Robot navigation using panoramic tracking

A vision-based navigation system is presented for determining a mobile robot's position and orientation using panoramic imagery. Omni-directional sensors are useful in obtaining a 360° field of view, permitting various objects in the vicinity of a robot to be imaged simultaneously. Recognizing landmarks in a panoramic image from an a priori model of distinct features in an environment allows a robot's location information to be updated. A system is shown for tracking vertex and line features for omni-directional cameras constructed with catadioptric (containing both mirrors and lenses) optics. With the aid of the panoramic Hough transform, line features can be tracked without restricting the mirror geometry so that it satisfies the single viewpoint criteria. This allows the use of rectangular scene features to be used as landmarks. Two paradigms for localization are explored, with experiments conducted with synthetic and real images. A working implementation on a mobile robot is also shown.     

Indoor navigation of a non-holonomic mobile robot using a visual memory

When navigating in an unknown environment for the first time, a natural behavior consists on memorizing some key views along the performed path, in order to use these references as checkpoints for a future navigation mission. The navigation framework for wheeled mobile robots presented in this paper is based on this assumption. During a human-guided learning step, the robot performs paths which are sampled and stored as a set of ordered key images, acquired by an embedded camera. The set of these obtained visual paths is topologically organized and provides a visual memory of the environment. Given an image of one of the visual paths as a target, the robot navigation mission is defined as a concatenation of visual path subsets, called visual route. When running autonomously, the robot is controlled by a visual servoing law adapted to its nonholonomic constraint. Based on the regulation of successive homographies, this control guides the robot along the reference visual route without explicitly planning any trajectory. The proposed framework has been designed for the entire class of central catadioptric cameras (including conventional cameras). It has been validated onto two architectures. In the first one, algorithms have been implemented onto a dedicated hardware and the robot is equipped with a standard perspective camera. In the second one, they have been implemented on a standard PC and an omnidirectional camera is considered.

Learning to select distinctive landmarks for mobile robot navigation

In landmark-based navigation systems for mobile robots, sensory perceptions (e.g., laser or sonar scans) are used to identify the robot’s current location or to construct internal representations, maps, of the robot’s environment. Being based on an external frame of reference (which is not subject to incorrigible drift errors such as those occurring in odometry-based systems), landmark-based robot navigation systems are now widely used in mobile robot applications.The problem that has attracted most attention to date in landmark-based navigation research is the question of how to deal with perceptual aliasing, i.e., perceptual ambiguities. In contrast, what constitutes a good landmark, or how to select landmarks for mapping, is still an open research topic. The usual method of landmark selection is to map perceptions at regular intervals, which has the drawback of being inefficient and possibly missing ‘good’ landmarks that lie between sampling points.In this paper, we present an automatic landmark selection algorithm that allows a mobile robot to select conspicuous landmarks from a continuous stream of sensory perceptions, without any pre-installed knowledge or human intervention during the selection process. This algorithm can be used to make mapping mechanisms more efficient and reliable. Experimental results obtained with two different mobile robots in a range of environments are presented and analysed.     

Application of Heuristic Asymmetric Mapping for mobile robot navigation using ultrasonic sensors

In this paper, an experimental study of a navigation system that allows a mobile robot to travel in an environment about which it has no prior knowledge is described. Data from multiple ultrasonic range sensors are fused into a representation called Heuristic Asymmetric Mapping to deal with the problem of uncertainties in the raw sensory data caused mainly by the transducer's beam-opening angle and specular reflections. It features a fast data-refresh rate to handle a dynamic environment. Potential-field method is used for on-line path planning based on the constructed gridtype sonar map. The mobile robot can therefore learn to find a safe path according to its self-built sonar map. To solve the problem of local minima in conventional potential field method, a new type of potential function is formulated. This new method is simple and fast in execution using the concept from distance-transform path-finding algorithms. The developed navigation system has been tested on our experimental mobile robot to demonstrate its possible application in practical situations. Several interesting simulation and experimental results are presented.This work was supported partly by the National Science Council of Taiwan, ROC under the grant NSC-82-0422-E-009-321.     

