基于主动视觉和超声的机器人目标跟踪系统

运动目标跟踪技术是未知环境下移动机器人研究领域的一个重要研究方向。该文提出了一种基于主动视觉和超声信息的移动机器人运动目标跟踪设计方法,利用一台SONY EV-D31彩色摄像机、自主研制的摄像机控制模块、图像采集与处理单元等构建了主动视觉系统。移动机器人采用了基于行为的分布式控制体系结构,利用主动视觉锁定运动目标,通过超声系统感知外部环境信息,能在未知的、动态的、非结构化复杂环境中可靠地跟踪运动目标。实验表明机器人具有较高的鲁棒性,运动目标跟踪系统运行可靠。     

基于多微处理器的智能超声探测系统

设计了一种基于多微处理器的智能超声探测系统.该系统采用由上位工作模式控制单元和下位智能超声传感器阵列组成的分布式结构.每个超声传感器均由独立的微处理器控制,并完成实时数据处理、抗干扰处理、故障报警以及数据通信等功能.上位控制单元根据移动机器人的运动状态采用不同的控制策略,使下位传感器阵列分组并行工作,提高了探测系统的实时性;下位传感器采用"阈值比较法"、"改进型递推均值滤波"算法和模糊信息处理技术,提高了探测系统的准确性、可靠性.将该探测系统安装于RIRA-Ⅱ移动机器人上,进行了基于主动视觉和超声信息的运动目标跟踪实验,实验表明超声探测系统运行可靠、稳定.     

多移动机器人组成任意队形的控制研究

本文将多移动机器人组成队形的整体行为分解为机器人个体分别向对应目标点运动的子行为加以研究．提出了基于两层监控模式的分布式控制框架，监控模块作为统一的指挥协调中枢，确定各机器人合理的对应目标点，避免机器人在目标区域内绕路，使得多移动机器人能够快速流畅地组成任意队形．实验证明了控制方法的有效性．     

监控系统中的多摄像机协同

描述了一个用于室内场合对多个目标进行跟踪的分布式监控系统.该系统由多个廉价的固定镜头的摄像机构成,具有多个摄像机处理模块和一个中央模块用于协调摄像机间的跟踪任务.由于每个运动目标有可能被多个摄像机同时跟踪,因此如何选择最合适的摄像机对某一目标跟踪,特别是在系统资源紧张时,成为一个问题.提出的新算法能根据目标与摄像机之间的距离并考虑到遮挡的情况,把目标分配给相应的摄像机,因此在遮挡出现时,系统能把遮挡的目标分配给能看见目标并距离最近的那个摄像机.实验表明该系统能协调好多个摄像机进行目标跟踪,并处理好遮挡问题.     

一种基于行为的移动机器人目标人跟踪控制方法

针对室内复杂环境,对于智能服务移动机器人,设计一个连续稳定的目标人跟踪算法是必要的.为此提出一种室内环境下基于行为的移动机器人对运动目标人进行跟踪的控制方法,该方法综合优先级裁决方法与模糊行为融合法选取移动机器人的控制行为,较好地解决跟踪过程中存在多种行为以及行为冲突问题,在完成运动避障的同时保持对运动目标的跟踪.对避...     

基于深度相机的移动机器人视觉跟踪控制研究

基于视觉利用移动机器人进行运动目标跟踪,该文提出一种基于二自由度云台和RGB-D相机的运动目标视觉跟踪及移动机器人路径实时规划、跟踪方法。该方法利用核相关滤波算法在图像中实时追踪目标,控制二自由度云台使深度相机实时对准目标,并根据深度相机得到目标的深度信息,利用坐标转换得到目标相对于机器人的位置信息;其后移动机器人根据目标的位置信息,基于五次多项式进行路径规划;最后采用李雅普诺夫控制律对移动机器人进行轨迹跟踪控制,使得机器人能够平稳地跟踪目标运动。该算法在阿克曼移动机器人上进行了实验,实验结果验证了算法的有效性和实时性。     

基于图像采集卡的智能安防监控系统设计

本文研究了基于Matrox图像采集卡的智能安防监控系统的总体解决方案,硬件设计部分详细叙述了该系统的图像采集模块设计和云台控制模块设计.通过对摄像机采集的图像进行运动目标的跟踪分析.产生控制信号,控制承栽摄像头的云台运动.以实现运动目标的跟踪:软件设计部分.针对运动目标的检测跟踪各种跟踪方法,详细研究并实现了基于时间差分的运动目标检测方法.完成了系统整体软件设计.实践证明,该系统具有较好的检测效果.     

