智能移动机器人跟踪系统应用研究与设计

针对移动机器人检测与跟踪系统的世界模型,从智能控制与模式识别方法和传统控制理论相结合的思想出发,提出一种多层次、多阶段的智能控制模型结构。此结构仿人思维模式把复杂任务系统分解为感知、执行、决策三个层次,解决了复杂任务中不易建模的问题;跟踪过程采用Kalman预报器对运动目标状态进行一步预测估计和两步增量式跟踪算法,可快速平滑地实现移动机器人对运动目标的跟踪驱动控制。给出了该结构模型的移动机器人视觉检测识别和跟踪控制系统在汽车桩考中的实际应用。     

多特征提取的双目机器人目标跟踪

为了解决复杂环境下双目机器人的目标跟踪问题,提出多特征提取的方法。在机器人静止-目标运动模式下根据改进的步态光流图和视角识别目标并构造颜色直方图模板;在机器人运动-目标运动模式下利用扩展卡尔曼滤波器、基于自适应核函数的CamShift算法、基于Hu矩的头肩模型匹配算法提取目标的运动特征、颜色特征和头肩特征以实现目标跟踪。实验分析表明,所提出方法能够避免启动时手动框选目标,可以实现遮挡和背景与目标相似度高等复杂环境下的目标跟踪。     

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...     

多移动机器人的目标跟踪研究

针对多移动机器人对固定目标和动态目标跟踪问题进行了研究;首先基于l－φ闭环控率对多移动机器人进行刚性编队,建立运动学模型,采用leader-follower协调策略算法,实现了多移动机器人的协调合作;然后采用SURF算法识别目标,通过路径规划实现对固定目标的跟踪以及利用卡尔曼滤波器实现对运动目标的跟踪,设计了基于Backstepping方法的控制器,使多移动机器人能稳定跟踪目标;最后用MATLAB进行仿真;仿真结果表明,所设计的控制器和算法能使多移动机器人的跟踪误差快速收敛于零,适用于多移动机器人对目标的跟踪。     

MB-LBP特征提取和粒子滤波相结合的运动目标检测与跟踪算法研究

在复杂环境下,由于行人密度大以及运动随机性,导致运动目标(行人)难以检测和跟踪,造成人员计数误差。提出一种MB-LBP(Multi-scale Block Local Binary Pattern)特征提取和粒子滤波相结合的运动目标检测与跟踪算法来解决此问题。该算法首先用AdaBoost提取MB-LBP特征训练生成分类器进行人头检测,并根据人头目标尺寸变化范围去除部分误检,然后用改进的粒子滤波算法预测跟踪多个运动目标,最后对跟踪的运动目标进行计数。实验结果表明,提出的算法能够对复杂环境下多个运动目标进行有效检测及跟踪,准确、快速地对视频帧中的人员进行计数。     

基于激光传感器的移动机器人运动目标跟踪

针对室外环境下移动机器人基于激光传感器对运动目标的实时跟踪问题进行了研究。利用激光传感器检测运动目标,对均值滤波与中值滤波处理后的激光扫描点进行数据对比后,提出一种递推型中值均值混合滤波方法,用以减少激光扫描数据孤点并提高扫描返回值的准确性。设计了目标检测视窗提前剔除与被检测目标无关的障碍数据;设计利用激光扫描点在物体表面连续的特性,在检测视窗内完成对激光数据点的分类。提出以跟踪过程中机器人与目标之间的距离信息作为跟踪算法的评价标准,使用卡尔曼滤波跟踪算法和平滑接近图算法有效实现了机器人对运动目标的平稳自主跟踪。     

实时运动目标检测与跟踪平台的构建*

构建了一个基于图像采集卡的复杂环境下实时运动目标检测与跟踪的实验平台。基于此平台提出并实现了一种改进的运动目标检测算法,它融合了帧间差分法和背景差分法的优点,以适应复杂环境的变化。实验表明,该算法利用所构建的平台,对变化场景中的运动目标实施了快速有效的检测与跟踪,为智能视频技术的研究提供了一个实用的实验平台。     

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。     

基于卡尔曼滤波的移动机器人运动目标跟踪

提出了一种基于卡尔曼滤波的运动目标快速跟踪算法。针对复杂背景下彩色运动目标跟踪问题,采用基于颜色特征和形状特征相结合的方法进行目标识别。利用卡尔曼滤波器的预测功能,预测运动目标在下一帧中的位置,将图像全局搜索问题转换为局部搜索,提高了系统的实时性。实验结果表明:该算法满足移动机器人运动控制的实时性要求,实现了对运动目标的快速跟踪。     

