Optimal control of nonholonomic motion planning for a free-falling cat

The nonholonomic motion planning of a free-falling cat is investigated. Nonholonomicity arises in a free-falling cat subject to nonintegrable angle velocity constraints or nonintegrable conservation laws. When the total angular momentum is zero, the motion equation of a free-falling cat is established based on the model of two symmetric rigid bodies and conservation of angular momentum. The control of system can be converted to the problem of nonholonomic motion planning for a free-falling cat. Based on Ritz approximation theory, the Gauss-Newton method for motion planning by a falling cat is proposed. The effectiveness of the numerical algorithm is demonstrated through simulation on model of a free-falling cat.

The Marking Technology in Motion Capture for the Complex Locomotor Behavior of Flexible Small Animals(Gekko gecko)

Animals have evolved a variety of behavior patterns to adapt to the environment. Motion-capture technology is utilized to quantify and characterize locomotor behaviors to reveal the mechanisms of animal motion. In the capture of flexible, small animals with complex locomotor behaviors, the markers interfere with each other easily, and the motion forms(bending, twisting) of the moving parts are obviously different;thus, it is a great challenge to realize accurate quantitative characterization of complex locomotor behaviors. The correlation between the marker properties, including the size and space length, and the precision of the system are revealed in this paper, and the effects of diverse marker shapes on the capturing accuracy of the captured objects in different motion forms were tested. Results showed that the precision of system is significantly improved when the ratio of the space length to the diameter of the markers is larger than four;for the capture of the spatial twisting motion of the flexible object, the hexagon markers had the lowest spatial lost-marker rate relative to the circle, triangle, and square. Customized markers were used to capture the locomotor behavior of the gecko-inspired robot(rigid connection) and the gecko(flexible connection). The results showed that this marking technology can achieve high accuracy of motion capture for geckos(the average deviation was approximately 0.32 mm, and the average deviation’s variation rate was approximately 0.96%). In this paper, the marking technology for the motion capture of flexible, small animals with complex motion is proposed;it can effectively improve the system precision as well as the capture accuracy, and realize the quantitative characterization of the complex motion of flexible, small objects. It provides a reliable technical means to deeply study the evolution of the motion function of small animals and advance systematic research of motion-capture technology.

Effects of Degrees of Motion Freedom on Free-Fall of A Sphere in Fluid

Free-fall of a sphere in fluid is investigated at a Galileo number of 204 by direct numerical simulations(DNS). We mainly focus on the effects of different degrees-of-freedom(DOFs) of the sphere motion during free-fall. The characteristics of free-fall are compared with those of flow past a fixed sphere. Additional numerical tests are conducted with constraints placed on the translational or rotational DOFs of the sphere motion to analyze different DOFs of sphere motion. The transverse motion contributes significantly to the characteristics of free-fall; it results in the retardation of the vortex shedding, leading to the decrease of the Strouhal number. In addition, the transversal sphere motion exhibits the tendency to promote the sphere rotation. On the contrary, the effects of the sphere rotation and vertical oscillations during free-fall are negligible.

Simulation of Crowd Motion Based on Boids Flocking Behavior and Social Force Model

In the process of crowd movement,pedestrians are often affected by their neighbors.In order to describe the consistency of adjacent individuals and collectivity of a group,this paper learns from the rules of the flocking behavior,such as segregation,alignment and cohesion,and proposes a method for crowd motion simulation based on the Boids model and social force model.Firstly,the perception area of individuals is divided into zone of segregation,alignment and cohesion.Secondly,the interactive force among individuals is calculated based upon the zone information,velocity vector and the group information.The interactive force among individuals is the synthesis of three forces:the repulsion force to avoid collisions,the alignment force to keep consistent with the velocity direction,and the attractive force to get close to the members of group.In segregation and alignment areas,the repulsion force and alignment force among pedestrians are limited by visual field factors.Finally,the interactive force among individuals,the driving force of destination and the repulsion force of obstacles work together to drive the behavior of crowd motion.The simulation results show that the proposed method can not only effectively simulate the interactive behavior between adjacent individuals but also the collective behavior of group.

