Multiparameter Numerical Investigation of Two Types of Moving Interactions Between the Deep-Sea Mining Vehicle Track Plate and Seabed Soil:Digging and Rotating Motions

To ensure the safe performance of deep-sea mining vehicles(DSMVs),it is necessary to study the mechanical characteristics of the interaction between the seabed soil and the track plate.The rotation and digging motions of the track plate are important links in the contact between the driving mechanism of the DSMV and seabed soil.In this study,a numerical simulation is conducted using the coupled Eulerian–Lagrangian(CEL)large deformation numerical method to investigate the interaction between the track plate of the DSMV and the seabed soil under two working conditions:rotating condition and digging condition.First,a soil numerical model is established based on the elastoplastic mechanical characterization using the basic physical and mechanical properties of the seabed soil obtained by in situ sampling.Subsequently,the soil disturbance mechanism and the dynamic mechanical response of the track plate under rotating and digging conditions are obtained through the analysis of the sensitivity of the motion parameters,the grouser structure,the layered soil features and the soil heterogeneity.The results indicate that the above parameters remarkably influence the interaction between the DSMV and the seabed soil.Therefore,it is important to consider the rotating and digging motion of the DSMV in practical engineering to develop a detailed optimization design of the track plate.

Stabilization and trajectory tracking of autonomous airship's planar motion

The stabilization and trajectory tracking problems of autonomous airship＇s planar motion are studied. By defining novel configuration error and velocity error, the dynamics of error systems are derived. By applying Lyapunov stability method, the state feedback control laws are designed and the close-loop error systems are proved to be uniformly asymptotically stable by Matrosov theorem. In particular, the controller does not need knowledge on system parameters in the case of set-point stabilization, which makes the controller robust with respect to parameter uncertainty. Numerical simulations illustrate the effectiveness of the controller designed.

A multiple template approach for robust tracking of fast motion target

Target tracking is very important in computer vision and related areas. It is usually difficult to accurately track fast motion target with appearance variations. Sometimes the tracking algorithms fail for heavy appearance variations. A multiple template method to track fast motion target with appearance changes is presented under the framework of appearance model with Kalman filter. Firstly, we construct a multiple template appearance model, which includes both the original template and templates affinely transformed from original one. Generally speaking, appearance variations of fast motion target can be covered by affine transformation. Therefore, the affine tr templates match the target of appearance variations better than conventional models. Secondly, we present an improved Kalman filter for approx- imate estimating the motion trail of the target and a modified similarity evaluation function for exact matching. The estimation approach can reduce time complexity of the algorithm and keep accuracy in the meantime. Thirdly, we propose an adaptive scheme for updating template set to alleviate the drift problem. The scheme considers the following differences： the weight differences in two successive frames; different types of affine transformation applied to templates. Finally, experiments demonstrate that the proposed algorithm is robust to appearance varia- tion of fast motion target and achieves real-time performance on middle/low-range computing platform.

Motion Tracking with Fast Adaptive Background Subtraction

To extract and tr ack moving objects is usually one of the most important tasks of intelligent video surveillance systems. This paper presents a fast and adaptive background subtraction algorithm and the motion tracking process using this algorithm. The algorithm uses only luminance components of sampled image sequence pixels and models every pixel in a statistical model. The algorithm is characterized by its ability of real time detecting sudden lighting changes, and extracting and tracking motion objects faster. It is shown that our algorithm can be realized with lower time and space complexity and adjustable object detection error rate with comparison to other background subtraction algorithms. Making use of the algorithm, an indoor monitoring system is also worked out and the motion tracking process is presented in this paper. Experimental results testify the algorithm's good performances when used in an indoor monitoring system.

Kinect-Based Motion Recognition Tracking Robotic Arm Platform

The development of artificial intelligence technology has promoted the rapid improvement of human-computer interaction. This system uses the Kinect visual image sensor to identify human bone data and complete the recognition of the operator’s movements. Through the filtering process of real-time data by the host computer platform with computer software as the core, the algorithm is programmed to realize the conversion from data to control signals. The system transmits the signal to the lower computer platform with Arduino as the core through the transmission mode of the serial communication, thereby completing the control of the steering gear. In order to verify the feasibility of the theory, the team built a 4-DOF robotic arm control system and completed software development. It can display other functions such as the current bone angle and motion status in real time on the computer operation interface. The experimental data shows that the Kinect-based motion recognition method can effectively complete the tracking of the expected motion and complete the grasping and transfer of the specified objects, which has extremely high operability.