Mobile robot localization using a self-organized visual environment representation

Owing to the upcoming applications in the field of service robotics mobile robots are currently receiving increasing attention in industry and the scientific community. Applications in the area of service robotics demand a high degree of system autonomy, which robots without learning capabilities will not be able to meet. Learning is required in the context of action models and appropriate perception procedures. In both areas flexible adaptivity is difficult to achieve especially when high bandwidth sensors (e.g. video cameras) - which are needed in the envisioned unstructured worlds - are used. This paper proposes a new methodology for image-based navigation using a self-organized visual representation of the environment. Self-organization leads to internal representations, which can be used by the robot, but are not transparent to the user. It is shown how this conceptual gap can be bridged.     

Human-following robot using the particle filter in ISpace with distributed vision sensors

We present a method for representing tracking and human-following by fusing distributed multiple vision systems in intelligent space, with applications to pedestrian tracking in a crowd. In this context, particle filters provide a robust tracking framework under ambiguous conditions. The particle filter technique is used in this work, but in order to reduce its computational complexity and increase its robustness, we propose to track the moving objects by generating hypotheses not in the image plan but on a top-view reconstruction of the scene. Comparative results on real video sequences show the advantage of our method for multiobject tracking. Simulations are carried out to evaluate the proposed performance. Also, the method is applied to the intelligent environment, and its performance is verified by experiments. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

Sonar-based robot navigation using nonlinear robust observers

This paper addresses the sonar-based navigation of mobile robots. The extended Kalman filtering (EKF) technique is considered, but from a deterministic, nonstochastic, point of view. For this problem, new results are presented on the robustness of the nonlinear observation scheme. The original feature is that the region-of-convergence question is posed in its complete nonlinear framework, that is, considering the dynamics not only of the estimation error ζ(t), but also of the covariance matrix P(t). In this way the approach followed makes less conservative the treatment and improves the convergence analysis. The proposed ideas were tested successfully on simulation experiments of a mobile platform.     

使用单目视觉的移动机器人导航方法

机器视觉与机器人的结合是未来机器人行业发展的一大趋势。在移动机器人的避障导航方案中,使用传统的传感器存在诸多问题,且获取的信息有限。提出一种基于单目视觉的移动机器人导航算法,在算法应用中,如果使用镜头焦距已知的相机,则无需对相机标定。为降低光照对障碍物边缘检测的影响,将机器人拍摄的彩色图像转换到HSI空间。采用canny算法对转换后的分量分别进行边缘检测,并合成检测结果。通过阈值处理过滤合成边缘,去除弱边缘信息,提高检测准确度。采用形态学处理连接杂散边缘,通过区域生长得到非障碍区域,并由几何关系建立图像坐标系与机器人坐标系之间的映射关系。利用结合隶属度函数的模糊逻辑得出机器人控制参数。实验结果表明,对图像颜色空间的转换降低了地面反光、阴影的影响,算法能有效排除地面条纹等的干扰并准确检测出障碍物边缘,而模糊逻辑决策方法提高了算法的鲁棒性和结果的可靠性。     

Autonomous robot navigation using optimal control of probabilistic regular languages

This paper addresses autonomous intelligent navigation of mobile robotic platforms based on the recently reported algorithms of language-measure-theoretic optimal control. Real-time sensor data and model-based information on the robot's motion dynamics are fused to construct a probabilistic finite state automaton model that dynamically computes a time-dependent discrete-event supervisory control policy. The paper also addresses detection and avoidance of livelocks that might occur during execution of the robot navigation algorithm. Performance and robustness of autonomous intelligent navigation under the proposed algorithm have been experimentally validated on Segway RMP robotic platforms in a laboratory environment.     

光纤传感器在机器人导航中的应用

简述了光纤传感器的工作原理,并测试了颜色对BF3RX光纤传感器测试位移的影响;利用该传感器这一特性在扫查机器人中进行导航,介绍了导航原理和导航算法,实验结果表明:利用该光纤传感器进行导航,具有较高的精度,完全可以满足超声波串列扫查的要求。     

基于混合遗传模拟退火算法的矢量场机器人导航

提出了一种解析形式的机器人矢量场导航模型, 模型中场矢量的指向就是机器人的理想移动方向. 模型假设工作空间中的障碍物为多边形, 通过对障碍边界上电场的积分得到了排斥场的封闭解. 导航必须考虑路径对长度、平滑度及安全性的要求, 因此, 一种混合遗传模拟退火优化算法被用来对导航模型的参数进行搜索, 以寻找最优路径解. 仿真结果验证了本文模型的有效性, 优化所获路径的比较说明此混合算法要优于遗传算法和模拟退火算法.     