Identification of traffic object's motion behavior based on track

To extract the track parameters of a traffic object in traffic video and identify its motion behavior,a new method is proposed based on CamShift(Continuously Adaptive Mean Shift)and HMM(Hidden Markov Model).First,an object entering the video scene is located and tracked by the CamShift based algorithm,then its track parameters are obtained.Next,the track parameters are processed to form the observation sequence of HMM,and the motion behavior modeling and probability evaluation are implemented based on HMM.At last,the behavior identification and behavior statistics of the tracked traffic object in video are achieved.Experiments show that this method can be used to sort and recognize the motion behavior of the traffic object by its corresponding behavior track,and to do some statistics or corresponding process schemes.

Motion behavior of non-metallic particles under high frequency magnetic field

Non-metallic particles, especially alumina, are the main inclusions in aluminum and its alloys. Numerical simulation and the corresponding experiments were carried out to study the motion behavior of alumina particles in commercial pure aluminum under high frequency magnetic field. At the meantime, multi-pipe experiment was also done to discuss the prospect of continuous elimination of non-metallic particles under high frequency magnetic field. It is shown that: 1) results of numerical simulation are in good agreement with the experimental results, which certificates the rationality of the simulation model; 2) when the intensity of high frequency magnetic field is 0.06 T, the 30 μm alumina particles in melt inner could migrate to the edge and be removed within 2 s; 3) multi-pipe elimination of alumina particles under high frequency magnetic field is also effective and has a good prospect in industrial application.

Generation and Selection of Driver-Behavior-Based Transferable MotionPrimitives

To integrate driver experience and heterogeneous vehicle platform characteristics in a motion-planning algorithm,based on the driver-behavior-based transferable motion primitives(MPs), a general motion-planning framework for offline generation and online selection of MPs is proposed. Optimal control theory is applied to solve the boundary value problems in the process of generating MPs, where the driver behaviors and the vehicle motion characteristics are integrated into the optimization in the form of constraints. Moreover, a layered, unequal-weighted MP selection framework is proposed that utilizes a combination of environmental constraints, nonholonomic vehicle constraints,trajectory smoothness, and collision risk as the single-step extension evaluation index. The library of MPs generated offline demonstrates that the proposed generation method realizes the effective expansion of MP types and achieves diverse generation of MPs with various velocity attributes and platform types. We also present how the MP selection algorithm utilizes a unique MP library to achieve online extension of MP sequences. The results show that the proposed motion-planning framework can not only improve the efficiency and rationality of the algorithm based on driving experience but can also transfer between heterogeneous vehicle platforms and highlight the unique motion characteristics of the platform.

Modulation of Void Motion Behavior in a Magnetized Dusty Plasma

Based on fluid equations, we show a time-dependent self-consistent nonlinear model for void formation in magnetized dusty plasmas. The cylindrical configuration is applied to better illustrate the effects of the static magnetic field, considering the azimuthal motion of the dusts. Tile nonlinear evolution of the dust void and the rotation of the dust particles are then investigated numerically. The results show that, similar to the unmagnetized one-dimensional model, the radial ion dragplays a crucial role in the evolution of the void. Moreover, the dust rotation is driven by the azimuthal ion drag force exerting on the dust. As the azimuthal component of ion velocity increases linearly with the strength of the magnetic field, tile azimuthal component of dust velocity increases synchronously. Moreover, the angular velocity gradients of the dust rotation show a sheared dust flow around the void.

Abnormal Crowd Behavior Detection Using Optimized Pyramidal Lucas-Kanade Technique

Abnormal behavior detection is challenging and one of the growing research areas in computer vision.The main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertain events.In this work,Pyramidal Lucas Kanade algorithm is optimized using EME-HOs to achieve the objective.First stage,OPLKT-EMEHOs algorithm is used to generate the opticalflow from MIIs.Second stage,the MIIs opticalflow is applied as input to 3 layer CNN for detect the abnormal crowd behavior.University of Minnesota(UMN)dataset is used to evaluate the proposed system.The experi-mental result shows that the proposed method provides better classification accu-racy by comparing with the existing methods.Proposed method provides 95.78%of precision,90.67%of recall,93.09%of f-measure and accuracy with 91.67%.

