators on skewness factor is concentrated in the region y + < 60 . The generation of high-speed fluids is depressed and the downward effect of high-speed fluids weakens. The alteration of energy distribution and the discernible impact of modulation between structures of varying scales is observed. The correlation coefficient exhibits a strong positive correlation, which indicates that the large-scale structures produce modulation effect on small-scale ones. The occurrence of burst events is effectively suppressed.The disturbance has the characteristics of stable periodicity, positive and negative symmetry, low intermittency and high pulsation strength.The conditional phase waveform shows the fluctuation amplitude increases, indicating amplitude modulation effects on coherent structures.

Changing magma recharge/discharge dynamics during the 2020–22 lava fountaining activity at Mt. Etna revealed by tilt deformation and volcanic tremor

Mt. Etna exhibited 62 lava fountaining events between December 13, 2020 and February 21, 2022. We analyzed tilt deformations and volcanic tremor amplitude time series, to characterize both eruptions and the preceding preparatory phases in terms of magnitude and speed of development of the volcanic phenomena, as well as to reconstruct the processes that took place inside the plumbing system and drove this intense period of activity. Based on deflation amplitudes associated with lava fountains and according with other retrieved parameters (i.e., magnitude of inflations, inflation and deflation velocities and volcanic tremor amplitudes), three periods have been distinguished. Period I displays higher values of all the aforementioned parameters, interpreted as conspicuous volumes of volatile-rich magma transferred towards the surface. Period II shows lower values evidencing lack of important new injections of magma from depth, whereas period III reveals a general increasing trend possibly related to gas flushing from magma residing in deeper portions of the plumbing system. Detailed elaborations of tilt signals allowed the identification of short-lived inflations accompanying the early stages of lava fountains during period II. Our results reveal significant correlations between amplitude and velocity of tilt and volcanic tremor signals associated with lava fountains and evidence the crucial role of gas in the inflation-deflation cycles.

Improved Position Sensorless Resonant Frequency Tracking Control Method for Linear Oscillatory Machine

To improve the reliability of control system and obtain the maximum output efficiency, it is very necessary to carry out the position sensorless piston stroke control and the resonance frequency tracking control for linear oscillatory machine (LOM) at the same time. The conventional resonant frequency tracking control method based on model reference adaptive system (MRAS) has the problem of low estimation accuracy caused by inaccurate model parameters. In order to obtain more accurate resonant frequency, an improved position sensorless resonant frequency tracking control method is proposed in this article. In this method, the adjustable model is constructed based on the mechanical dynamics equation of LOM to track the system resonant frequency. A hybrid terminal sliding-mode observer (HTSMO) is designed to replace the conventional reference model, which can obtain more accurate velocity reference signal for adjustable model by introducing the current error feedback link, thereby further improving the resonant frequency tracking accuracy. Moreover, a second-order generalized integrator (SOGI) is introduced to filter the estimated velocity signal of HTSMO. The filtered velocity signal is not only used for the adjustable model to track the resonant frequency, but also used to obtain the smooth piston stroke signal for the stroke closed-loop control. Comprehensive simulation and experimental results have proved the effectiveness and the superiority of the proposed method.

Tracing the Galactic Disk with the Kinematics of Gaia Cepheids

Classical Cepheids (CCs) are excellent tracers for understanding the structure of the Milky Way disk. The latest Gaia Data Release 3 provides a large number of line-of-sight velocity information for Galactic CCs, offering an opportunity for studying the kinematics of the Milky Way. We determine the 3D velocities of 2057 CCs relative to the Galactic center. From the projections of the 3D velocities onto the X–Y plane of the Galactic disk, we find that the V R and V ϕ velocities of the northern and southern warps (directions with highest amplitude) are different. This phenomenon may be related to warp precession or asymmetry in warp structure. By investigating the kinematic warp model, we find that the vertical velocities of CCs are more suitable for constraining the warp precession rate than the line-of-node angle. Our results suggest that CCs at 12–14 kpc are the best sample for determining the Galactic warp precession rate. Based on the spatial structure parameters of Cepheid warp from Chen et al., we determine a warp precession rate of ω = 4.9 ± 1.6 km s−1 kpc−1 at 13 kpc, which supports a low precession rate in the warp model. In the future, more kinematic information on CCs will help to constrain the structure and evolution of the Milky Way better.

A three-dimensional human motion pose recognition algorithm based on graph convolutional networks

In the task of three-dimensional human motion posture recognition, there are problems such as target loss, inaccurate target positioning, and high computational complexity. This article designs a recognition evaluation algorithm to address these issues. Design a LiteHRNet model for extracting skeleton sequences from action videos, and propose a graph convolutional structure that combines residual networks and attention mechanisms. This network can effectively enhance the expression ability of node key features. Introducing second-order velocity information and spatial position information of joint points to improve positioning accuracy. Improve the TCN and Transformer network models to simultaneously extract local and long-term features throughout the entire model, and more accurately model the temporal correlation between nodes in the entire action sequence. The fusion of Transformer networks can reduce the computational complexity of the model while ensuring its accuracy. The experiment shows that the model has good evaluation performance on multiple datasets.

