轨迹空间中基于特征符号搜索算法的非刚体三维运动重建研究

现有研究一般是在正交约束条件下采用最小二乘法来求解三维运动轨迹基重建问题,然而这些算法的性能容易受噪声影响,产生不适定性。为此,通过利用离散线性的方法为三维运动搜索最优轨迹基系数和三维结构,建立了一种基于特征符号搜索算法的三维运动轨迹基重建方法。利用该方法,对一系列单目图像序列进行了重建实验研究,结果表明所提出的方法是可行的,并提高了重建算法的准确性。     

基于近似弧长参数化的机器人轨迹规划

主要研究了采用近似弧长参数化的插值方法进行关节式工业机器人的轨迹规划.运用近似弧长参数化的插值方法将机器人末端轨迹参数曲线离散为等弧长的插值点序列,通过机器人逆向运动学求解各关节的位移点序列,采用极限的方法进行各关节速度和加速度规划.这种轨迹规划方法可以避免关节空间的插值计算和雅克比矩阵的计算.在 matlab7.8平台上,对近似弧长参数化的插值方法、轨迹规划及可行性验证进行了实例仿真,仿真结果表明该轨迹规划方法是可行的.     

防空导弹拦截目标的建模与仿真

利用simulink构造防空导弹抗击目标的模型.在比例导引的基础上,建立导弹和目标的运动轨迹方程,模拟两者运动轨迹对抗击过程进行仿真.模型结构简单,能够实现对航向角和航迹角的实时跟踪,同时可以利用简单的参数调整模拟不同类型目标.最后,对拦截过程多个相关因素的影响给予对比,仿真结果理想可靠.     

智能轮椅上的机械手运动学分析及轨迹规划*

装备了机械手臂的智能轮椅在助老、助残、辅助康复等方面都有重要的研究价值。对装备于智能轮椅的四自由度机械手臂运动学分析以及轨迹规划进行了研究,为机械手臂自主抓取建立了数学模型和理论依据,运用D-H法建立了机器人的运动学方程。提出了基于目标函数的最小功率法,在逆解存在多组解的情况下,选取节约能量的一组解,从而节约了能量。通过仿真实验验证了算法的有效性,为以后的轨迹优化、无碰撞轨迹规划建立基础。     

一种基于HMM聚类的视频目标轨迹分析方法

针对视觉监控中基于运动轨迹的目标行为分析问题,提出了一种基于隐马尔科夫模型(HMM)聚类的轨迹分布模式提取和异常行为检测算法。首先为每一条运动轨迹训练一个HMM,并通过这些模型来计算轨迹两两之间的距离;然后对该距离矩阵采用主元分析法(PCA)降维并以降维后的每一行作为对应轨迹的特征进行模糊C均值聚类,接着为聚类后的每一类轨迹训练一个HMM作为其分布表达模型;最后利用这些HMM模型来检测给定轨迹所表示的目标行为是否异常。对不同场景的轨迹分析实验表明了方法的有效性。     

弹道导弹中段机动变轨模型研究

鉴于弹道导弹在中段机动变轨会导致飞行轨迹和落点的改变,对弹道导弹中段机动变轨进行了研究.针对弹道导弹中段飞行,引入小火箭的推力作用,建立弹道导弹中段变轨运动模型,运用四阶Runge-Kutta积分法求解其飞行轨迹,并将两种时机的机动变轨结果与自由飞行情况进行了比较,变轨时的中段结束点位置改变较为明显.仿真结果表明了该模型的有效性.该变轨模型可为目标的跟踪研究提供依据.     