Seismic behavior of precast segmental column bridges under near-fault forward-directivity ground motions

Precast segmental column bridges exhibit various construction advantages in comparison to traditional monolithic column bridges.However,the lack of cognitions on seismic behaviors has seriously restricted their applications and developments.In this paper,comprehensive investigations are conducted to analyze the dynamic characteristics of precast segmental column bridges under near-fault,forward-directivity ground motions.First,the finite-element models of two comparable bridges with precast segmental columns and monolithic columns are constructed by using OpenSees software,and the nonlinearities of the bridges are considered.Next,three different earthquake loadings are meticulously set up to handle engineering problems,namely recorded near-and far-field ground motions,parameterized pulses,and pulse and residual components extracted from real records.Finally,based on the models and earthquake sets,extensive explorations are carried out.The results show that near-fault forward-directivity ground motions are more threatening than far-field ones;precast segmental column bridges may suffer more pounding impacts than monolithic bridges;the“narrow band”effect caused by near-fault,forward-directivity ground motions may occur in bridges with shorter periods than pulse periods;and pulse and residual components play different roles in seismic responses.

Video object's behavior analyzing based on motion history image and hidden markov model

A novel method was proposed, which extracted video object＇ s track and analyzed video object＇ s be- havior. Firstly, this method tracked the video object based on motion history image, and obtained the co- ordinate-based track sequence and orientation-based track sequence of the video object. Then the pro- posed hidden markov model （HMM） based algorithm was used to analyze the behavior of video object with the track sequence as input. Experimental results on traffic object show that this method can achieve the statistics of a mass of traffic objects＇ behavior efficiently, can acquire the reasonable velocity behavior curve of traffic object, and can recognize traffic object＇ s various behaviors accurately. It provides a base for further research on video object behavior.

Visual Motion Segmentation in Crowd Videos Based on Spatial-Angular Stacked Sparse Autoencoders

Visual motion segmentation(VMS)is an important and key part of many intelligent crowd systems.It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes,which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades.Trajectory clustering has become one of the most popular methods in VMS.However,complex data,such as a large number of samples and parameters,makes it difficult for trajectory clustering to work well with accurate motion segmentation results.This study introduces a spatial-angular stacked sparse autoencoder model(SA-SSAE)with l2-regularization and softmax,a powerful deep learning method for visual motion segmentation to cluster similar motion patterns that belong to the same cluster.The proposed model can extract meaningful high-level features using only spatial-angular features obtained from refined tracklets(a.k.a‘trajectories’).We adopt l2-regularization and sparsity regularization,which can learn sparse representations of features,to guarantee the sparsity of the autoencoders.We employ the softmax layer to map the data points into accurate cluster representations.One of the best advantages of the SA-SSAE framework is it can manage VMS even when individuals move around randomly.This framework helps cluster the motion patterns effectively with higher accuracy.We put forward a new dataset with itsmanual ground truth,including 21 crowd videos.Experiments conducted on two crowd benchmarks demonstrate that the proposed model can more accurately group trajectories than the traditional clustering approaches used in previous studies.The proposed SA-SSAE framework achieved a 0.11 improvement in accuracy and a 0.13 improvement in the F-measure compared with the best current method using the CUHK dataset.

