Supervised Autonomy: A Framework for Human-Robot Systems Development

In this paper we present a paradigm for robot control, Supervised Autonomy. Supervised Autonomy is a framework, which facilitates the development of human robot systems. The components which this framework embraces has been devised in a human-oriented manner, to augment users in accomplishing their task. The general concept of our paradigm is to incorporate supervisory control with a qualitative approach for the control of robots. Supervisory control does not rely on human users to perform all the basic functions of perception and action in a system. The approach we have taken shifts all basic autonomous functions to the physical robot agent, integrated with a set of qualitative instructions, in combination with a simple graphical user interface, and together with suitable feedback form the complete framework. Experimental results of applying this framework to the use of a mobile robot teleoperation system are presented. The system we have developed make extensive use of behavior-based control technology, embracing a number of real-time visual behaviours, together with a set of intuitive instructions designed for the navigation of a mobile robot.     

基于傅立叶描述子和HMM的手势识别

针对家庭服务机器人平台中人机交互的问题,提出基于视觉的手势识别作为人与机器人交互的方式,研究利用傅立叶描述子对手势形状进行描述,并结合支持向量机和隐马尔可夫模型分别对静态手势和动态手势进行分类,实现了静态手势和动态手势的识别。该系统基于新型传感器Kinect,在图像分割阶段结合图像深度信息,可以有效的将手势区域提取出来,在一定范围内具有较强的鲁棒性,特征提取阶段基于傅立叶描述子,使手势识别具有旋转、缩放、平移不变性。针对七种常见静态手势和四种动态手势进行测试,平均识别率分别达到98.8%和96.7%,实验结果表明该系统具有较高的准确度。     

基于多分类器组合的多角度彩色人脸图像检测

提出了基于神经网络和隐马尔可夫模型组合的彩色人脸图像检测方法 .根据归一化后的彩色图像的色度彩色分量直方图将图像粗分割成若干幅二值图像 ;在亮度图像上 ,以上述二值图像为掩模进行多分辨率的旋转不变性人脸检测 .在人脸检测时 ,本文分两步 :第一步先用神经网络来确定人脸的旋转角度 ,然后对旋正后的图像运用识别人脸奇异值特征的隐马尔可夫模型进行验证 .实验结果表明 ,本文算法是有效的     

基于模糊逻辑控制与人机交互的移动机器人避障系统

主要采用模糊逻辑控制的方法,使用十六位单片机SPCE061A的语音输入功能,构成一个人机交互系统,并使二者结合起来应用于移动机器人导航过程中,较好地搭建了一个基于人机监督指导的移动机器人智能避障系统、完成避障和路径规划任务,可以克服基于多传感器的机器人避障技术方面存在的不足之处,具有较好的工程实用价值.     

A Multiple Models Approach for Adaptation and Learning in Mobile Robots Control

The paper proposes a multiple models based control methodology for the solution of the tracking problem for mobile robots. The proposed method utilizes multiple models of the robot for its identification in an adaptive and learning control framework. Radial Basis Function Networks (RBFNs) are considered for the multiple models in order to exploit the non-linear approximation capabilities of the nets for modeling the kinematic behaviour of the vehicle and for reducing unmodelled tracking errors contributions. The training of the nets and the control performance analysis have been done in a real experimental setup. The experimental results are satisfactory in terms of tracking errors and computational efforts and show the improvement in the tracking performance when the proposed methodology is used for tracking tasks in dynamical uncertain environments.     

Advanced Interfaces for Vehicle Teleoperation: Collaborative Control, Sensor Fusion Displays, and Remote Driving Tools

We are working to make vehicle teleoperation accessible to all users, novices and experts alike. In our research, we are developing a new control model for teleoperation, sensor-fusion displays and a suite of remote driving tools. Our goal is to build a framework which enables humans and robots to communicate, to exchange ideas and to resolve differences. In short, to develop systems in which humans and robots work together and jointly solve problems.     

一种基于GOR+GPU算法的机器人视觉导航方法

提出一种一般物体识别(GOR)方法.借鉴词袋(BOW)的统计模型,利用SIFT(尺度不变特征变换)检测算子进行特征向量描述.为了增加信息的冗余度,利用物体部件空间关系的统计信息来描述一幅图片中所有特征点的空间(相对距离和角度)关系,增广了原BOW模型中的特征向量.运用无监督判别分类器支持向量机(SVM)来实现分类识别.与此同时,采用GPU加速技术来实现SIFT特征提取与描述,以保证其实时性.然后,存手绘地图辅助导航的基础上,将该方法成功地应用到室内移动机器人导航上.实验结果表明,基于该方法的机器人导航技术具有较强的鲁棒性和有效性.     