基于大数据聚类的移动机器人运动跟踪控制系统设计

目前研究的移动机器人运动跟踪控制系统控制过程易受到外界扰动影响,导致控制稳定性较差,运动跟踪准确性较差,为此,基于大数据聚类算法设计了一种新的移动机器人运动跟踪控制系统。硬件部分主控制器负责远程无线通讯,图像采集的数据传输和舵机驱动连接,驱动控制器为机器人行走提供动能保证;远程控制模块负责数据,图像和指令的传输;舵机控制模块机器人的行走、转向;软件部分首先通过大数据聚类的方法分析机器人移动步态,根据运动超声波传感器原理判定障碍物位置,考虑移动机器人运行状态与足端轨迹,构建机器人行走控制模型。通过髋关节调节机器人姿态,消除外部扰动对机器人姿态和运动速度的影响,得到抗扰动控制模型。实验结果表明,所设计系统在对机器人运动控制的稳定性及对抗外界扰动方法具有较好的性能,能够实现对移动机器人运动的准确跟踪。     

基于模糊逻辑行为融合路径规划方法

采用基于模糊逻辑的路径规划方法,提出了一种在未知环境中基于模糊逻辑行为融合路径规划方法,设计了移动机器人路径规划方案,并介绍了该控制器的结构组成和设计过程.将模糊逻辑控制和行为控制相结合,构建了一个由避障行为、目标导向行为、模糊行为监督器组成的基于模糊逻辑行为控制的路径规划器.仿真研究和实验结果表明了避障方法的有效性和可行性.     

基于关联规则挖掘的全向轮移动机器人目标跟踪控制系统设计

为保证全向轮机器人在移动过程中所捕获到的目标对象能够完全符合理想目标设定条件,准确追踪目标节点的运动行为,设计基于关联规则挖掘的全向轮移动机器人目标跟踪控制系统;根据CAN主控框架的部署形式,按需连接核心管控电路与I/O跟踪模块;分别以转向控制器、速度控制器为例,完善全向轮控制结构的物理作用能力,实现机器人目标跟踪控制系统的硬件设计;在此基础上,定义频繁项集合,按照具体的关联规则特征描述结果,确定挖掘程序指令的执行能力,得到准确的关联离散度指标计算结果,实现控制系统的关联规则挖掘,再联合相关硬件设备结构,完成基于关联规则挖掘的全向轮移动机器人目标跟踪控制系统设计;分析对比实验结果可知,随着关联规则挖掘控制系统的应用,全向轮机器人在移动过程中所捕获到的目标对象能够将理想目标完全包含在内,机器人目标跟踪结果准确,可以辅助全向轮移动机器人更加准确地追踪目标节点的运动行为,符合实际应用需求。     

Optimal Representative Blocks for the Efficient Tracking of a Moving Object

Optimal representative blocks are proposed for an efficient tracking of a moving object and it is verified experimentally by using a mobile robot with a pan‐tilt camera. The key idea comes from the fact that when the image size of a moving object is shrunk in an image frame according to the distance between the camera of mobile robot and the moving object, the tracking performance of a moving object can be improved by shrinking the size of representative blocks according to the object image size. Motion estimation using edge detection (ED) and block‐matching algorithm (BMA) are often used in the case of moving object tracking by vision sensors. However, these methods often miss the real‐time vision data since these schemes suffer from the heavy computational load. To overcome this problem and to improve the tracking performance, the optimal representative block that can reduce a lot of data to be computed is defined and optimized by changing the size of the representative block according to the size of object in the image frame. The proposed algorithm is verified experimentally by using a mobile robot with a two degree‐of‐freedom active camera. © 2004 Wiley Periodicals, Inc.     

Position estimation of a mobile robot using images of a moving target in intelligent space with distributed sensors

Latest advances in hardware technology and state-of-the-art of mobile robots and artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. A mobile service robot requires the perception of its present position to co-exist with humans and support humans effectively in populated environments. To realize this, a robot needs to keep track of relevant changes in the environment. This paper proposes localization of a mobile robot using images recognized by distributed intelligent networked devices in intelligent space (ISpace) in order to achieve these goals. This scheme combines data from the observed position, using dead-reckoning sensors, and the estimated position, using images of moving objects, such as a walking human captured by a camera system, to determine the location of a mobile robot. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the ISpace. Using the a priori known path of a moving object and a perspective camera model, the geometric constraint equations that represent the relation between image frame coordinates for a moving object and the estimated robot's position are derived. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot, and the Kalman filtering scheme is used for the estimation of the mobile robot location. The proposed approach is applied for a mobile robot in ISpace to show the reduction of uncertainty in determining the location of a mobile robot, and its performance is verified by computer simulation and experiment.     

移动机器人实时目标跟踪系统设计与实现

针对单目视觉移动机器人目标跟踪的实时性和鲁棒性要求,提出了基于Kalman滤波器的改进Camshift算法检测和定位目标.将Kalman预测值作为目标初始位置,补偿摄像头和目标相对运动导致的目标在图像中的偏移.在系统“跟丢”后判断目标丢失的原因,根据原因自适应拓展搜索窗口作为Cam-shift算法的下一帧初始搜索窗口.为了验证改进算法的有效性,自主研制了一种应用该算法的履带式机器人实时目标跟踪系统.实验结果表明:该系统具有很好的鲁棒性和实时性.     

