Concurrent localization of multiple robots

A concurrent localization method for multiple robots using ultrasonic beacons is proposed. This method provides a high-accuracy solution using only low-price sensors. To measure the distance of a mobile robot from a beacon at a known position, the mobile robot alerts one beacon to send out an ultrasonic signal to measure the traveling time from the beacon to the mobile robot. When multiple robots requiring localization are moving in the same block, it is necessary to have a schedule to choose the measuring sequence in order to overcome constant ultrasonic signal interference among robots. However, the increased time delay needed to estimate the positions of multiple robots degrades the localization accuracy. To solve this problem, we propose an efficient localization algorithm for multiple robots, where the robots are in groups of one master robot and several slave robots. In this method, when a master robot calls a beacon, all the group robots simultaneously receive an identical ultrasonic signal to estimate their positions. The effectiveness of the proposed algorithm has been verified through experiments.     

Dynamic localization with hybrid trilateration for mobile robots in intelligent space

In this paper, we propose a new localization algorithm based on a hybrid trilateration algorithm for obtaining an accurate position of a robot in intelligent space. The proposed algorithm is also able to estimate a position of the moving robot by using the extended Kalman filter, taking into consideration time synchronization and velocity of the robot. For realizing the localization system, we employ several smart sensors as beacons on the ceiling in intelligent space and as a listener attached to the robot. Finally, simulation results show the feasibility and effectiveness of the proposed localization algorithm compared with existing trilateration algorithms.     

可移动机器人的马尔可夫自定位算法研究

马尔可夫定位算法是利用机器人运动环境中的概率密度分布进行定位的方法.使用该 方法机器人可在完全不知道自己位置的情况下通过传感器数据和运动模型来估计自己的位置. 但是,在研究中发现它还存在一些问题,如概率减小到零后就无法恢复.对只有距离传感器的机 器人在对称的环境中仅仅采用该算法就无法确定位置.为了解决这些问题,文中给出了修正算 法,并建议在机器人上装上方向仪(如指南针或陀螺仪等),然后利用定义的一个角度高斯分布 函数来构造新的机器人感知模型.在此基础上详细地阐述了一种新的自定位技术.最后,采用仿 真程序验证了机器人在对称环境中运动时这一新算法的可行性.     

Intelligent and Efficient Strategy for Unstructured Environment Sensing Using Mobile Robot Agents

In field environments it is not usually possible to provide robots in advance with valid geometric models of its task and environment. The robot or robot teams need to create these models by scanning the environment with its sensors. Here, an information-based iterative algorithm to plan the robot's visual exploration strategy is proposed to enable it to most efficiently build 3D models of its environment and task. The method assumes mobile robot (or vehicle) with vision sensors mounted at a manipulator end-effector (eye-in-hand system). This algorithm efficiently repositions the systems' sensing agents using an information theoretic approach and fuses sensory information using physical models to yield a geometrically consistent environment map. This is achieved by utilizing a metric derived from Shannon's information theory to determine optimal sensing poses for the agent(s) mapping a highly unstructured environment. This map is then distributed among the agents using an information-based relevant data reduction scheme. This method is particularly well suited to unstructured environments, where sensor uncertainty is significant. Issues addressed include model-based multiple sensor data fusion, and uncertainty and vehicle suspension motion compensation. Simulation results show the effectiveness of this algorithm.     

A Localization Algorithm for Autonomous Mobile Robots via a Fuzzy Tuned Extended Kalman Filter

The capability to acquire the position and orientation of an autonomous mobile robot is an important element for achieving specific tasks requiring autonomous exploration of the workplace. In this paper, we present a localization method that is based on a fuzzy tuned extended Kalman filter (FT-EKF) without a priori knowledge of the state noise model. The proposed algorithm is employed in a mobile robot equipped with 16 Polaroid sonar sensors and tested in a structured indoor environment. The state noise model is estimated and adapted by a fuzzy rule-based scheme. The proposed algorithm is compared with other EKF localization methods through simulations and experiments. The simulation and experimental studies demonstrate the improved performance of the proposed FT-EKF localization method over those using the conventional EKF algorithm.     