A long short term memory network-based, global navigation satellite system/inertial navigation system for unmanned surface vessels

In recent years, research on unmanned surface vessels has entered a new phase, demanding the increased accuracy and reliability of integrated navigation and positioning systems. However, in complex environments, due to intermittent sensor failures or communication limitations, Global Navigation Satellite System (GNSS) signals are unreliable. Various errors that emerge in the system make it impossible to complete the measurement update process of filtering algorithms. This study provides a mechanism for improving the accuracy of combined GNSS/Shipborne Inertial Navigation System (SINS) integrated navigation when a GNSS outage occurs. Firstly, a SINS denoising method combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and an improved wavelet threshold algorithm is proposed. Secondly, a method based on Long Short-Term Memory (LSTM) network assisted GNSS/SINS integrated navigation is proposed to address the issue of poor information fusion performance when GNSS outages occur. The trained LSTM model is combined with SINS data to predict velocity and position during GNSS outages, providing high-precision velocity and position information. This ensures continuous, high-precision, navigation of unmanned surface vessels. Finally, to confirm the validity of the proposed methodology, a sea trial is performed to compare the different methods, and the results reveal the feasibility and effectiveness of the proposed methodology.

CARMENES input catalog of M dwarfs

Aims. Knowledge of rotation periods (Prot) is important for understanding the magnetic activity and angular momentum evolution of late-type stars, as well as for evaluating radial velocity signals of potential exoplanets and identifying false positives. We measured photometric and spectroscopic Prot for a large sample of nearby bright M dwarfs with spectral types from M0 to M9, as part of our continual effort to fully characterize the Guaranteed Time Observation programme stars of the CARMENES survey. Methods. We analyse light curves chiefly from the SuperWASP survey and TESS data. We supplemented these with our own follow-up photometric monitoring programme from ground-based facilities, as well as spectroscopic indicator time series derived directly from the CARMENES spectra. Results. From our own analysis, we determined Prot for 129 stars. Combined with the literature, we tabulated Prot for 261 stars, or 75% of our sample. We developed a framework to evaluate the plausibility of all periods available for this sample by comparing them with activity signatures and checking for consistency between multiple measurements. We find that 166 of these stars have independent evidence that confirmed their Prot. There are inconsistencies in 27 periods, which we classify as debated. A further 68 periods are identified as provisional detections that could benefit from independent verification. We provide an empirical relation for the Prot uncertainty as a function of the Prot value, based on the dispersion of the measurements. We show that published formal errors seem to be often underestimated for periods longwards of ∼10 d. We examined rotation–activity relations with emission in X-rays, Hα, Ca II H&K, and surface magnetic field strengths for this sample of M dwarfs. We find overall agreement with previous works, as well as tentative differences in the partially versus fully convective subsamples. We show Prot as a function of stellar mass, age, and galactic kinematics. With the notable exception of three transiting planet systems and TZ Ari, all known planet hosts in this sample have Prot ≳ 15 d. Conclusions. Inherent challenges in determining accurate and precise stellar Prot means independent verification is important, especially for inactive M dwarfs. Evidence of potential mass dependence in activity–rotation relations would suggest physical changes in the magnetic dynamo that warrants further investigation using larger samples of M dwarfs on both sides of the fully convective boundary. Important limitations need to be overcome before the radial velocity technique can be routinely used to detect and study planets around young and active stars.

The ℓ1−2-norm-regularized basis pursuit seismic inversion based on the exact Zoeppritz equation

Sparsity constraints have been widely adopted in the regularization of ill-posed problems to obtain subsurface properties with sparseness features. However, the target parameters are generally not sparsely distributed, and sparsity constraints lead to results that are missing information. Smooth constraints (e.g., [Formula: see text] norm) lead to the insufficient resolution of the inversion results. To overcome this issue, an effective solution is to convert the target parameters to a sparse representation, which can then be solved with sparsity constraints. For the estimation of elastic parameters, a high-resolution and reliable seismic basis pursuit inversion is developed based on the exact Zoeppritz equation. Furthermore, the [Formula: see text] norm is developed as a constraint, wherein a regularized function is minimized with the alternating direction method of the multipliers algorithm. Numerical examples and real data applications demonstrate that our method can not only improve the accuracy of the inversion results, especially the S-wave velocity and density information but also increase the resolution of the inversion results. Furthermore, the [Formula: see text]-norm constraint has better noise suppression demonstrating great potential in practical applications.