Image-based memory for robot navigation using properties of omnidirectional images

This paper proposes a new technique for vision-based robot navigation. The basic framework is to localise the robot by comparing images taken at its current location with reference images stored in its memory. In this work, the only sensor mounted on the robot is an omnidirectional camera. The Fourier components of the omnidirectional image provide a signature for the views acquired by the robot and can be used to simplify the solution to the robot navigation problem. The proposed system can calculate the robot position with variable accuracy (‘hierarchical localisation’) saving computational time when the robot does not need a precise localisation (e.g. when it is travelling through a clear space). In addition, the system is able to self-organise its visual memory of the environment. The self-organisation of visual memory is essential to realise a fully autonomous robot that is able to navigate in an unexplored environment. Experimental evidence of the robustness of this system is given in unmodified office environments.     

Robust omnidirectional mobile robot topological navigation system using omnidirectional vision

Robust topological navigation strategy for omnidirectional mobile robot using an omnidirectional camera is described. The navigation system is composed of on-line and off-line stages. During the off-line learning stage, the robot performs paths based on motion model about omnidirectional motion structure and records a set of ordered key images from omnidirectional camera. From this sequence a topological map is built based on the probabilistic technique and the loop closure detection algorithm, which can deal with the perceptual aliasing problem in mapping process. Each topological node provides a set of omnidirectional images characterized by geometrical affine and scale invariant keypoints combined with GPU implementation. Given a topological node as a target, the robot navigation mission is a concatenation of topological node subsets. In the on-line navigation stage, the robot hierarchical localizes itself to the most likely node through the robust probability distribution global localization algorithm, and estimates the relative robot pose in topological node with an effective solution to the classical five-point relative pose estimation algorithm. Then the robot is controlled by a vision based control law adapted to omnidirectional cameras to follow the visual path. Experiment results carried out with a real robot in an indoor environment show the performance of the proposed method.     

Mobile robot interception using human navigational principles: Comparison of active versus passive tracking algorithms

We examined human navigational principles for intercepting a projected object and tested their application in the design of navigational algorithms for mobile robots. These perceptual principles utilize a viewer-based geometry that allows the robot to approach the target without need of time-consuming calculations to determine the world coordinates of either itself or the target. Human research supports the use of an Optical Acceleration Cancellation (OAC) strategy to achieve interception. Here, the fielder selects a running path that nulls out the acceleration of the retinal image of an approaching ball, and maintains an image that rises at a constant rate throughout the task. We compare two robotic control algorithms for implementing the OAC strategy in cases in which the target remains in the sagittal plane headed directly toward the robot (which only moves forward or backward). In the “passive” algorithm, the robot keeps the orientation of the camera constant, and the image of the ball rises at a constant rate. In the “active” algorithm, the robot maintains a camera fixation that is centered on the image of the ball and keeps the tangent of the camera angle rising at a constant rate. Performance was superior with the active algorithm in both computer simulations and trials with actual mobile robots. The performance advantage is principally due to the higher gain and effectively wider viewing angle when the camera remains centered on the ball image. The findings confirm the viability and robustness of human perceptual principles in the design of mobile robot algorithms for tasks like interception. Thomas Sugar works in the areas of mobile robot navigation and wearable robotics assisting gait of stroke survivors. In mobile robot navigation, he is interested in combining human perceptual principles with mobile robotics. He majored in business and mechanical engineering for his Bachelors degrees and mechanical engineering for his Doctoral degree all from the University of Pennsylvania. In industry, he worked as a project engineer for W. L. Gore and Associates. He has been a faculty member in the Department of Mechanical and Aerospace Engineering and the Department of Engineering at Arizona State University. His research is currently funded by three grants from the National Sciences Foundation and the National Institutes of Health, and focuses on perception and action, and wearable robots using tunable springs. Michael McBeath works in the area combining Psychology and Engineering. He majored in both fields for his Bachelors degree from Brown University and again for his Doctoral degree from Stanford University. Parallel to his academic career, he worked as a research scientist at NASA—Ames Research Center, and at the Interval Corporation, a technology think tank funded by Microsoft co-founder, Paul Allen. He has been a faculty member in the Department of Psychology at Kent State University and at Arizona State University, where he is Program Director for the Cognition and Behavior area, and is on the Executive Committee for the interdisciplinary Arts, Media, and Engineering program. His research is currently funded by three grants from the National Sciences Foundation, and focuses on perception and action, particularly in sports. He is best known for his research on navigational strategies used by baseball players, animals, and robots.     