复杂背景下运动点目标的检测和跟踪

为了解决复杂背景下运动点目标的检测和跟踪问题,本文提出了一种基于图像差分和聚类的运动目标检测和跟踪算法.该算法首先根据图像配准的方法,对序列图像进行差分运算,提取出候选的运动目标.在此基础上,利用运动目标在空间和时间上的相关性以及运动目标的轨迹所具有的连续性,采用一种特殊的聚类方法,从噪声环境中正确检测出运动目标的轨迹,并实现对运动目标的跟踪.实验表明该算法能快速检测出复杂背景下的运动点目标,并能有效处理轨迹相交和检测过程中出现新目标的情况.     

Real-time seam tracking for robotic laser welding using trajectory-based control

In this paper a real-time seam tracking algorithm is proposed that can cope with the accuracy demands of robotic laser welding. A trajectory-based control architecture is presented, which had to be developed for this seam tracking algorithm. Cartesian locations (position and orientation) are added to the robot trajectory during the robot motion. In this way, sensor information obtained during the robot motion is used to generate the robot trajectory while moving. Experiments have been performed to prove the tracking capabilities of the seam tracking algorithm.     

Real-Time Dynamic Visual Tracking Using PSD Sensors and Extended Trapezoidal Motion Planning

A real-time visual servo tracking system for an industrial robot has been implemented using PSD (Position Sensitive Detector) cameras, neural networks, and an extended trapezoidal motion planning method. PSD and directly transduces the light's projected position on its sensor plane into an analog current and lends itself to fast real-time tracking. A neural network, after proper training, transforms the PSD sensor reading into a 3D position of the target, which is then input to an extended trapezoidal motion planning algorithm. This algorithm implements a continuous motion update strategy in response to an ever-changing sensor information from the moving target, while greatly reducing the tracking delay. This planning method is found to be very useful for sensor-based control such as moving target tracking or weld-seam tracking in which the robot needs to change its motion in real time in response to incoming sensor information. Further, for real-time usage of the neural net, a new architecture called LANN (Locally Activated Neural Network) has been developed based on the concept of CMAC input partitioning and local learning. Experimental evidence shows that an industrial robot can smoothly track a moving target of unknown motion with speeds of up to 1 m/s and with oscillation frequency up to 5 Hz.     

Chance-constrained target tracking using sensors with bounded fan-shaped sensing regions

We present a robust target tracking algorithm for a mobile robot. It is assumed that a mobile robot carries a sensor with a fan-shaped field of view and finite sensing range. The goal of the proposed tracking algorithm is to minimize the probability of losing a target. If the distribution of the next position of a moving target is available as a Gaussian distribution from a motion prediction algorithm, the proposed algorithm can guarantee the tracking success probability. In addition, the proposed method minimizes the moving distance of the mobile robot based on the chosen bound on the tracking success probability. While the considered problem is a non-convex optimization problem, we derive a closed-form solution when the heading is fixed and develop a real-time algorithm for solving the considered target tracking problem. We also present a robust target tracking algorithm for aerial robots in 3D. The performance of the proposed method is evaluated extensively in simulation. The proposed algorithm has been successful applied in field experiments using Pioneer mobile robot with a Microsoft Kinect sensor for following a pedestrian.     

基于自适应Kalman预测器的运动估计算法

利用图像序列估计目标运动速度是机器人视觉中的一项重要研究内容。它应用在机器人操作、导航、视觉跟踪等多项领域中。这些应用一般均要求运动估计算法具有较好的实时性和抗噪能力。卡尔曼滤波器和预测器正符合上述要求。该文基于运动图像的仿射模型，探讨从序列图像中预测目标三维平动速度的卡尔曼预测算法。首先建立运动目标的“当前”统计模型，然后根据运动图像的仿射模型找出图像运动参数与目标三维速度间的关系(图像运动参数由目标图像的几何矩计算获得)。最后结合自适应卡尔曼滤波和卡尔曼一步预测算法设计自适应卡尔曼一步预测器。为减轻预测器的发散性，对初始状态进行估计。仿真结果表明，基于“当前”统计模型和运动图像仿射模型设计出的自适应卡尔曼一步预测器具有较高的精度。     

