基于机器人运动模型的EKF-SLAM算法改进

针对两轮驱动机器人运动模型定向误差的累积问题,提出改进的三轮驱动机器人运动模型,对EKF-SLAM算法的一致性进行改进;该模型通过对机器人车轮线速度的解耦运算,将机器人运动过程中的旋转角速度提取出来并作为系统的控制输入之一,从而可以直接得到各个控制时间间隔内的机器人姿态角变化,很好地避免了机器人定向误差的累积;最后,基于归一化估计方差的检验标准进行实验,验证了三轮驱动机器人运动模型有效提高了EKF-SLAM算法的一致性。     

Taking advantage of collective knowledge in emergency response systems

When large groups work on a theme, they have the potential to produce a lot of useful knowledge, regardless of whether they are acting in a coordinated manner or individually. Spontaneously generated information has received much attention in recent years, as organizations and businesses discover the power of crowds. New technologies, such as blogs, Twitter, wikis, photo sharing, collaborative tagging and social networking sites, enable the creation and dissemination of content in a relatively simple way. As a result, the aggregate body of knowledge is growing at an accelerated rate. Many organizations are looking for ways to harness this power, which is being called collective intelligence. Research has shown that it is possible to obtain high quality results from collectively produced work.In this paper, we consider the domain of emergency response. Research has shown that individuals respond quickly and massively to emergencies, and that they try to help with the situation. Thus, it seems like a logical step to attempt to harness collective knowledge for emergency management. Disaster relief groups and field command frequently suffer from lack of up to date information, which may be critical in a rapidly evolving situation. Some of this information could be generated by the crowd at large, enabling more effective response to the situation. In this paper, we discuss the possibilities for the introduction of collective knowledge in disaster relief and present architecture and examples of how this could be accomplished.     

基于运动信息自适应的快速运动估计算法

UMHexagonS是H.264视频编码标准中所采用的快速整像素运动估计算法,但在许多实时场景的应用中,该算法还明显存在搜索点数过多、搜索速度较慢的缺憾,急需进一步的改进和优化。在UMHexagonS算法的基础上,提出一种基于运动信息自适应的快速运动估计算法。使用动态搜索窗为不同尺寸的块自适应地分配预测搜索窗;根据当前块的运动剧烈程度选择运动类型自适应的搜索方案;通过分析实际运动序列水平、垂直方向的偏向特性依次采用带方向的十字型搜索和自适应的矩形—菱形搜索;利用预测运动矢量的方向信息采用自适应的多层次八边形区域搜索;并依据块的尺寸大小采用自适应的六边形搜索。实验结果表明,本文算法相比于UMHexagonS算法而言,图像的峰值信噪比(PSNR)平均提高了0.0125 dB,同时运动估计时间减少了13%32%,其场景自适应能力和实时性能都得到了很大的增强。     

曲线运动SAR成像算法研究

研究合成孔径雷达曲线运动时的优化成像质量问题。提出了一种结合了惯导加速度参数和根据回波数据两种运动补偿曲线运动合成孔径雷达成像算法,提高了成像质量。首先研究了合成孔径雷达曲线运动时的目标模型,分析了惯性导航系统的原理,并以频谱分析的子孔径成像为基础,加入运动补偿等辅助处理。进行仿真,根据一组仿真参数和曲线轨迹生成仿真回波数据,证明仿真成像算法的有效性。系统运算简单,可为曲线运动时的合成孔径雷达实时信号处理系统系统设计提供依据。     

一种动态场景下运动对象分割新算法

视频运动对象分割是计算机视觉和视频处理的基本问题。在摄像机存在全局运动的动态场景下,准确分割运动对象依然是难点和热点问题。本文提出一种基于全局运动补偿和核密度检测的动态场景下视频运动对象分割算法。首先,提出匹配加权的全局运动估计补偿算法,消除动态场景下背景运动对运动对象分割的影响;其次,采用非参数核密度估计方法分别估计各像素属于前景与背景的概率密度,通过比较属于前景和属于背景的概率及形态学处理得到运动对象分割结果。实验结果证明,该方法实现简单,有效地提高了动态场景下运动对象分割的准确性。     

Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape

The problem of path planning for an automaton moving in a two-dimensional scene filled with unknown obstacles is considered. The automaton is presented as a point; obstacles can be of an arbitrary shape, with continuous boundaries and of finite size; no restriction on the size of the scene is imposed. The information available to the automaton is limited to its own current coordinates and those of the target position. Also, when the automaton hits an obstacle, this fact is detected by the automaton's tactile sensor. This information is shown to be sufficient for reaching the target or concluding in finite time that the target cannot be reached. A worst-case lower bound on the length of paths generated by any algorithm operating within the framework of the accepted model is developed; the bound is expressed in terms of the perimeters of the obstacles met by the automaton in the scene. Algorithms that guarantee reaching the target (if the target is reachable), and tests for target reachability are presented. The efficiency of the algorithms is studied, and worst-case upper bounds on the length of generated paths are produced.Supported in part by the National Science Foundation Grant DMC-8519542.     

基于样本熵与时频分析的手部抓握运动意图预测

针对上肢外骨骼控制信号产生与外部设备响应存在时间滞后导致脑机接口(BCI)系统实时性差的问题,采集被试手部自主抓握前运动相关皮质电位(MRCP) ,提出基于非线性复杂度特征样本熵 (SampEn)与线性幅值特征融合算法的手部运动意图预测方法. 从时频、神经复杂度分析不同大脑状态之间存在的差异,通过特征融合实现对手部抓握运动意图的预测. 基于特征融合意图离线预测准确率最高可达88.46%,可以在人体手部自主运动发生时刻?1 400 ms实现对手部运动预测. 与平静时期手部静止状态相比,被试产生手部抓握运动意图时脑电信号的功率谱与复杂度均产生明显变化,为基于手部运动意图预测提前驱动机器人实现人机协同提供控制策略.     

陈垃圾滚筒筛运动模式与筛分效率优化

推导陈垃圾在滚筒筛中的运动方程并求数值解,按最大位置角将陈垃圾运动模式划分成滚落、抛落、圆周运动,给出不同筛筒转速、半径、动摩擦因数下的运动模式判别云图. 滚筒筛试验结果显示,陈垃圾运动的最大位置角随转速的升高先增大后不变,转速超过50 r/min后垃圾进行圆周运动. 陈垃圾滚筒筛的筛分效率随转速增大呈先升后降的趋势,随抛落差的增大呈持续上升的趋势,随着原料水的质量分数的增加呈下降的趋势. 基于试验结果,给出滚筒筛最优转速取值云图,在实际工程中可根据垃圾动摩擦因数及滚筒半径选择最优转速,同时减小水的质量分数以提高筛分效率.     

振荡翼改进运动模型的能量捕获性能分析

为了提高振荡水翼的水动力性能和能量捕获效率,基于传统简谐运动模型提出改进运动模型,通过引入俯仰运动系数k,推导改进后俯仰运动规律的一般形式,利用Fluent软件建立水翼的二维模型,综合分析半主动振荡模型下不同俯仰运动系数、折算频率和俯仰振幅等运动参数对水翼能量捕获性能的影响。结果表明:与传统简谐运动模型相比,改进运动模型能够使水翼升力系数在较长时间段内维持较大数值,提高水翼的升力系数和捕能效率;在相同的折算频率和俯仰振幅下,减小俯仰运动系数会增大俯仰角转动的角速度,进而增大俯仰运动所消耗的能量;不同俯仰振幅下对应不同的最优折算频率,折算频率越低水翼的捕能性能越不稳定,俯仰振幅越大,转动俯仰角所消耗的能量越多。在给定参数条件下,水翼的能量转化效率可超过40%。     

柔性腕试验台的力觉信号采集与力控制

文章针对柔性腕试验台，根据被测指标及精度要求，采用数字运动控制器、传感器及IPC等硬件设备，利用多媒体定时器和多线程技术，开发了力觉信号采集与力控制等软件模块，实现了对试验台的力／位混合控制，提高了加载过程平稳性。