Markerless Respiratory Motion Tracking Using Single Depth Camera

The aim of this study is to propose a novel system that has an ability to detect intra-fractional motion during radiotherapy treatment in real-time using three-dimensional surface taken by a depth camera, Microsoft Kinect v1. Our approach introduces three new aspects for three-dimensional surface tracking in radiotherapy treatment. The first aspect is a new algorithm for noise reduction of depth values. Ueda’s algorithm was implemented and enabling a fast least square regression of depth values. The second aspect is an application for detection of patient’s motion at multiple points in thracoabdominal regions. The third aspect is an estimation of three-dimensional surface from multiple depth values. For evaluation of noise reduction by Ueda’s algorithm, two respiratory patterns are measured by the Kinect as well as a laser range meter. The resulting cross correlation coefficients between the laser range meter and the Kinect were 0.982 for abdominal respiration and 0.995 for breath holding. Moreover, the mean cross correlation coefficients between the signals of our system and the signals of Anzai with respect to participant’s respiratory motion were 0.90 for thoracic respiration and 0.93 for abdominal respiration, respectively. These results proved that the performance of the developed system was comparable to existing motion monitoring devices. Reconstruction of three-dimensional surface also enabled us to detect the irregular motion and breathing arrest by comparing the averaged depth with predefined threshold values.

OPTICALLY TRACKING THE MOTION OF MICROBEADS TO STUDY PHYSICAL BEHAVIORS OF THE LIVING CELL IN RESPONSE TO TRANSIENT STRETCH OR COMPRESSION

Optical magnetic twisting cytometry and traction force microscopy are two advanced cell mechanics research tools that employ optical methods to track the motion of microbeads that are either bound to the surface or embedded in the substrate underneath the cell.The former measures rheological properties of the cell such as cell stiffness,and the latter measures cell traction force dynamics.Here we describe the principles of these two cell mechanics research tools and an example of using them to study physical behaviors of the living cell in response to transient stretch or compression.We demonstrate that,when subjected to a stretch
unstretch manipulation,both the stiffness and traction force of adherent cells promptly reduced,and then gradually recover up to the level prior to the stretch.Immunofluorescent staining and Western blotting results indicate that the actin cytoskeleton of the cells underwent a corresponding disruption and reassembly process almost in step with the changes of cell mechanics.Interestingly,when subjected to compression,the cells did not show such particular behaviors.Taken together,we conclude that adherent cells are very sensitive to the transient stretch but not transient compression,and the stretch-induced cell response is due to the dynamics of actin polymerization.

Reliability of Passive Physical Activity Assessment for Inpatients Using Marker⁃Free Motion Tracking System

Evidence⁃based practices of public health will benefit from quantification of passive physical activity assessment.This study aims to investigate the reliability of marker⁃free system(MFS)such as Microsoft Kinect in measuring upper extremity motion from different angles.Ten healthy participants performed elbow and shoulder extension/flexion along frontal and median anatomical planes for ten pace⁃controlled repetitions,during which the spatiotemporal positions of upper extremity joints were concurrently recorded by two sensors from 0°and 45°viewing angles.Reliability between the two sensors were evaluated using Pearson correlation coefficient,intra⁃class correlation coefficients,and 95%limits of agreement and coefficient of variation.Worse reliability was observed when possibility of occlusion was higher.However,better reliability was found when longer observation interval(10 s)was used as elementary measuring unit than shorter observation interval(2 s).The overall angular reliability of activity as displacement or changes in angle was not satisfactory.The results are expected to inform the industry for the extension of MFS to clinic applications.

Electronic image stabilization system based on global feature tracking

A new robust electronic image stabilization system is presented, which involves feature-point, tracking based global motion estimation and Kalman filtering based motion compensation. First, global motion is estimated from the local motions of selected feature points. Considering the local moving objects or the inevitable mismatch, the matching validation, based on the stable relative distance between the points set is proposed, thus maintaining high accuracy and robustness. Next, the global motion parameters are accumulated for correction by Kalman filteration. The experimental result illustrates that the proposed system is effective to stabilize translational, rotational, and zooming jitter and robust to local motions.

Modeling and Tracking Control for Piezoelectric Actuator Based on a New Asymmetric Hysteresis Model

This paper presents a new asymmetric hysteresis model and its application in the tracking control of piezoelectric actuators. The proposed model is based on a coupled-play operator which can avoid the conventional Prandtl-Ishlinskii(CPI)model's defects, i.e., the symmetric property. The high accuracy for modeling asymmetric hysteresis is validated by comparing simulation results with experimental measurements. In order to further evaluate the performance of the proposed model in closed-loop tracking application, two different hybrid control methods which experimentally demonstrate their performance under the same operating conditions, are compared to validate that the hybrid control strategy with proposed hysteresis model can mitigate the hysteresis more effectively and achieve better tracking precision. The experimental results demonstrate that the proposed modeling and tracking control strategy can realize efficient control of piezoelectric actuator.

