UHF-RFID环境下的移动机器人定位方法

研究UHF-RFID环境中移动机器人的定位问题,提出一种基于自适应UKF滤波器组的移动机器人定位方法,融合UHF-RFID和机器人内部传感器信息,以实现初始位姿未知的移动机器人定位.首先,利用UHF-RFID系统对移动机器人进行初始定位,并根据其初始位置信息随机生成移动机器人的初始状态估计集;然后,考虑UHF-RFID系统定位的量化误差,应用自适应UKF方法对机器人的状态估计集进行预测和更新,并对状态估计集进行有效地裁剪、筛选以及更新,以提高滤波器的估计精度和稳定性.仿真结果表明,相比于标准UKF滤波方法,自适应UKF滤波器组方法具有更高的定位精度和更快的收敛速度.     

无线传感器网络环境下基于粒子滤波的移动机器人SLAM算法

定位问题是移动机器人研究领域中最基本的问题,在Bayes的框架下研究了机器人与无线传感器网络(WSN)组成系统中的同时建图与定位问题(SLAM).针对该系统中只存在距离测量信息可用的情况提出了一种基于粒子滤波的SLAM算法.该方法将机器人状态和节点位置估计设置为一组全局估计粒子,通过对粒子及其权重的更新来计算整个系统的状态.算法将WSN节点的位置估计在机器人的路径上分解为相互独立的估计,从而将全局粒子的计算转化为使用一个机器人状态滤波器和对应于每个机器人粒子的节点位置滤波器进行计算.针对观测信息低维的特点,设计了处理低维观测信息的方法,使得观测信息可以在滤波阶段得到合理利用.并且详细介绍了提出的SLAM算法原理和计算过程,并通过仿真实验证明了算法的有效性和实用性.     

基于RGB-D相机的室内移动机器人自定位方法

精准可靠的自定位是移动机器人实现多机协同、路径规划与控制决策等自主能力的基础。因此,室内服务机器人作为移动机器人的典型代表,要求能够实时进行自定位,并且有效地避开各种静态和动态障碍物。基于此,提出一种基于RGB-D相机的室内移动机器人自定位方法,该方法利用Hough变换对机器人建立的环境地图进行线特征提取,并建立环境模型误差查找表,将非结构化环境中的自定位问题转化为结构化环境中的自定位问题,然后利用匹配优化算法实现自定位。实验结果表明,采用所提出的方法,机器人能够实现室内环境下实时精确的自定位。     

基于LiDAR/INS的野外移动机器人组合导航方法

移动机器人在地形复杂等野外环境跨区域运动时,机器人运动特性和环境特征变化更为明显,由此引起的点云畸变和特征点稀疏等问题尤为突出,有必要结合传感器标定误差、车轮打滑和车体颠簸等因素进一步改进机器人的位姿估计精度。本文对基于LiDAR/INS的移动机器人环境建模和自主导航方法展开研究,针对LeGO-LOAM等在处理车体姿态快速变化时的性能退化问题,提出一种适用于野外移动机器人运动特性的点云特征分析和多传感融合方法,利用IMU的预积分与LiDAR的scan-to-map构成优化函数,进而迭代更新机器人的位姿。野外环境实验结果表明,当机器人以较高速度做转弯运动或在短时间内多次转向时,本文所提方法仍可以为优化提供良好的初值估计,相比LeGO-LOAM等方法具有更高的位姿估计精度。     

一种有效的移动机器人里程计误差建模方法

移动机器人里程计误差建模是研究移动机器人定位问题的基础. 现有的移动机器人里程计误差建模方法多数针对某一种驱动类型移动机器人设计, 运动过程中缺乏对里程计累计误差的实时反馈补偿, 经过长距离运动过程定位精度大幅度降低. 因此本文针对同步驱动和差动驱动轮式移动机器人平台提出了一种通用的里程计误差建模方法. 在假设机器人运动路径近似弧线基础上, 依据里程计误差传播规律推导了非系统误差、系统误差与里程计过程输入之间的近似函数关系, 进而提出一种具有闭环误差实时反馈补偿功能的移动机器人定位算法, 对定位过程中产生的里程计累计误差给予实时反馈补偿. 实验表明新算法有效地减少了里程计累计误差, 提高了定位精度.     

