基于光流传感器的移动机器人定位方法

详细介绍了光流传感器移动机器人定位系统的运动学原理,给出了理论模型推导;基于理论模型,搭建了基于光流传感器实验装置,将3只传感器置于移动机器人的固定位置,通过运动学几何关系,解算机器人的位姿;在室内进行了移动机器人的导航实验,推算了机器人的预测轨迹.实验证明了理论的可行性,可以作为短距离定位的一种导航方案.     

Estimation and control of the attitude of a dynamic mobile robot using internal sensors

For the control of a dynamic mobile robot, the attitude in gravity space is an important state of the robot. Usually, the attitude is difficult to detect by simply using the signals from sensors. For example, an external sensor contacting the ground suffers disturbances from the roughness of the ground; the integration of a gyroscope signal has the problem of drift; an inclination sensor does not indicate the direction of gravity when acceleration exists. To solve these problems, we propose a control method in which the attitude of a mobile robot is estimated by an observer considering the robot dynamics and using only the information obtained by internal sensors. We applied this method to a wheeled inverted pendulum as an example of a dynamic mobile robot. The estimation of the attitude was made with good accuracy using the signals from the rate gyroscope and the motor encoder, and the control of stable running of the pendulum on a flat level plane worked successfully. We also realized the running control of a pendulum on an unknown rough road using the estimation of the slope gradient made by the observer. Thus, the effectiveness of the proposed method was demonstrated experimentally.     

Cross-spectral visual simultaneous localization and mapping (SLAM) with sensor handover

In this work, we examine the classic problem of robot navigation via visual simultaneous localization and mapping (SLAM), but introducing the concept of dual optical and thermal (cross-spectral) sensing with the addition of sensor handover from one to the other. In our approach we use a novel combination of two primary sensors: co-registered optical and thermal cameras. Mobile robot navigation is driven by two simultaneous camera images from the environment over which feature points are extracted and matched between successive frames. A bearing-only visual SLAM approach is then implemented using successive feature point observations to identify and track environment landmarks using an extended Kalman filter (EKF). Six-degree-of-freedom mobile robot and environment landmark positions are managed by the EKF approach illustrated using optical, thermal and combined optical/thermal features in addition to handover from one sensor to another. Sensor handover is primarily targeted at a continuous SLAM operation during varying illumination conditions (e.g., changing from night to day). The final methodology is tested in outdoor environments with variation in the light conditions and robot trajectories producing results that illustrate that the additional use of a thermal sensor improves the accuracy of landmark detection and that the sensor handover is viable for solving the SLAM problem using this sensor combination.     

Self-localization and obstacle avoidance for a mobile robot

This article presents a fast self-localization method based on ZigBee wireless sensor network and laser sensor, an obstacle avoidance algorithm based on ultrasonic sensors for a mobile robot. The positioning system and positioning theory of ZigBee which can obtain a rough global localization of the mobile robot are introduced. To realize accurate local positioning, a laser sensor is used to extract the features from environment, then the environmental features and global reference map can be matched. From the matched environmental features, the position and orientation of the mobile robot can be obtained. To enable the mobile robot to avoid obstacle in real-time, a heuristic fuzzy neural network is developed by using heuristic fuzzy rules and the Kohonen clustering network. The experiment results show the effectiveness of the proposed method.     

Kinematic calibration for collaborative robots on a mobile platform using motion capture system

For modern robotic applications that go beyond the typical industrial environment, absolute accuracy is one of the key properties that make this possible. There are several approaches in the literature to improve robot accuracy for a typical industrial robot mounted on a fixed frame. In contrast, there is no method to improve robot accuracy when the robot is mounted on a mobile base, which is typical for collaborative robots. Therefore, in this work, we proposed and analyzed two approaches to improve the absolute accuracy of the robot mounted on a mobile platform using an optical measurement system. The first approach is based on geometric operations used to calculate the rotation axes of each joint. This approach identifies all rotational axes, which allows the calculation of the Denavit–Hartenberg (DH) parameters and thus the complete kinematic model, including the position and orientation errors of the robot end-effector and the robot base. The second approach to parameter estimation is based on optimization using a set of joint positions and end-effector poses to find the optimal DH parameters. Since the robot is mounted on a mobile base that is not fixed, an optical measurement system was used to dynamically and simultaneously measure the position of the robot base and the end-effector. The performance of the two proposed methods was analyzed and validated on a 7-DoF Franka Emika Panda robot mounted on a mobile platform PAL Tiago-base. The results show a significant improvement in absolute accuracy for both proposed approaches. By using the proposed approach with the optical measurement system, we can easily automate the estimation of robot kinematic parameters with the aim of improving absolute accuracy, especially in applications that require high positioning accuracy.     