Global and segmental myocardial deformation by 2D speckle tracking compared to visual assessment

AIM:To examine the feasibility and reliability of measuring global and segmental longitudinal strain(LS) compared to visual assessment of wall motion(WM).METHODS:Assessment of segmental(17 left ventricular segments) LS using automatic function imaging(AFI) in 55 patients(60.0 ± 8.7 years,73% male) divided into 2 groups:groupⅠ included 35 patients with WM abnormalities and/or impaired ejection fraction and group Ⅱ included 20 patients with normal WM and ejection fraction.Visual analysis of WM abnormalities was performed using 2-dimensional echocardiography(2DE) and WM score was calculated.Both modalities were analyzed by one expert reader at 2 different sessions.RESULTS:Analysis of 935 left ventricular segments was completed in 94.1% and 96.3% by visual assessment and AFI,respectively.There was a strong positive linear relationship between the WM score and global LS in all patients.Intra-observer agreement for calculation of WM score was excellent for groupⅠ patients(kappa:0.97) and very good for group Ⅱ patients(kappa:0.92).Intra-observer agreement for AFI showed excellent agreement with very small mean difference in both groupⅠ and Ⅱ(-0.0 ± 2.3 and-0.0 ± 1.9,respectively).CONCLUSION:The interpretation of global and segmental LS using AFI is a more feasible and reliable technique for the quantification of myocardial deformation than visual assessment of WM scores.

Method of target tracking with Doppler blind zone constraint

Doppler blind zone （DBZ） has a bad influence on the airborne early warning radar, although it has good detection performance for low altitude targets with pulse Doppler （PD） technology. In target tracking, the blind zone can cause target tracking breakage easily. In order to solve this problem, a parallel particle filter （PF） algorithm based on multi-hypothesis motion models （MHMMs） is proposed. The algorithm produces multiple possible target motion models according to the DBZ constraint. Particles are updated with the constraint in each motion model. Once the first measurement from the target which reappears from DBZ falls into the particle cloud formed by any model, the measurementtrack association succeeds and track breakage is avoided. The simulation results show that on the condition of different DBZ ranges, a high association ratio can be got for targets with different maneuverability levels, which accordingly improves the tracking quality.

UAV target tracking algorithm based on task allocation consensus

In order to adapt to the changes in the states of moving targets and meet the requirements of quality of service in communication among unmanned aerial vehicles (UAVs), the UAVs formation needs to dynamically adjust tracking tasks and tracking paths, when they carry out a mission of tracking multiple moving targets. A multi-targets tracking algorithm based on task allocation consensus is proposed for UAVs to track multiple moving targets under the distributed control architecture in the limited communication range. This algorithm creates a distributed dynamic task allocation model using intermittent asynchronous communication principle to realize the sharing of observation information gathered by each UAV with fewer communications. In addition, this algorithm also makes it possible that multi-UAVs can plan tracking paths for multiple moving targets. We implement the algorithm on the formation with three UAVs to track three moving targets through simulation. Simulation results show the effectiveness of the proposed algorithm. © 2016 Beijing Institute of Aerospace Information.

Tracking controller for robot manipulators viac omposite nonlinear feedback law

A composite nonlinear feedback tracking controller for motion control of robot manipulators is described. The structure of the controller is composed of a composite nonlinear feedback law plus full robot nonlinear dynamics compensation. The stability is carried out in the presence of friction. The controller takes advantage of varying damping ratios induced by the composite nonlinear feedback control, so the transient performance of the closed-loop is remarkably improved. Simulation results demonstrate the feasibility of the proposed method.

Particle Filter Object Tracking Algorithm Based on Sparse Representation and Nonlinear Resampling

Object tracking with abrupt motion is an important research topic and has attracted wide attention.To obtain accurate tracking results,an improved particle filter tracking algorithm based on sparse representation and nonlinear resampling is proposed in this paper. First,the sparse representation is used to compute particle weights by considering the fact that the weights are sparse when the object moves abruptly,so the potential object region can be predicted more precisely. Then,a nonlinear resampling process is proposed by utilizing the nonlinear sorting strategy,which can solve the problem of particle diversity impoverishment caused by traditional resampling methods. Experimental results based on videos containing objects with various abrupt motions have demonstrated the effectiveness of the proposed algorithm.