分布式感知协作的扩展Monte Carlo定位算法

针对移动机器人难以单纯依赖自身传感器定位的问题,提出了一种分布式感知协作的扩展Monte Carlo定位方法.在定位过程中,机器人根据感知更新前后采样分布信息熵、有效采样数目及采样分布均匀性的变化,适时地从环境传感器的检测模型进行重采样,从而有效减少其位姿估计的不确定性.在算法的具体实现过程中,采用彩色摄像头作为环境传感器,摄像头的参数由机器人进行在线标定;然后依据标定的参数获得摄像头的检测模型.实验验证了该算法在解决全局定位和机器人绑架问题时的有效性.     

未知环境下的移动机器人定位及实时避障

针对未知环境下移动机器人实时动态避障及定位问题，考虑里程计定位的无界累加误差和动态障碍物环境下实时障碍躲避需要，提出了一种可行的避障定位的策略。该策略融合了机器人内部传感器、里程计、电子罗盘和激光测距仪的同步和异步信息，合理地解决了常规定位过程中的方向迷失问题，对于静态和动态障碍物都能很好地实时躲避，具有很强的抗干扰性和较高的定位精度。实验证明了该方法的有效性和实用性.     

自主移动机器人足球比赛视觉定位方法综述

综述了RoboCup足球赛中全自主移动机器人基于视觉的定位技术,包括机器人自定位和多机器人协作物体定位.介绍了定位技术的发展情况与分类.从机器人环境构建形式的不同以及先验位姿和概率方法的应用与否等方面,系统地分析和比较了各种自定位方法.对于多机器人协作物体定位,阐述了静态方法和动态跟踪方法.总结了定位过程中需要重点研究的传感器模型构建、图像处理、特征匹配以及协作过程涉及的相关问题.最后就视觉定位存在的问题和技术发展趋势进行了讨论.     

WSNs环境下基于高斯混合容积卡尔曼滤波的移动机器人定位算法

针对移动机器人的定位问题,提出一种面向无线传感器网络WSNs( Wireless Sensor Networks)环境下,结合高斯混合容积卡尔曼滤波( GM￣CKF)优化的定位算法。将WSNs对移动机器人的观测、机器人自身对环境特征的观测以及机器人自身运动控制量进行数据融合,并利用带有门限判别和选择性高斯分割的GM￣CKF算法,对机器人的预估位置实施预测修正,降低计算求解的空间维数,提高定位精度。仿真实验结果表明,所提出的方法比传统机器人自定位法定位精度有所提高,算法精度较标准的CKF算法提高了39.11％,比EKF算法提高了65.81％。     

可移动机器人在中心对称环境中的自定位算法

可移动机器人的自定位问题是智能机器人研究中的重要课题，它包含许多传感器技术和定位算法，马尔可夫定位算法的优点是可以使机器人在全局不确定的情况下估计它的位置。这种方法采用概率分布描述机器人的位置信度，机器人通过在运动过程中所获得的传感器数据和运动记录来更新信度分布，然后采用最高信度值来估计它所在的位置。对于只有距离测量传感器的机器人在中心对称环境中仅仅采用马尔可夫自定位法还是无法确定其位置，为了解决中心对称的环境中所存在的问题，建议在机器人上装上陀螺仪或指南针，定义一个角度高斯分布函数，并利用这个函数建立新的机器人感知模型来扩展马尔可夫定位算法，通过仿真程序对多种对称情况进行实验，验证了这一新算法的可行性，这个扩展马尔可夫自定位算法不仅可使机器人在中心对称环境中很快地确定自己的位置，而且可以加快非对称环境中信度分布收敛到真实位置的速度。     

一种基于SIFT特征的机器人环境感知双目立体视觉系统

针对双目立体视觉系统在机器人环境感知领域中存在的立体匹配以及测量精度问题,设计一种基于SIFT特征的双目立体视觉测量系统.利用SIFT特征良好的旋转、尺度、光照不变性等特性,有效地解决双目立体视觉系统的匹配问题,同时将SIFT算法在由FPGA和DSP组成的嵌入式系统上实现,显著地提高测量系统的实时性.提出一种基于二次多项式的误差补偿方法,对系统的测量结果进行补偿,弥补双目立体视觉系统测量误差随测量距离增加而增加的不足,从而提高系统测量精度.通过实际的测量实验、移动机器人环境感知实验以及与现有双目立体视觉产品的对比实验结果表明,系统能够很好地解决双目立体视觉的立体匹配和精度问题,并且能较好地应用于移动机器人环境感知任务中.     