Kinematic criteria determining swing movement of world class dancesport athletes

PurposeThe aim of the study was to identify the distinguishing characteristics of the swing movement of the world’s top dancesport couples through kinematic variables.MethodsThe study involved six world-class and six intermediate dance couples. The couples were asked to execute three identical series of three natural turns of the figure in a Viennese Waltz, all being filmed as they danced to music. Six international experts evaluated all the trials of each couple from the point of view of the technical quality component based on the Absolute Judging System. A triaxial rotational angular velocity measurement (gyroscope) device was placed on the dorsal part of the pelvic girdle and on the posterior part of the thorax of each competitor.ResultsAn analysis of covariance demonstrated that regardless of the sports level, the maximum hip girdle angular rotation velocity in the forward swing movement performed by men was significantly related to the judging score achieved (F1,9 = 11.5; p < 0.05; ηp2 = 0.45).ConclusionsThe mean squares of the differences of the hip and thoracic spine angular rotation velocity signals were found to be a good criterion for evaluating the swing movement, which is related to the judges’ evaluation. The descriptions of the performance of complex rotational movements obtained in this study can find their application in the analysis, teaching, and evaluation of dance couples. This is one of the first studies in the literature that deals in detail with swing movement in sports dance.

A Coherent Integration Method for Moving Target Detection in Frequency Agile Signal-Based Passive Bistatic Radar

In this paper, the possibility of improving target detection performance in passive bistatic radar by exploiting a frequency agile (FA) signal is investigated, namely frequency agile signal-based passive bistatic radar (FAPBR) coherent integration. Since the carrier frequency of each pulse signal is agile, FAPBR coherent integration suffers from the problems of random range and Doppler phase fluctuations. To tackle these challenges, a novel FA signal coherent integration target detection scheme for PBR is proposed. In particular, the phase quadratic difference principle is presented for eliminating Doppler phase hopping. Then, frequency rearrangement is adopted to compensate for random range phase fluctuation while obtaining the high-range-resolution profiles (HRRPs) of the detecting target. Further, we innovatively present a sliding-range ambiguity decoupling (S-RAD) method to remove the range ambiguity effect in the case of the high pulse repetition frequency (HPRF). Compared with the existing methods, the proposed method can effectively mitigate Doppler phase hopping without requiring prior target velocity information, offering improved coherent integration performance in frequency agile signals with reduced computational complexity. Moreover, it successfully corrects the range ambiguity issue caused by HPRF. Finally, a series of simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

RadarMOSEVE: A Spatial-Temporal Transformer Network for Radar-Only Moving Object Segmentation and Ego-Velocity Estimation

Moving object segmentation (MOS) and Ego velocity estimation (EVE) are vital capabilities for mobile systems to achieve full autonomy. Several approaches have attempted to achieve MOSEVE using a LiDAR sensor. However, LiDAR sensors are typically expensive and susceptible to adverse weather conditions. Instead, millimeter-wave radar (MWR) has gained popularity in robotics and autonomous driving for real applications due to its cost-effectiveness and resilience to bad weather. Nonetheless, publicly available MOSEVE datasets and approaches using radar data are limited. Some existing methods adopt point convolutional networks from LiDAR-based approaches, ignoring the specific artifacts and the valuable radial velocity information of radar measurements, leading to suboptimal performance. In this paper, we propose a novel transformer network that effectively addresses the sparsity and noise issues and leverages the radial velocity measurements of radar points using our devised radar self- and cross-attention mechanisms. Based on that, our method achieves accurate EVE of the robot and performs MOS using only radar data simultaneously. To thoroughly evaluate the MOSEVE performance of our method, we annotated the radar points in the public View-of-Delft (VoD) dataset and additionally constructed a new radar dataset in various environments. The experimental results demonstrate the superiority of our approach over existing state-of-the-art methods. The code is available at https://github.com/ORCAUboat/RadarMOSEVE.

Deep‐salt: Complete three‐dimensional salt segmentation from inaccurate migrated subsurface offset gathers using deep learning

AbstractDelimiting salt inclusions from migrated images during the velocity model building flow is a time‐consuming activity that depends on highly human‐curated analysis and is subject to interpretation errors or limitations of the images and methods available. We propose a supervised deep learning based method to include three‐dimensional salt geometries in the velocity models. We compare two convolutional networks – based on the U‐Net architecture – which can process three‐dimensional seismic data. One architecture uses three‐dimensional convolutional kernels, and the other has convolutional long short‐term memory units. Each architecture requires specific preprocessing steps which affects their training and predictive performance. Both proposed architectures use subsurface offset gathers obtained from reverse time migration with an extended imaging condition as input and are trained to predict the salt inclusions. The velocity model used in migration is a reasonable approximation of sediment velocity but without salt inclusions. Thus, the migration model and, consequently, the migrated images are inaccurate due to the absence of the salt inclusion in the model using just the sediment velocity information for the segmentation. A similar salt inclusion methodology was previously validated for two‐dimensional approaches; we extend it to the three‐dimensional case. Our approach relies on subsurface common image gathers to focus the sediments' reflections around the zero offset and spread salt reflections' energy over large subsurface offsets. The results show that both proposed network models can accurately delineate the salt bodies in the test models, but when evaluating the trained networks for the three‐dimensional SEG/EAGE salt model, the architecture with convolutional long short‐term memory units has proven to generalize better.