Marker-Based and Marker-Less Motion Capturing Video Data: Person and Activity Identification Comparison Based on Machine Learning Approaches

Biomechanics is the study of physiological properties of data and the measurement of human behavior.In normal conditions,behavioural properties in stable form are created using various inputs of subconscious/conscious human activities such as speech style,body movements in walking patterns,writing style and voice tunes.One cannot perform any change in these inputs that make results reliable and increase the accuracy.The aim of our study is to perform a comparative analysis between the marker-based motion capturing system(MBMCS)and the marker-less motion capturing system(MLMCS)using the lower body joint angles of human gait patterns.In both the MLMCS and MBMCS,we collected trajectories of all the participants and performed joint angle computation to identify a person and recognize an activity(walk and running).Using five state of the art machine learning algorithms,we obtained 44.6%and 64.3%accuracy in person identification using MBMCS and MLMCS respectively with an ensemble algorithm(two angles as features).In the second set of experiments,we used six machine learning algorithms to obtain 65.9%accuracy with the k-nearest neighbor(KNN)algorithm(two angles as features)and 74.6%accuracy with an ensemble algorithm.Also,by increasing features(6 angles),we obtained higher accuracy of 99.3%in MBMCS for person recognition and 98.1%accuracy in MBMCS for activity recognition using the KNN algorithm.MBMCS is computationally expensive and if we redesign the model of OpenPose with more body joint points and employ more features,MLMCS(low-cost system)can be an effective approach for video data analysis in a person identification and activity recognition process.

Nonlinear suboptimal tracking control of spacecraft approaching a tumbling target

We investigate the close-range relative motion and control of a spacecraft approaching a tumbling target. Unlike the traditional rigid-body dynamics with translation and rotation about the center of mass（CM）, the kinematic coupling between translation and rotation is taken into consideration to directly describe the motion of the spacecraft＇s sensors or devices which are not coincident with the CM. Thus, a kinematically coupled 6 degrees-of-freedom（DOF） relative motion model for the instrument（feature point） is set up. To make the chaser spacecraft＇s feature point track the target＇s, an optimal tracking problem is defined and a control law with a feedback-feedforward structure is designed. With quasi-linearization of the nonlinear dynamical system, the feedforward term is computed from a specified constraint about the dynamical system and the reference model, and the feedback action is derived starting from the state-dependent Ricca equation（SDRE）. The proposed controller is compared with an existing suboptimal tracking controller, and numerical simulations are presented to illustrate the effectiveness and superiority of the proposed method.

Conditional Random Field Tracking Model Based on a Visual Long Short Term Memory Network

In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is established by using a visual long short term memory network in the three-dimensional(3D)space and the motion estimations jointly performed on object trajectory segments.Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation.To address the uncertainty of the length and interval of trajectory segments,a multimode long short term memory network is proposed for the object motion estimation.The tracking performance is evaluated using the PETS2009 dataset.The experimental results show that the proposed method achieves better performance than the tracking methods based on the independent motion estimation.

Real-time Non-linear Target Tracking Control of Wheeled Mobile Robots

A control strategy for real-time target tracking for wheeled mobile robots is presented.Using a modified Kalman filter for environment perception,a novel tracking control law derived from Lyapunov stability theory is introduced.Tuning of linear velocity and angular velocity with mechanical constraints is applied.The proposed control system can simultaneously solve the target trajectory prediction,real-time tracking,and posture regulation problems of a wheeled mobile robot.Experimental results illustrate the effectiveness of the proposed tracking control laws.

Dynamic Motion Resistance for Tracked Vehicle

Based on previous achievements,a dynamic pressure-sinkage equation for saturated clay is established.First,aquasi-static penetration rate is selected,and the ratio of the dynamic penetration rate to the quasi-static rate is used to characterize the degree of dynamic effect,then theβth power of the ratio is used to quantify the dynamic effect of sinkage.The dynamic effect exponentβis obtained using penetration tests with different penetration rates.Then,a dynamic motion resistance equation for a tracked vehicle is established based on the dynamic pressure-sinkage equation.The equation incorporates both penetration and bulldozing resistance.Finally,a series of simulation experiments with varying travel speeds and slip rates is carried out.The results show that an increase in the speed leads to stronger terrain stiffness,resulting in a decrease in sinkage and motion resistance.However,the enhancement effect becomes weaker with an increase in the travel speed.