Three-dimensional Motion of Multichain-Buoy Mooring System in shallow Water and Survival Conditions

An engineering numerical model for three dimensional motion of multichain-buoy mooring system in shallow water and survival condition is given in this paper. Shooting-aim method is employed for solving the dynamic equations of chain system in order to match the computation of buoy motion. The responses of buoy and chain have been computed for different wind-wave-current directions and different rigidity of chain. The results show that the present numerical model is reasonable.

Numerical study on wave loads and motions of two ships advancing in waves by using three-dimensional translating-pulsating source

A frequency domain analysis method based on the three-dimensional translating-pulsating （3DTP） source Green function is developed to investigate wave loads and free motions of two ships advancing on parallel course in waves. Two experiments are carried out respectively to mea- sure the wave loads and the free motions for a pair of side-by- side arranged ship models advancing with an identical speed in head regular waves. For comparison, each model is also tested alone. Predictions obtained by the present solution are found in favorable agreement with the model tests and are more accurate than the traditional method based on the three dimensional pulsating （3DP） source Green function. Numer- ical resonances and peak shift can be found in the 3DP pre- dictions, which result from the wave energy trapped in the gap between two ships and the extremely inhomogeneous wave load distribution on each hull. However, they can be eliminated by 3DTP, in which the speed affects the free sur- face and most of the wave energy can be escaped from the gap. Both the experiment and the present prediction show that hydrodynamic interaction effects on wave loads and free motions are significant. The present solver may serve as a validated tool to predict wave loads and motions of two ves- sels under replenishment at sea, and may help to evaluate the hydrodynamic interaction effects on the ships safety in replenishment operation.

Criteria for the Nonlinear Stability of Three-Dimensional Quasi-Geostrophic Motions

Some more proper criteria for the nonlinear stability of three-dimensional quasi-geostrophic motions are given by combining variational principle with a prior estimates method. The criteria are suitable for perturbations of initial condition as well as parameters in the model. The basic flow can be steady or unsteady. Particularly the difficulty due to the nonlinear boundary condition is completely overcome by the use of our method.

An image encryption scheme based on three-dimensional Brownian motion and chaotic system

At present, many chaos-based image encryption algorithms have proved to be unsafe, few encryption schemes permute the plain images as three-dimensional（3D） bit matrices, and thus bits cannot move to any position, the movement range of bits are limited, and based on them, in this paper we present a novel image encryption algorithm based on 3D Brownian motion and chaotic systems. The architecture of confusion and diffusion is adopted. Firstly, the plain image is converted into a 3D bit matrix and split into sub blocks. Secondly, block confusion based on 3D Brownian motion（BCB3DBM）is proposed to permute the position of the bits within the sub blocks, and the direction of particle movement is generated by logistic-tent system（LTS）. Furthermore, block confusion based on position sequence group（BCBPSG） is introduced, a four-order memristive chaotic system is utilized to give random chaotic sequences, and the chaotic sequences are sorted and a position sequence group is chosen based on the plain image, then the sub blocks are confused. The proposed confusion strategy can change the positions of the bits and modify their weights, and effectively improve the statistical performance of the algorithm. Finally, a pixel level confusion is employed to enhance the encryption effect. The initial values and parameters of chaotic systems are produced by the SHA 256 hash function of the plain image. Simulation results and security analyses illustrate that our algorithm has excellent encryption performance in terms of security and speed.

Motion Planning for Autonomous Driving with Real Traffic Data Validation

Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public roads.In this paper,a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method.To begin with,a trajectory prediction model is established based on the graph neural network(GNN)that is trained utilizing the INTERACTION dataset.Then,the validated trajectory prediction model is used to predict the future trajectories of surrounding road users,including pedestrians and vehicles.In addition,a GNN prediction model-enabled motion planner is developed based on the model predictive control technique.Furthermore,two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach,i.e.,merging and roundabout scenarios.The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.

Fast fixed-time three-dimensional terminal guidance with non-concave trajectory constraint

Focusing on the non-concave trajectory constraint,a sliding-mode-based nonsingular feedback fast fixed-time three-dimensional terminal guidance of rotor unmanned aerial vehicle landing,planetary landing and spacecraft rendezvous and docking terminal phase with external disturbance is investigated in this paper.Firstly,a fixed-time observer based on real-time differentiator is developed to compensate for the external disturbance,whose estimation error can converge to zero after a time independent of the initial state.Then,a sliding surface ensuring fixed-time convergence is presented.This sliding surface can guarantee that the vehicle achieves a non-concave trajectory,which is better for avoiding collision and maintaining the visibility of the landing site or docking port.Next,the nonsingular guidance ensuring the fixed-time convergence of the sliding surface is proposed,which is continuous and chatter free.At last,three numerical simulations of Mars landing are performed to validate the effectiveness and correctness of the designed scheme.

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.