基于虚拟导航线的农业机器人精确视觉导航方法

针对农田、野外环境中无人工标记情况下的导航问题,提出了一种基于虚拟导航线的农业机器人精确视觉导航方法。该方法不需要铺设导航线或者路标即可引导机器人行走直线。首先,根据需求确定需要跟踪的目标区域,之后控制机器人调整方向直到目标移至视野中央;其次,根据机器人和目标的位置确定参照目标,并依据两个目标的位置确定虚拟导航线;然后,动态更新导航线,并结合虚拟定标线和虚拟导航线确定偏移角度和偏移距离;最后,利用偏移参数构建模糊控制表,并以此实现对机器人旋转角度和行走速度的调整。实验结果表明,该算法能较为精确地实现对导航路线的识别,进而利用模糊控制策略使机器人沿直线向目标行走,且导航精度在10 cm以内。     

面向医院的移动机器人导航系统设计

本文设计了一种采用RFID和机器视觉相结合的自主移动机器人导航系统.该系统在搜索和识别走廊内RFID标签和门牌号的基础上实现自定位和导航.本文首先给出了移动机器人导航系统的整体设计,然后分别给出了基于RFID辅助定位方法和基于DSP的门牌号识别方法,最后在博创UpVoyager移动机器人平台上验证了本系统的有效性.     

On-line road boundary modeling with multiple sensory features, flexible road model, and particle filter

This paper describes a method of robustly modeling road boundaries on-line for autonomous navigation. Since sensory evidence for road boundaries might change from place to place, we cannot depend on a single cue but have to use multiple sensory features. It is also necessary to cope with various road shapes and road type changes. These requirements are naturally met in the proposed particle filter-based method, which makes use of multiple features with the corresponding likelihood functions and keeps multiple road hypotheses as particles. The proposed method has been successfully applied to various road scenes with cameras and a laser range finder. To show that the proposed method is applicable to other sensors, preliminary results of using stereo instead of the laser range finder are also described.     

基于粒子滤波的无人机自主轨迹视觉导航控制方法研究

针对现有无人机导航控制方法存在的控制效果不佳的问题,本文提出一种基于粒子滤波的无人机自主轨迹视觉导航控制方法研究。利用粒子滤波算法,实现对无人机自主轨迹视觉导航控制方法的优化设计。采用栅格法构建无人机飞行环境地图,根据无人机的机械组成结构和工作原理,构建运动状态模型。利用内置的摄像机设备采集视觉图像,执行图像灰度转换、几何校正、滤波等预处理步骤。通过对视觉图像的特征提取,判断当前环境是否存在障碍物。利用粒子滤波算法确定无人机位姿,结合障碍物识别结果规划无人机的自主飞行轨迹。将位置、速度和姿态角的控制量计算结果,输入到安装的导航控制器中,完成无人机的自主轨迹视觉导航控制任务。通过实测分析得出结论：应用设计的导航控制方法,其位置误差、速度误差以及姿态角误差均维持在预设值以下,即设计的导航控制方法具有良好的控制效果。     

Biologically inspired visual odometer for navigation of a flying robot

Experimental research in biology has uncovered a number of different ways in which flying insects use cues derived from optical flow for navigational purposes, such as safe landing, obstacle avoidance and dead reckoning. In this study, we use a synthetic methodology to gain additional insights into the navigation behavior of bees. Specifically, we focus on the mechanisms of course stabilization behavior and visually mediated odometer by using a biological model of motion detector for the purpose of long-range goal-directed navigation in 3D environment. The performance tests of the proposed navigation method are conducted by using a blimp-type flying robot platform in uncontrolled indoor environments. The result shows that the proposed mechanism can be used for goal-directed navigation. Further analysis is also conducted in order to enhance the navigation performance of autonomous aerial vehicles.     

液下搅拌机器人的超声波导航定位研究

针对研究在水煤浆环境下工作的机器人,提出了采用超声波进行导航定位的方法;详细介绍了该定位系统的硬件布置方案、定位数学模型,并在水煤浆介质中做了超声波传播实验。