Cooperative localization and control of multiple heterogeneous robots using a string formation

This paper is on cooperative localization and control of multiple heterogeneous robots utilizing a string formation. This formation is preferred, since robots can move along a narrow passage utilizing this formation. Dead reckoning localization based on inertial measurement units leads to accumulated localization error. To avoid the error accumulation in dead reckoning localization, this paper introduces the last-move strategy for multiple heterogeneous robots. In the last-move strategy, a single robot is selected for maneuvering, and it turns on its bearing-range sensors for a short amount of time, in order to locate itself. While the selected robot moves, all other robots stop moving and perform as static landmarks for the moving robot. A robot may not maintain its desired course, in the case where environmental disturbance is severe. We thus develop a control strategy for avoiding obstacles while estimating the disturbance direction at a robot's location. To the best of our knowledge, this paper is novel in localization and control of a team of heterogeneous robots, considering the case where environmental disturbance is severe. The proposed localization process is energy-efficient, thus is suitable for practical applications. The performance of the proposed schemes is demonstrated utilizing MATLAB simulations.     

Particle Filter for Collaborative Multi-Robot Localization Tolerant to Recognition Error

Statistical algorithms using particle filters have been proposed previously for collaborative multi-robot localization. In these algorithms, by synchronizing each robot's belief or exchanging the particles of the robots, fast and accurate localization is attained. However, there algorithms assume correct recognition of other robots and the effects of recognition error are not considered. If the recognition of other robots is incorrect, a large amount of error in localization can occur. This paper describes this problem. Furthermore, in order to cope with the problem, an algorithm for collaborative multi-robot localization is proposed. In the proposed algorithm, the particles of a robot are exchanged with those of other robots according to measurement results obtained by the sending robot. At the same time, some particles remain in the sending robot. Received particles from other robots are evaluated using measurement results obtained by the receiving robot. The proposed method copes with recognition error by using the remaining particles, and increases the accuracy of estimation by twice evaluating the exchanged particles of the sending and receiving robots. These properties of the proposed method are argued mathematically. Simulation results show that incorrect recognition of other robots does not cause serious problems in the proposed method.     

A Localization Method Based on Map-Matching and Particle Swarm Optimization

This paper presents a novel localization method for small mobile robots. The proposed technique is especially designed for the Robot@Factory, a new robotic competition which is started in Lisbon in 2011. The real-time localization technique resorts to low-cost infra-red sensors, a map-matching method and an Extended Kalman Filter (EKF) to create a pose tracking system that performs well. The sensor information is continuously updated in time and space according to the expected motion of the robot. Then, the information is incorporated into the map-matching optimization in order to increase the amount of sensor information that is available at each moment. In addition, the Particle Swarm Optimization (PSO) relocates the robot when the map-matching error is high, meaning that the map-matching is unreliable and the robot gets lost. The experiments presented in this paper prove the ability and accuracy of the presented technique to locate small mobile robots for this competition. Extensive results show that the proposed method presents an interesting localization capability for robots equipped with a limited amount of sensors, but also less reliable sensors.     

A new space and time sensor fusion method for mobile robot navigation

To fully utilize the information from the sensors of mobile robot, this paper proposes a new sensor‐fusion technique where the sample data set obtained at a previous instant is properly transformed and fused with the current data sets to produce a reliable estimate for navigation control. Exploration of an unknown environment is an important task for the new generation of mobile service robots. The mobile robots may navigate by means of a number of monitoring systems such as the sonar‐sensing system or the visual‐sensing system. Notice that in the conventional fusion schemes, the measurement is dependent on the current data sets only. Therefore, more sensors are required to measure a given physical parameter or to improve the reliability of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequences of the data sets are stored and utilized for the purpose. The basic principle is illustrated by examples and the effectiveness is proved through simulations and experiments. The newly proposed STSF (space and time sensor fusion) scheme is applied to the navigation of a mobile robot in an environment using landmarks, and the experimental results demonstrate the effective performance of the system. © 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.     