Vehicle Motion Prediction at Intersections Based on the Turning Intention and Prior Trajectories Model

Intersections are quite important and complex traffic scenarios,where the future motion of surrounding vehicles is an indispensable reference factor for the decision-making or path planning of autonomous vehicles.Considering that the motion trajectory of a vehicle at an intersection partly obeys the statistical law of historical data once its driving intention is determined,this paper proposes a long short-term memory based(LSTM-based)framework that combines intention prediction and trajectory prediction together.First,we build an intersection prior trajectories model(IPTM)by clustering and statistically analyzing a large number of prior traffic flow trajectories.The prior trajectories model with fitted probabilistic density is used to approximate the distribution of the predicted trajectory,and also serves as a reference for credibility evaluation.Second,we conduct the intention prediction through another LSTM model and regard it as a crucial cue for a trajectory forecast at the early stage.Furthermore,the predicted intention is also a key that is associated with the prior trajectories model.The proposed framework is validated on two publically released datasets,next generation simulation(NGSIM)and INTERACTION.Compared with other prediction methods,our framework is able to sample a trajectory from the estimated distribution,with its accuracy improved by about 20%.Finally,the credibility evaluation,which is based on the prior trajectories model,makes the framework more practical in the real-world applications.

The New Method for CNC Trajectory Machining Motion Planning Based on Reference Time

In order to make motion planning fitting practice,many characteristic of CNC trajectory motion are discussed, such as the geometric function,the motion and the time.It is found that the relation between orbit function and motional parame- ter,so the differential equation about the trajectory motion be set-up by the goal of trajectory motion.The actual motion process is defined as reference time to link planning and practice.Present a new movement planning method based on self-defining time.At rest state,the differential simultaneous equation can be calculated according geometric characteristic analysis,it can be get that simple function consisted of coordinate and reference time variants.At motive state,dynamic parameter can be worked out accord- ing practical value of reference time,It is proved by experiment and simulation that it is a good way to control geometry and motion comprehensively,to reduce computation times and to increase the ability of environmental adaptation for path

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.

面向实船运动特性的轨迹发生器设计

为提高轨迹发生器生成的仿真数据与实船运动状态的符合性,设计了一种面向实船运动特性的船舶轨迹发生器。利用GPR-DBSCAN算法,将实测数据识别分割成不同运动状态,再结合船舶运动学和动力学方程,建立了不同运动状态的船舶运动数学模型,并给出了姿态速度和位置(AVP)、惯性测量单元(IMU)数据生成算法。最后利用非线性回归辨识模型中的未知参数,完成船舶轨迹发生器的构建。仿真和实测数据验证结果表明,构建的船舶轨迹发生器与PSINS工具箱中的轨迹发生器相比,位置、航速和航向准确度分别提高了46.73%、6.91%和40.71%,更符合实船运动特性。

基元库构建思想的机器人动作与策略演示学习方法

为解决机器人演示学习过程中演示数据优化、动作与任务策略的存储调用问题,提出一种利用基元库思想的演示学习方法。动作学习采用专家拖动机械臂执行动作获取演示数据,利用高斯混合模型与高斯混合回归提升数据质量,由动态运动基元算法转换为基函数的权重值。策略学习将任务步骤创建为动作基元,向基元内添加得到的权重值并构建包含任务执行策略的基元名片,由基元组成基元库完成存储。执行任务时从基元库中有序调用基元,利用YOLOv5目标检测网络和AlexNet图像分类网络检测目标信息,匹配动作并泛化出具有原动作特征的新动作。该方法实现了从演示中学习动作与策略存储,根据实际目标组合合适动作完成任务。钢筋绑扎实验创建5个动作基元,通过专家演示学习10个动作,机器人利用动作基元库成功完成水平面与竖直面钢筋交叉点绑扎任务说明其有效性。

4+2自由度叶片抛磨专用机器人轨迹规划方法研究

砂带抛磨作为复杂曲面叶片精密加工的最后一道工序,其加工质量直接影响叶片的服役性能和寿命。传统6自由度机器人多关节串联具有明显的弱刚性,在末端夹持大型叶片时抗变形能力欠佳。为此,文章自主设计研发了4+2自由度叶片抛磨专用机器人系统,并开展复杂曲面叶片抛磨轨迹规划方法研究。首先基于D-H法建立该机器人运动学模型,进行机器人运动学的正、逆解的求解;其次给出了综合考虑抛磨工具与工件曲率的干涉、刀路轨迹行距和轨迹点密度对残留高度的影响规律的轨迹规划方法,建立了2个单元的协同运动模型保证叶片的加工实现;最后通过叶片抛磨轨迹数控程序验证了所获得的抛磨轨迹的正确性。

基于改进运动原语生成的陆空两栖机器人Kinodynamic A^(*)算法

针对Kinodynamic A^(*)算法采用统一的运动原语生成,在陆空两栖机器人上应用时存在机动性无法充分发挥等问题,提出了基于改进运动原语生成的陆空两栖机器人Kinodynamic A^(*)算法。该算法从考虑陆、空模态运动能力差异出发,通过以下三个方法改进陆空两栖机器人Kinodynamic A^(*)算法性能。首先是基于动力边界决定状态空间加速度输入,差异化生成陆、空模态运动原语;其次是基于动力学约束剪枝,确保运动原语不超过速度边界;最后是构造轨迹优化代价函数优化轨迹平顺性、安全性及机动性。在仿真场景下的结果表明,所改进的算法在将空中平均速度提高了54.1%,平均路径总时间降低6.7 s,路径总代价降低22.00%,且算法运行时间降低0.069 s,充分发挥陆空两栖机器人上的强机动能力与高续航能力。