一种新的基于RFID的室内移动机器人自定位方法研究

针对室内移动机器人自定位算法定位精度不高、定位误差存在波动的问题,提出了一种RTFL(RFID tag floor based localization)定位算法与RSSI定位算法相结合的室内移动机器人自定位方法。由RTFL定位算法给定机器人位置估算初值和机器人所在的范围,然后通过基于RSSI的机器人自定位系统进行机器人位置的进一步精确定位。求解过程中,通过遗传算法求解极大似然方程组,并且提出了染色体的筛选和剔除策略。仿真实验结果表明：该方法在有效的时间内完成定位,平均定位误差为0.1572m,与传统的改进方法0.33214m的定位误差相比,降低了近一倍。并且新方法受环境影响较小,鲁棒性较好,能够很好的满足室内移动机器人的定位要求。     

Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm

This paper discusses odor source localization (OSL) using a mobile robot in an outdoor time-variant airflow environment. A novel OSL algorithm based on particle filters (PF) is proposed. When the odor plume clue is found, the robot performs an exploratory behavior, such as a plume-tracing strategy, to collect more information about the previously unknown odor source. In parallel, the information collected by the robot is exploited by the PF-based OSL algorithm to estimate the location of the odor source in real time. The process of the OSL is terminated if the estimated source locations converge within a given small area. The Bayesian-inference-based method is also performed for comparison. Experimental results indicate that the proposed PF-based OSL algorithm performs better than the Bayesian-inference-based OSL method.     

局域精确定位的工业移动机器人惯性导航方案

为解决工业移动机器人投入使用前需沿路密集布设地面磁条或激光反射点等外部装置的复杂性,以及因工作路线固定而缺少灵活性的问题,提出了基于惯性器件和超声波传感器的局域导航方案。操作人员事先通过遥控器训练机器人从一目标位置沿特定路线运动到另一目标位置,从而生成训练路线。机器人工作在非目标区域,即定位精度要求不高的区域时,按训练路线进行惯性导航;进入到目标区域,即包含目标位置、需精准定位的区域时,利用超声波进行导航。所提方案缩短了使用前的准备周期,同时方便更改工作路线。通过仿真实验,验证了局域范围使用惯性导航和超声波定位相结合的方案是合理可行的。     

A Modified Particle Filter for Simultaneous Localization and Mapping

The implementation of a particle filter (PF) for vision-based bearing-only simultaneous localization and mapping (SLAM) of a mobile robot in an unstructured indoor environment is presented in this paper. Variations, using techniques from the genetic algorithm (GA), to standard PF procedures are proposed to alleviate the sample impoverishment problem. A monochrome CCD camera mounted on the robot is used as the measuring device and a measure on the image quality is incorporated into data association and PF update. Since the bearing-only measurement does not contain range information, we add a pseudo range to the measurement during landmark initialization as a hypothesised pair and the non-promising landmark is removed by a map management strategy. Simulation and experimental results from an implementation using real-life data acquired from a Pioneer robot are included to demonstrate the effectiveness of our approach.     

An optimized modular neural network controller based on environment classification and selective sensor usage for mobile robot reactive navigation

A new approach to the design of a neural network (NN) based navigator is proposed in which the mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment. This navigator can be optimized for any user-defined objective function through the use of an evolutionary algorithm. The motivation of this research is to develop an efficient methodology for general goal-directed navigation in generic indoor environments as opposed to learning specialized primitive behaviors in a limited environment. To this end, a modular NN has been employed to achieve the necessary generalization capability across a variety of indoor environments. Herein, each NN module takes charge of navigating in a specialized local environment, which is the result of decomposing the whole path into a sequence of local paths through clustering of all the possible environments. We verify the efficacy of the proposed algorithm over a variety of both simulated and real unstructured indoor environments using our autonomous mobile robot platform.     

