Biomimetic Experimental Research on Hexapod Robot's Locomotion Planning

To provide hexapod robots with strategies of locomotion planning, observation experiments were operated on a kind of ant with the use of high speed digital photography and computer assistant analysis. Through digitalization of original analog video, locomotion characters of ants were obtained, the biomimetic foundation was laid for polynomial trajectory planning of multi-legged robots, which was deduced with mathematics method. In addition, five rules were concluded, which apply to hexapod robots marching locomotion planning. The first one is the fundamental strategy of multi-legged robots’ leg trajectory planning. The second one helps to enhance the static and dynamic stability of multi-legged robots. The third one can improve the validity and feasibility of legs’ falling points. The last two give criterions of multi-legged robots’ toe trajectory figures and practical recommendatory constraints. These five rules give a good method for marching locomotion planning of multi-legged robots, and can be expended to turning planning and any other special locomotion.     

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.     

基于北斗的城市垃圾清运系统

为建设更美好的市容市貌, 降低运输车辆的空车率, 实现源头追溯与全流程透明化管理, 结合目前通用的软件开发方式与数据库技术, 给出了以智能化, 绿色为主的城市垃圾清运系统. 该系统以北斗定位数据为基础, 辅助以高德开放平台来进行设计开发. 系统的开发采用基于PHP语言的ThinkPHP框架以及B/S架构设计模式, 客户端采用Ajax和Web技术等方式实现数据的传输和展现. 该系统旨在为用户提供车辆实时位置查询、车辆历史轨迹查询、用户管理、统计分析等服务. 对于车辆运输过程中的整个轨迹数据, 系统采用了北斗系列来进行数据采集, 更有效地维护了信息和技术的安全. 后期还可结合机器学习等技术来对司机的驾驶行为, 车辆的健康状况进行相应分析. 通过北斗定位数据与互联网技术的结合, 该系统可有效地对车辆进行管理.     

基于机器视觉与单片机结合的机械臂抓取系统

抓取规划和控制是机械臂抓取系统中的难点。为了有效的解决这两个问题,本文提出一种基于机器视觉和单片机相结合的机械臂抓取系统。首先利用前期视觉测量成果对目标定位,然后设计了一种软件接口将目标表面三维信息进行可视化,并通过人为经验手动选择一个良好的抓取点;再结合逆运动学求解和轨迹规划算法,利用单片机驱动舵机使机械臂末端执行器移动到抓取点并完成抓取任务。实验结果表明,该系统成本低、并且目标形状不受限制,达到了预期效果。     

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.     

任意初态下的工业机器人迭代学习控制

对迭代初值为任意值的工业机器人轨迹跟踪控制系统,提出了一种基于滑模面的非线性迭代学习控制算法,使机器人轨迹能快速、精确跟踪上期望轨迹。基于有限时间收敛原理,构建了关于机器人轨迹跟踪误差的迭代滑模面,在滑模面内,机器人轨迹跟踪误差在预定时间内收敛到零。设计了基于滑模面的迭代学习控制算法,理论证明了随着迭代次数的增加,处于任意初态的轨迹将一致收敛到滑模面内,解决了迭代学习中的任意初值问题。数值仿真验证了该算法的有效性和抗干扰能力。     

轨迹数据的时空模式挖掘与管理决策研究综述

时空轨迹数据的获取变得越来越容易,轨迹数据刻画了移动对象的行为模式与活动规律,是对移动对象在时空环境下的移动模式和行为特征的真实写照,在城市规划、交通管理、服务推荐、位置预测等领域具有重要的应用价值。这些过程通常需要通过对时空轨迹数据进行模式挖掘才能得以实现。简述了轨迹数据挖掘的预处理和基本步骤,归纳了异常轨迹检测方法的分类,分析、总结了近年来基于轨迹数据的四种模式挖掘,从管理决策角度对轨迹数据挖掘进行相关综述和分析,有望为轨迹数据的模式挖掘与管理决策提供必要的文献资料和理论基础。     

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.