自动泊车发展现状及运动规划研究进展

汽车保有量持续上升与城市停车场建设不足间的矛盾引发了严重的停车问题,同时泊车位数量不足、泊车空间狭小等问题严重阻碍了车辆出行任务的完成。自动泊车因其较低的车速以及在特定场景下的高度自动驾驶,成为最先落地的自动驾驶产品。分析了国内外自动泊车市场的发展现状及趋势,重点讨论了泊车系统的规划决策模块。强调了运动规划的重要性,它直接影响车辆行驶质量,综述了国内外的研究成果,包括曲线插值、随机采样、图搜索、智能算法和数值优化方法,并分析了不同路径规划的优势和局限性。最后,展望了未来自动泊车技术的融合和发展方向。

基于轨迹计算的落石风险评估与防护措施决策研究

崩塌落石作为常见地质灾害之一,为探索验证简便可靠的风险评估手段,文中根据工程自身需求和特点,选择形式简洁的轨迹计算法评估落石风险。通过运动学公式进行程序化设计,讨论计算参数的合理取值,用大量计算数据模拟落石在坡面运动过程中的特征,给出落石风险评估结果,并制定出以主动防护为主的防治策略。

一种新型足底驱动式康复机器人的设计与分析

针对当前下肢运动障碍患者较多,不同康复阶段的患者,康复训练需求存在差异性的现状,设计了一种运动轨迹和幅度都可以调节的足底驱动式下肢康复机器人。该机器人包括一个靠背角度、椅面高度可调的座椅和一组运动轨迹、幅度可设定的五杆运动机构,能够适用于不同身高和不同康复期的患者。首先,依据患者的下肢训练需求完成了康复机器人的结构设计;然后,建立了足底驱动式康复机器人和人体联合运动的模型,并对该模型进行了运动学正分析,从而证明所设计的足底驱动式康复机器人有实现个性化下肢康复训练的可能性;最后,分析了下肢运动轨迹,结果表明该机器人可以实现正常的步态运动。

基于人工鱼群算法的篮球跳投轨迹实时跟踪

篮球运动轨迹跟踪方法由于轨迹特征提取效果差,导致跟踪误差高,提出一种基于人工鱼群算法的篮球跳投轨迹实时跟踪方法。提取三维坐标系下图像中篮球边缘轮廓信息,通过滤波函数消除篮球跳投图像噪声,基于人工鱼群算法提取图像中篮球跳投轨迹特征,寻找篮球运动轨迹的最优解集,改进篮球实时跟踪匹配路径,形成轨迹跟踪函数。实验结果可知,该方法的平均轨迹跟踪误差为18.28mm,与常规方法相比降低了6.22mm以上。因此,该跟踪方法的篮球轨迹跟踪精度更高,并且实时跟踪轨迹与真实轨迹更吻合。

基于机械臂运动轨迹控制双生成对抗网络的设计

为了提高机械臂的运动准确性,以改善机械臂的工作效率,设计了一种用于机械臂运动轨迹控制的双生成对抗网络。首先,对机械臂的结构进行了分析,根据机械臂的结构特点建立了机械臂的坐标系,在该坐标系上,通过机械臂各关节点的变换矩阵,求取了机械臂的运动学方程;然后,将机械臂末端执行器的位置向量、关节速度等信息与编码器相结合,以获取双向生成性对抗网络,并在双向生成性对抗网络的基础上,设计了双生成对抗网络,以对机械臂的运动轨迹进行控制;最后,利用所提方法与干扰观测器方法对机械臂的运动轨迹进行控制测试。测试结果显示,所提方法的控制准确度比干扰观测器方法的控制准确度提高了40.13%,而且所提方法控制机械臂运动过程中,出现的波动也比干扰观测器方法的控制过程要小。说明所提方法不仅能较为准确地控制机械臂运动,而且控制过程较为平稳,有助于提高机械臂的工作效率。

基于Leap Motion的手势识别在虚拟交互中的研究

针对虚拟场景中的自然手势交互进行了研究,提出了基于Leap Motion的动态指尖手势轨迹识别方法。首先借助Leap Motion设备采集指尖在场景中运动时产生的坐标并同时对数据进行预处理,然后从这一系列坐标中找出起始和结束位置并提取出有效的手势轨迹,再进行轨迹优化和手势初步分类,基于加权欧氏距离将轨迹和手势模板进行相似度计算,得到识别结果。采集200组手势数据进行实验,结果证明提出的方法具有很高的识别率,将方法应用在手势交互系统中,实现使用自然手势和虚拟物品进行交互,增加了交互乐趣,改善了交互体验。