Heterogeneous multisensor fusion for mapping dynamic environments

In this paper, we propose a heterogeneous multisensor fusion algorithm for mapping in dynamic environments. The algorithm synergistically integrates the information obtained from an uncalibrated camera and sonar sensors to facilitate mapping and tracking. The sonar data is mainly used to build a weighted line-based map via the fuzzy clustering technique. The line weight, with confidence corresponding to the moving object, is determined by both sonar and vision data. The motion tracking is primarily accomplished by vision data using particle filtering and the sonar vectors originated from moving objects are used to modulate the sample weighting. A fuzzy system is implemented to fuse the two sensor data features. Additionally, in order to build a consistent global map and maintain reliable tracking of moving objects, the well-known extended Kalman filter is applied to estimate the states of robot pose and map features. Thus, more robust performance in mapping as well as tracking are achieved. The empirical results carried out on the Pioneer 2DX mobile robot demonstrate that the proposed algorithm outperforms the methods a using homogeneous sensor, in mapping as well as tracking behaviors.     

Design and implementation of vision-based transport for a mobile robot via Kalman filter

A vision-based scheme for object recognition and transport with a mobile robot is proposed in this paper. First, camera calibration is experimentally performed with Zhenyou Zhang’s method, and a distance measurement method with the monocular camera is presented and tested. Second, Kalman filtering algorithm is used to predict the movement of a target with HSI model as the input and the seed filling algorithm as the image segmentation approach. Finally, the motion control of the pan-tilt camera and mobile robot is designed to fulfill the tracking and transport task. The experiment results demonstrate the robust object recognition and fast tracking capabilities of the proposed scheme.     

六足机器人动态目标检测与跟踪系统研究

动态目标检测与目标跟踪是图像领域的热点研究问题,为研究其在移动机器人领域的应用价值,设计了六足机器人动态目标检测与跟踪系统。针对非刚体运动目标容易被检测为多个分散区域的问题提出区域合并算法,并通过对称匹配、自适应外点滤除对运动背景进行精确补偿,最终基于背景补偿法实现对运动目标的精确检测。研究了基于KCF（Kernel Correlation Filter）的目标跟踪算法在六足机器人平台上的应用,设计了自适应跟踪算法实现六足机器人对运动目标的角度跟踪。将运动目标检测及跟踪算法应用于六足机器人系统。实验表明,在六足机器人移动过程中,系统可对运动目标进行精确检测与跟踪。     

Reliability-based camera handoff for cooperative tracking with multiple pan-tilt cameras

We present a new handoff method for multiple pan-tilt cameras for mobile robot tracking in an indoor environment. Camera handoff is an important step to consistently maintain the visibility of a mobile robot with maximized object tracking accuracy. First, we propose a method to estimate the position of a mobile robot using single pan-tilt camera. Then, the concept of position reliability is defined to quantitatively evaluate the accuracy of position estimation and tracking ability of individual pan-tilt cameras. Position reliability is used to decide when to trigger handoff and who to response handoff in the proposed handoff algorithm. Experimental results demonstrate that four pan-tilt cameras can systematically track a mobile robot in an indoor environment using the proposed method.     

Application of voting to fusion of purposive modules: An experimental investigation

Control in behavior-based systems is distributed among a set of specialized behaviors. To achieve efficient implementation, behaviors exploit specific assumptions about a given task and environment. Thus they become vulnerable to deviations that render these assumptions invalid. Yet it is important to provide appropriate responses to unforeseen situations.

We demonstrate that, using voting techniques, a model-free approach may be provided to construct reliable behaviors from a multitude of less reliable ones. A team of complementary behaviors vote for the set of possible actions, and the action which is most favored is selected for controlling the system. We conjecture that selecting actions according to this scheme can improve the probability of success.

Our conjecture is investigated through two sets of experiments. In the first, a team of obstacle avoidance behaviors vote to guide a mobile robot platform in the most appropriate direction. In the second, four object tracking modules are integrated to perform smooth pursuit with a camera head.     

Finite-time tracking control for multiple non-holonomic mobile robots based on visual servoing

This paper investigates finite-time tracking control problem of multiple non-holonomic mobile robots via visual servoing. It is assumed that the pinhole camera is fixed to the ceiling, and camera parameters are unknown. The desired reference trajectory is represented by a virtual leader whose states are available to only a subset of the followers, and the followers have only interaction. First, the camera-objective visual kinematic model is introduced by utilising the pinhole camera model for each mobile robot. Second, a unified tracking error system between camera-objective visual servoing model and desired reference trajectory is introduced. Third, based on the neighbour rule and by using finite-time control method, continuous distributed cooperative finite-time tracking control laws are designed for each mobile robot with unknown camera parameters, where the communication topology among the multiple mobile robots is assumed to be a directed graph. Rigorous proof shows that the group of mobile robots converges to the desired reference trajectory in finite time. Simulation example illustrates the effectiveness of our method.