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。     

自主式微小型移动机器人群体行为进化的研究

自主式微小型移动机器人群体面临的一些环境常常是未知的、无结构的,同时由于其 自身体积大小的限制,在目前的工业水平上也很难在其上安装一些较为先进的传感器,以致 机器人仅能获取局部的信息,这些原因使得采用传统基于任务的设计方法将十分困难,而采 用基于行为的设计方法时,也很难保证所设计的机器人行为的有效性,为此本文采用了遗传 算法,随机产生了机器人群体中各初始个体的障碍物回避行为及机器设备故障排除行为,当 群体在特定的工作环境中仿真运行时,根据环境的情况和所需实现的任务,使群体行为性能 达到了较为优化的目的．     

履带腿式非结构环境移动机器人特性分析

针对非结构环境的特点,提出一种适用于非结构环境的新型履带腿移动机器人.这种移动机器人结构简洁,实用,具有很好的环境适应能力.本文主要分析该移动机器人在非结构环境下的斜坡行走、越障、跨沟、阶梯地形行走、自动复位等功能.􀁱 􀁽     

基于EKF的全景视觉机器人SLAM算法

研究全景视觉机器人同时定位和地图创建（SLAM）问题。针对普通视觉视野狭窄, 对路标的连续跟踪和定位能力差的问题, 提出了一种基于改进的扩展卡尔曼滤波（EKF）算法的全景视觉机器人SLAM方法, 用全景视觉得到机器人周围的环境信息, 然后从这些信息中提取出环境特征, 定位出路标位置, 进而通过EKF算法同步更新机器人位姿和地图库。仿真实验和实体机器人实验结果验证了该算法的准确性和有效性, 且全景视觉比普通视觉定位精度更高。     

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     

移动机器人编队视觉定位方法研究

该文提出一种基于视觉的移动机器人编队定位方法。该方法采用基于纹理的图象对机器人进行标识,然后使用纹理中的特征点对机器人的位置和姿态进行估计,使用最小二乘算法使估计结果误差最小。实验证明该方法能够有效地对编队的移动机器人定位,同时对环境干扰具有鲁棒性。     

Mobile robot navigation using grid line patterns via probabilistic measurement modeling

Mobile robots are generally equipped with proprioceptive motion sensors such as odometers and inertial sensors. These sensors are used for dead-reckoning navigation in an indoor environment where GPS is not available. However, this dead-reckoning scheme is susceptible to drift error in position and heading. This study proposes using grid line patterns which are often found on the surface of floors or ceilings in an indoor environment to obtain pose (i.e., position and orientation) fix information without additional external position information by artificial beacons or landmarks. The grid lines can provide relative pose information of a robot with respect to the grid structure and thus can be used to correct the pose estimation errors. However, grid line patterns are repetitive in nature, which leads to difficulties in estimating its configuration and structure using conventional Gaussian filtering that represent the system uncertainty using a unimodal function (e.g., Kalman filter). In this study, a probabilistic sensor model to deal with multiple hypotheses is employed and an online navigation filter is designed in the framework of particle filtering. To demonstrate the performance of the proposed approach, an experiment was performed in an indoor environment using a wheeled mobile robot, and the results are presented.     

Extensions to vector field SLAM for large environments

Vector field SLAM is a framework for localizing a mobile robot in an unknown environment by learning the spatial distribution of continuous signals such as those emitted by WiFi or active beacons. In our previous work we showed that this approach is capable of keeping a robot localized in small to medium sized areas, e.g. in a living room, where four continuous signals of an active beacon are measured (Gutmann et al., 2012). In this article we extend the method to larger environments up to the size of a complete home by deploying more signal sources for covering the expanded area. We first analyze the complexity of vector field SLAM with respect to area size and number of signals and then describe an approximation that divides the localization map into decoupled sub-maps to keep memory and run-time requirements low. We also describe a method for re-localizing the robot in a vector field previously mapped. This enables a robot to resume its navigation after it has been kidnapped or paused and resumed. The re-localization method is evaluated in a standard test environment and shows an average position accuracy of 10 to 35 cm with a localization success rate of 96 to 99%. Additional experimental results from running the system in houses of up to 125 m² demonstrate the performance of our approach. The presented methods are suitable for commercial low-cost products including robots for autonomous and systematic floor cleaning.