基于设计心理学的手机医疗服务平台交互设计

文章通过对手机交互设计和医院挂号心理分析,确定该交互设计原则。以手机交互界面为基础,通过对就医用户客观心理因素特点分析研究。得出患者的消费习惯与心理需求对手机系统图标布局及界面美化设计有很大影响。通过对手机平台与挂号流程分析,以及对医院的服务条件、服务设施、服务能力和水平的不到及时改善,造成医疗费用增长过快,群众负担加重,医患关系紧张等等情况了解,得出契合医疗手机交互设计原则,使该挂号系统界面美观简洁、操作方便,希望对看病难,看病慢的现状有很大改善。     

基于多传感器信息融合的移动机器人导航系统

介绍了多传感器信息融合的基本原理,给出了基于多传感器信息融合的移动机器人导航系统结构。建立了移动机器人数学模型,运用基于扩展卡尔曼滤波的信息融合方法实现了移动机器人导航算法。通过实验验证了基于多传感器信息融合的移动机器人导航系统和导航算法的有效性。     

基于多传感器的家庭服务机器人局部导航方法研究

本文提出了一种基于多传感器的家庭服务机器人局部导航方法。首先,采用单个摄像头获取居室内障碍物的图像信息,利用超声波传感器和红外线传感器探测障碍物的距离信息。然后,据此计算在机器人运动方向上障碍物的遮挡空间或者多个障碍物之间的实际距离,再根据机器人自身的大小计算避开障碍物应该转动的方向及角度,从而实现居室内的自主导航。最后,仿真实验结果证明了该方法的有效性。     

利用Kinect的人体动作视觉感知算法

提出了一种面向类人机器人的人体动作视觉感知算法,提高了利用Kinect作为视觉输入设备捕捉到的人体动作数据的精度.首先,通过逆运动学方程将捕捉到的关节位移信息转换成角度信息.然后,以角速度和角加速度的变化为依据,将长时间的运动自动分割成独立片段,并用相关向量机原理估计出高精度的角度轨迹.最后,用角度轨迹的空间相似性、时间相似性、平滑度等指标对该算法进行了评估,并在NAO机器人平台上对算法处理后的动作进行了实验验证.实验结果表明,该算法有效提高了动作感知的时空相似性和轨迹平滑度,为高精度的动作模仿奠定了基础.     

Hybrid Control for Autonomous Mobile Robot Navigation Using Neural Network Based Behavior Modules and Environment Classification

A hybrid control architecture combining behavior based reactive navigation and model based environment classification has been developed. It is also hybrid in the sense that both competitive coordination and cooperative coordination are used for the BBC (Behavior Based Control) part. The contributions are as follows. First, a Neural Network (NN) in charge of environment classification has been developed based on 16 prototypes of topological maps roughly describing various local navigation environments. This environment classification NN not only enables the navigator to avoid local minimum points but also eliminates the requirement for prior detailed modeling of the environment since it needs to memorize only rough information on local environments encountered along the way that might be sufficient for navigation. Next, an NN based reactive behavior controller will be trained to learn human steering commands for each of the 16 prototype local environments. Third, the modified potential field (MPF) method obtained by adding the free space vector as the third component is used to select a particular reactive behavior in conjunction with the classification NN. Finally, a hybrid control architecture integrating all three of these concepts was developed. It avoids local minimum traps as well as solves the problems of poor obstacle clearance or oscillation. It is robust against sensor noise and adaptive to dynamic environments. This hybrid architecture is also amenable to easy addition of new behaviors due to the modularity of the BBC architecture. The effectiveness of the proposed architecture has been verified through both computer simulation and an actual robot called MORIS (MObile Robot as an Intelligent System).     

基于粒子群优化神经网络的移动机器人门牌识别方法研究

针对移动机器人门牌识别问题,提出了一种基于粗分类与细分类相结合的门牌识别方法。首先利用门牌号码字符的特殊节点进行粗分类,进而计算图像的不变矩;在此基础上,利用粒子群神经网络进行细分类,完成门牌识别。最后通过办公室环境中的门牌号码识别实验验证了该方法的快速性与有效性。     

基于能量空间逼近策略的三层前馈神经网络隐层训练算法

针对基于最佳平方逼近的三层前馈神经网络讨论了隐层生长模式的一种训练策略．首先根据隐层输出行为和期望输出数据的分布特征对样本数据确定的N维空间进行了不同意义上的划分．分析表明最有效的隐单元其输出向量应该在误差空间存在投影分量,同时该分量应位于目标空间中的某一能量空间内．在此基础上提出了基于能量空间逼近策略的隐层生长式训练算法．最后通过仿真实验验证了所提出算法的有效性．