A real-time vehicle detection and a novel vehicle tracking systems for estimating and monitoring traffic flow on highways

Real-time highway traffic monitoring systems play a vital role in road traffic management, planning, and preventing frequent traffic jams, traffic rule violations, and fatal road accidents. These systems rely entirely on online traffic flow info estimated from time-dependent vehicle trajectories. Vehicle trajectories are extracted from vehicle detection and tracking data obtained by processing road-side camera images. General-purpose object detectors including Yolo, SSD, EfficientNet have been utilized extensively for real-time object detection task, but, in principle, Yolo is preferred because it provides a high frame per second (FPS) performance and robust object localization functionality. However, this algorithm’s average vehicle classification accuracy is below 57%, which is insufficient for traffic flow monitoring. This study proposes improving the vehicle classification accuracy of Yolo, and developing a novel bounding box (Bbox)-based vehicle tracking algorithm. For this purpose, a new vehicle dataset is prepared by annotating 7216 images with 123831 object patterns collected from highway videos. Nine machine learning-based classifiers and a CNN-based classifier were selected. Next, the classifiers were trained via the dataset. One out of ten classifiers with the highest accuracy was selected to combine to Yolo. This way, the classification accuracy of the Yolo-based vehicle detector was increased from 57% to 95.45%. Vehicle detector 1 (Yolo) and vehicle detector 2 (Yolo + best classifier), and the Kalman filter-based tracking as vehicle tracker 1 and the Bbox-based tracking as vehicle tracker 2 were applied to the categorical/total vehicle counting tasks on 4 highway videos. The vehicle counting results show that the vehicle counting accuracy of the developed approach (vehicle detector 2 + vehicle tracker 2) was improved by 13.25% and this method performed better than the other 3 vehicle counting systems implemented in this study.     

基于终端滑模的四旋翼飞行器非线性轨迹跟踪控制

考虑到四旋翼飞行器的传统内外环控制策略依赖时标分离假设,稳定性分析复杂,并且控制参数选取困难的缺点,提出了一种与传统内外环控制策略不同的轨迹跟踪控制器;首先将四旋翼飞行器数学模型进行相应的变换,以分解为高度、偏航角和纵横向三个级联的子系统,再使用终端滑模控制方法设计高度和偏航角子系统的控制器,使两个子系统的状态误差可以在有限时间内收敛到原点,之后基于变量非线性变换设计纵横向子系统的控制器,分析了闭环系统稳定性,证明了所设计的轨迹跟踪控制器可以保证闭环系统跟踪误差渐近稳定到原点,最后仿真实验的结果验证了所设计的控制器的有效性。     

基于RFID技术的四旋翼无人机轨迹跟踪控制系统设计

为促进四旋翼无人机的飞行自主性,增强无人监管情况下飞行器主机所具备的避障行进能力,设计基于RFID技术的四旋翼无人机轨迹跟踪控制系统;采用RFID标签识别技术,调制处理既定控制信号,利用标签识别协议,连接微型四旋翼轨迹控制器与内环姿态控制器,通过数据通信链路,提取轨迹跟踪控制所需的传输电子量,完成轨迹跟踪控制系统硬件设计;利用动力系统中的参数辨识策略,确定与轨迹姿态控制相关的物理规律标注,实现四旋翼无人机轨迹跟踪控制;实验结果表明,与机器视觉型控制系统相比,基于RFID技术的控制系统的SSI避障行进指标数值相对较高,全局最大值达到了 79％,四旋翼无人机滚转角平均值为85°,能够有效抑制四旋翼无人机滚转角的数值上升趋势,增强无人监管情况下飞行器主机避障行进能力.     

Trajectory Simulation for Underwater Vehicle with Power-Lunched

The motion of combustion gas bubble produced by underwater ignition was developed based on Rayleigh-Plesset equation. Combining the bubble motion equation with the underwater launched vehicle motion equation in the longitudinal plane, a trajectory simulation model with power-launched was established. The hydrodynamic characteristics of underwater ignition at different depths and the trajectory analysis of the underwater vehicle with power-launched were given by simulation. The simulation results have a good agreement with experimental results, and show that the thrust peak caused by underwater ignition and the stable thrust both decrease slightly with the increase of the water depth, and the thrust peak will decline obviously by enlarging the initial radius of gas bubble; the thrust peak generated at the instant of ignition and the low-frequency oscillation of the stable thrust have no significant influence on the trajectory of underwater vehicle.