融合帧间差分与碰撞算法的快速跟踪预测算法

传统的运动目标跟踪预测算法难以保证机器人对高速运动目标的快速捕捉和提前预测,尤其是运动目标在滑行过程中发生碰撞改变了原有的运动方向,针对这一问题提出了基于帧间差分与碰撞算法相结合的运动目标跟踪预测算法.通过帧间差分法快速识别出平面内运动物体的具体位置和运动速度,根据其运动速度方向判别运动目标是否发生碰撞.当运动目标在运动过程中发生碰撞,采用LS-DYNA显示动力分析软件建立碰撞仿真模型,并用MATLAB拟合仿真数据得到碰撞算法,结合碰撞算法对运动目标的运动轨迹进行预测.结果表明以帧间差分和碰撞算法相结合的运动目标检测跟踪算法对于在平面内运动目标的跟踪预测方面速度更快,完全能够满足机器人对算法快速性的要求.     

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.     

基于双计算机的仿人机器人的视觉跟踪系统

运动目标的实时跟踪是机器人视觉的关键技术之一。设计了仿人机器人的视觉跟踪系统,系统采用双计算机,分别负责视觉信息的处理和运动单元的控制,两台计算机通过Memolink进行通讯。基于Windows的视觉信息处理子系统实现运动目标的分割,状态估计和预测。运动控制子系统采用RTlinux实时操作系统,利用PD控制器控制关节运动。实验验证了系统的稳定性和实时性。     

Moving people tracking with detection by latent semantic analysis for visual surveillance applications

The latent semantic analysis (LSA) has been widely used in the fields of computer vision and pattern recognition. Most of the existing works based on LSA focus on behavior recognition and motion classification. In the applications of visual surveillance, accurate tracking of the moving people in surveillance scenes, is regarded as one of the preliminary requirement for other tasks such as object recognition or segmentation. However, accurate tracking is extremely hard under challenging surveillance scenes where similarity among multiple objects or occlusion among multiple objects occurs. Usual temporal Markov chain based tracking algorithms suffer from the ‘tracking error accumulation problem’. The accumulated errors can finally make the tracking to drift from the target. To handle the problem of tracking drift, some authors have proposed the idea of using detection along with tracking as an effective solution. However, many of the critical issues still remain unsettled in these detection based tracking algorithms. In this paper, we propose a novel moving people tracking with detection based on (probabilistic) LSA. By employing a novel ‘twin-pipeline’ training framework to find the latent semantic topics of ‘moving people’, the proposed detection can effectively detect the interest points on moving people in different indoor and outdoor environments with camera motion. Since the detected interest points on different body parts can be used to locate the position of moving people more accurately, by combining the detection with incremental subspace learning based tracking, the proposed algorithms resolves the problem of tracking drift during each target appearance update process. In addition, due to the time independent processing mechanism of detection, the proposed method is also able to handle the error accumulation problem. The detection can calibrate the tracking errors during updating of each state of the tracking algorithm. Extensive, experiments on various surveillance environments using different benchmark datasets have proved the accuracy and robustness of the proposed tracking algorithm. Further, the experimental comparison results clearly show that the proposed tracking algorithm outperforms the well known tracking algorithms such as ISL, AMS and WSL algorithms. Furthermore, the speed performance of the proposed method is also satisfactory for realistic surveillance applications.     

Fast RGB-D people tracking for service robots

Service robots have to robustly follow and interact with humans. In this paper, we propose a very fast multi-people tracking algorithm designed to be applied on mobile service robots. Our approach exploits RGB-D data and can run in real-time at very high frame rate on a standard laptop without the need for a GPU implementation. It also features a novel depth-based sub-clustering method which allows to detect people within groups or even standing near walls. Moreover, for limiting drifts and track ID switches, an online learning appearance classifier is proposed featuring a three-term joint likelihood. We compared the performances of our system with a number of state-of-the-art tracking algorithms on two public datasets acquired with three static Kinects and a moving stereo pair, respectively. In order to validate the 3D accuracy of our system, we created a new dataset in which RGB-D data are acquired by a moving robot. We made publicly available this dataset which is not only annotated by hand, but the ground-truth position of people and robot are acquired with a motion capture system in order to evaluate tracking accuracy and precision in 3D coordinates. Results of experiments on these datasets are presented, showing that, even without the need for a GPU, our approach achieves state-of-the-art accuracy and superior speed.