结合空间和运动特征的行为识别算法

针对视频行为识别方法中运动特征不足导致识别精度低的问题,提出一种结合空间和运动特征的行为识别算法。通过空间和运动两路卷积提取特征。空间卷积采用Res2Net作为主干网络并添加注意力模块;运动卷积细粒度地分为局部和全局特征,构造自适应通道序列重构模块提取局部特征,采用逐帧特征信息作差后聚合的方法提取全局特征。将运动和空间特征信息融合后,经过全连接层输出类别。该模型在UCF101和HMDB51数据集上准确率达到95.6%和70.7%,与传统算法相比,识别精度得到一定提升,验证了该算法的有效性。

基于改进蜣螂算法的并联机器人轨迹优化

提出了基于改进的蜣螂优化算法的并联机器人时间最优轨迹优化方法。对Delta并联机器人进行运动学逆解分析,基于NURBS曲线对并联机器人轨迹进行规划,使用改进的蜣螂优化算法对并联机器人轨迹进行优化,设置关键路径点并通过并联机器人运动学逆解模型求出各路径点对应的关节角度。模拟实验结果表明,与蜣螂优化算法相比,改进的蜣螂优化算法能够提升并联机器人的运动效率,缩短并联机器人的运动时间。

浮选气泡尺寸对粗颗粒-气泡涡旋湍流脱附机制的影响

气泡是浮选过程中不可或缺的重要角色,其尺寸对颗粒-气泡间相互作用有着显著影响,然而气泡尺寸对粗颗粒-气泡涡旋湍流脱附机制的影响尚未清晰。为此,采用自制的颗粒-气泡受限湍流脱附测试平台探索了涡旋湍流中不同气泡尺寸的气絮体上颗粒的脱附行为,并运用Image-ProPlus图像处理软件对颗粒脱附过程的动力学参数进行了测量分析。结果表明:在确保气泡浮力的前提下,减小气泡尺寸能够显著提升粗颗粒-气泡的矿化气絮体在涡旋湍流中的稳定性;涡旋湍流中气絮体上颗粒的脱附主要表现为流体剪切、气泡振荡和颗粒离心三种脱附形式,方腔内涡旋结构的改变对气絮体上颗粒的脱附方式至关重要;区别于传统离心脱附理论,涡旋湍流驱使下颗粒并非单独在气泡表面做高速离心运动,而是与气泡一起做离心运动,且小气泡与颗粒在方腔内共同旋转时,位于气泡外缘的颗粒与气泡运动速度接近,相应气絮体稳定性高,颗粒不易发生离心脱附,而大气泡形成的气絮体中,气泡外缘的颗粒与气泡运动速度差异明显增大,颗粒相对于气泡旋转更快,从而使得颗粒所受离心力显著增强,增加了离心脱附的可能性。研究结果有望对涡旋湍流场中粗颗粒的脱附机制提供一个基本认识。

基于深度学习的高超声速飞行器运动行为识别

高超声速飞行器具有机动大、速度高、航程远、威胁强等特点,已成为各大国重点发展的武器装备,识别其运动行为能为防御和拦截提供有力的技术支持。本文对有禁飞区等复杂约束条件下的高超声速飞行器运动行为识别问题开展研究:首先通过分析高超声速飞行器的运动特点,建立能够充分描述其行为模式的参数化运动方程;然后基于优化思想求解有禁飞区等复杂约束条件下的航迹规划问题,构建其数据集;最后基于深度学习方法设计高超声速飞行器运动行为识别算法。试验结果表明:所设计的识别算法能够实现对高超声速飞行器运动行为的识别,且具有一定的泛化能力,可为后续防御决策提供一定的参考。

基于运动特征提取和2D卷积的鱼类摄食行为识别研究

为了促进渔业装备智能化,近年来基于视频流的鱼类摄食行为识别研究受到了广泛关注。针对基于视频流的传统识别方法模型过于复杂,难以在边缘计算设备部署的问题,提出了一种轻量级的2D卷积运动特征提取网络Motion-EfficientNetV2,该网络以视频流为输入,能够有效识别鱼类摄食行为。提出的模型以EfficientNetV2为主干网络,基于TEA和ECANet构建了运动特征提取模块Motion,并将该模块嵌入到EfficientNetV2的每个Fused-MBConv模块中,使改进后的EfficientNetV2具有运动特征提取能力。同时使用ECANet对EfficientNetV2网络中的MBConv进行改进,增强其通道特征提取能力。在此基础上利用空洞卷积扩大感受野,提高大范围特征提取能力。试验结果表明,Motion-EfficientNetV2的参数量和浮点运算量分别为9.3×106和1.31×1010,优于EfficientNetV2。在TSN-ResNet50、TSN-EfficientNetV2、C3D以及R3D模型上进行对比试验,本文模型在降低参数量和浮点运算量的同时,使识别准确率提高到93.97%。该研究对于渔业装备智能化升级和科学养殖具有推动作用。