Localization of a mobile robot based on an ultrasonic sensor using dynamic obstacles

In this article, we propose a localization scheme for a mobile robot based on the distance between the robot and moving objects. This method combines the distance data obtained from ultrasonic sensors in a mobile robot, and estimates the location of the mobile robot and the moving object. The movement of the object is detected by a combination of data and the object’s estimated position. Then, the mobile robot’s location is derived from the a priori known initial state. We use kinematic modeling that represents the movement of a robot and an object. A Kalman-filtering algorithm is used for addressing estimation error and measurement noise. Throughout the computer simulation experiments, the performance is verified. Finally, the results of experiments are presented and discussed. The proposed approach allows a mobile robot to seek its own position in a weakly structured environment. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

A Case Study of the Collision-Avoidance Problem Based on Bernstein–Bézier Path Tracking for Multiple Robots with Known Constraints

In this paper a case study of a new, cooperative, collision-avoidance method for multiple, nonholonomic robots based on Bernstein–Bézier curves is given. In the presented examples the velocities and accelerations of the mobile robots are constrained and the start and the goal velocity are defined for each robot. This means that the proposed method can be used as a subroutine in a huge path-planning problem in real time, in a way to split the whole path into smaller partial paths. The reference path of each robot, from the start pose to the goal pose, is obtained by minimizing the penalty function, which takes into account the sum of all the path lengths subjected to the distances between the robots, which should be bigger than the minimum distance defined as the safety distance, and subjected to the velocities and accelerations which should be lower than the maximum allowed for each robot. When the reference paths are defined the model-predictive trajectory tracking is used to define the control. The prediction model derived from the linearized tracking-error dynamics is used to predict future system behavior. The control law is derived from a quadratic cost function consisting of the system tracking error and the control effort. The proposed method was tested with a simulation and with a real-time experiment in which four robots were used.     

Fuzzy target tracking control of autonomous mobile robots by using infrared sensors

The theme of this paper is to design a real-time fuzzy target tracking control scheme for autonomous mobile robots by using infrared sensors. At first two mobile robots are setup in the target tracking problem, where one is the target mobile robot with infrared transmitters and the other one is the tracker mobile robot with infrared receivers and reflective sensors. The former is designed to drive in a specific trajectory. The latter is designed to track the target mobile robot. Then we address the design of the fuzzy target tracking control unit, which consists of a behavior network and a gate network. The behavior network possesses the fuzzy wall following control (FWFC) mode, fuzzy target tracking control (FTTC) mode, and two fixed control modes to deal with different situations in real applications. Both the FWFC and FTTC are realized by the fuzzy sliding-mode control scheme. A gate network is used to address the fusion of measurements of two infrared sensors and is developed to recognize which situation is belonged to and which action should be executed. Moreover, the target tracking control with obstacle avoidance is also investigated in this paper. Both computer simulations and real-time implementation experiments of autonomous target tracking control demonstrate the effectiveness and feasibility of the proposed control schemes.     

Motion-based identification of multiple mobile robots using trajectory analysis in a well-configured environment with distributed vision sensors

Networked mobile robots are able to determine their poses (i.e., position and orientation) with the help of a well-configured environment with distributed sensors. Before localizing the mobile robots using distributed sensors, the environment has to have information on each of the robots?? prior knowledge. Consequently, if the environment does not have information on the prior knowledge of a certain mobile robot then it will not determine its current pose. To solve this restriction, as a preprocessing step for indoor localization, we propose a motion-based identification of multiple mobile robots using trajectory analysis. The proposed system identifies the robots by establishing the relation between their identities and their positions, which are estimated from their trajectories related to each of the paths generated as designated signs. The primary feature of the proposed system is the fact that networked mobile robots are quickly and simultaneously able to determine their poses in well-configured environments. Experimental results show that our proposed system simultaneously identifies multiple mobile robots, and approximately estimates each of their poses as an initial state for autonomous localization.