基于BCW的健康教育联合分级运动应用于青少年支气管哮喘病人的效果

目的:探究基于行为改变理论(behavior change wheel, BCW)的健康教育联合分级运动应用于青少年支气管哮喘病人的效果。方法:选取2021年5月—2022年5月于医院就诊的105例青少年支气管哮喘病人,按照随机数字表法分为对照组(52例)和观察组(53例)。对照组予以分级运动干预,研究组在对照组基础上实施基于BCW的健康教育。观察两组病人哮喘改善情况、干预前后的疾病认知情况和生活质量情况。结果:连续干预6个月后,观察组病人哮喘改善优良率为90.57%,明显高于对照组的71.15%(P<0.05);哮喘疾病知识问卷中的用药知识、诱发因素、疾病本质及自我管理维度评分均明显高于对照组(P<0.001);成人哮喘生命质量量表中的心理状态、哮喘症状、活动受限维度评分也明显高于对照组(P<0.001)。结论:基于BCW的健康教育联合分级运动应用于青少年支气管哮喘病人的效果显著,可明显改善病人的哮喘症状,提升病人对疾病相关知识的认知情况,改善病人生活质量。

现实情境羽毛球运动员接发球预判的视觉行为特征研究

旨在测试不同水平羽毛球运动员现场接发球的预判表现与视觉行为差异,研究现实情境下羽毛球运动员接发球预判的视觉行为特征。24名被试分为专家组和新手组参与测试,通过高速摄像机录制分析被试接发球预判的准确率和反应时,利用便携式眼动仪追踪记录被试视觉行为信息。结果发现,两组被试接发球启动时间和接发效果统计学差异极为显著(P<0.01);准备阶段,两组被试在球与场外区域注视时间的统计学差异极为显著(P<0.01);执行阶段,两组被试在头、肩、场外注视时间的统计学差异极为显著(P<0.01);飞行阶段,新手后场与专家后场、新手前场与专家前场统计学差异显著(P<0.05)。结论:在接发球预判过程中,羽毛球专家的启动时间短,接发效果较好,而新手组启动时间长且接发效果差。羽毛球专家的注视轨迹较为集中,无关注视较少,具有高效的视觉搜索模式,而新手组注视轨迹较为分散,无关注视较多,视觉搜索效率低。研究结果可以直接应用于羽毛球运动实践,为羽毛球运动员接发球预判技能、视觉注意技能训练提供理论基础与实践指导。

基于改进光流法的视频监控中人群异常行为检测算法

危害社会公共安全的事件频发,研究视频监控中人群异常行为对于恢复治安秩序和保障公众安全有着重要意义。由于视频监控涉及多种多样的场景,复杂环境影响了人群异常行为的准确检测。因此,为提升视频监控中人群异常行为的检测效果,提出基于改进光流法的视频监控中人群异常行为检测算法。利用改进单高斯模型在视频监控中人群视频帧内提取角点作为特征点;基于改进光流法计算特征点的运动速度与方向,提取有效特征点,得到人群运动目标图像;通过计算人群运动目标图像内光流点的方向熵、幅值熵与平均速度的乘积,确定运动混乱度;对比分析运动混乱度与设置阈值,完成人群异常行为检测。实验结果表明,该算法可有效提取人群视频帧内的角点和运动目标图像,准确检测人群异常行为,具有较好的视频监控中人群异常行为检测效果。