Optimal location of mouse sensors on mobile robots for position sensing

Optical mouse sensors have been utilized recently to measure the position and orientation of a mobile robot. This work provides a systematic solution to the problem of locating N optical mouse sensors on a mobile robot with the aim of increasing the quality of the position measurements. The developed analysis gives insights on how the selection of a particular configuration influences the estimation of the robot position, and it allows to compare the effectiveness of different configurations. The results are derived from the analysis of the singular values of a particular matrix obtained by solving the sensor kinematics problem. Moreover, given any mobile robot platform, an end-user procedure is provided to select the best location for N optical mouse sensors on such a platform. The procedure consists of solving a feasible constrained optimization problem.     

Systematic robustness analysis of least squares mobile robot velocity estimation using a regular polygonal optical mouse array

This paper presents the robust velocity estimation of an omnidirectional mobile robot using a regular polygonal array of optical mice that are installed at the bottom of a mobile robot. First, the velocity kinematics from a mobile robot to an array of optical mice is derived, from which the least squares estimation of the mobile robot velocity is obtained as the simple average of the optical mouse velocity readings. Second, it is shown that a redundant number of optical mice contributes to the robustness of the least squares mobile robot velocity estimation against both measurement noises and partial malfunction of optical mice. Third, the sensitivity analysis of the least squares mobile robot velocity estimation to imprecise installation of optical mice is made, from which a practical method of optical mouse position calibration is devised. Finally, some experimental results using commercial optical mice are given to demonstrate the validity and performance of the proposed mobile robot velocity estimation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Outdoor autonomous navigation using SURF features

In this article, we propose a speeded-up robust features (SURF)-based approach for outdoor autonomous navigation. In this approach, we capture environmental images using an omni-directional camera and extract features of these images using SURF. We treat these features as landmarks to estimate a robot’s self-location and direction of motion. SURF features are invariant under scale changes and rotation, and are robust under image noise, changes in light conditions, and changes of viewpoint. Therefore, SURF features are appropriate for the self-location estimation and navigation of a robot. The mobile robot navigation method consists of two modes, the teaching mode and the navigation mode. In the teaching mode, we teach a navigation course. In the navigation mode, the mobile robot navigates along the teaching course autonomously. In our experiment, the outdoor teaching course was about 150 m long, the average speed was 2.9 km/h, and the maximum trajectory error was 3.3 m. The processing time of SURF was several times shorter than that of scale-invariant feature transform (SIFT). Therefore, the navigation speed of the mobile robot was similar to the walking speed of a person.     

Autonomous robot navigation in outdoor cluttered pedestrian walkways

This paper describes an implementation of a mobile robot system for autonomous navigation in outdoor concurred walkways. The task was to navigate through nonmodified pedestrian paths with people and bicycles passing by. The robot has multiple redundant sensors, which include wheel encoders, an inertial measurement unit, a differential global positioning system, and four laser scanner sensors. All the computation was done on a single laptop computer. A previously constructed map containing waypoints and landmarks for position correction is given to the robot. The robot system's perception, road extraction, and motion planning are detailed. The system was used and tested in a 1‐km autonomous robot navigation challenge held in the City of Tsukuba, Japan, named “Tsukuba Challenge 2007.” The proposed approach proved to be robust for outdoor navigation in cluttered and crowded walkways, first on campus paths and then running the challenge course multiple times between trials and the challenge final. The paper reports experimental results and overall performance of the system. Finally the lessons learned are discussed. The main contribution of this work is the report of a system integration approach for autonomous outdoor navigation and its evaluation. © 2009 Wiley Periodicals, Inc.     

A Neural-based Model for Fast Continuous and Global Robot Location

One of the problems in the field of mobile robotics is the estimation of the robot position in an environment. This paper proposes a model for estimating a confidence interval of the robot position in order to compare it with the estimation made by a dead-reckoning system. Both estimations are fused using heuristic rules. The positioning model is very valuable in estimating the current robot position with or without knowledge about the previous positions. Furthermore, it is possible to define the degree of knowledge of the robot previous position, making it possible to adapt the estimation by varying this knowledge degree. This model is based on a one-pass neural network which adapts itself in real time and learns about the relationship between the measurements from sensors and the robot position.     

Omnidirectional vision-based ego-pose estimation for an autonomous in-pipe mobile robot

This paper describes the omnidirectional vision-based ego-pose estimation method of an in-pipe mobile robot. An in-pipe mobile robot has been developed for inspecting the inner surface of various pipeline configurations, such as the straight pipeline, the elbow and the multiple-branch. Because the proposed in-pipe mobile robot has four individual drive wheels, it has the ability of flexible motions in various pipelines. The ego-pose estimation is indispensable for the autonomous navigation of the proposed in-pipe robot. An omnidirectional camera and four laser modules mounted on the mobile robot are used for ego-pose estimation. An omnidirectional camera is also used for investigating the inner surface of the pipeline. The pose of the in-pipe mobile robot is estimated from the relationship equation between the pose of a robot and the pixel coordinates of four intersection points where light rays that emerge from four laser modules intersect the inside of the pipeline. This relationship equation is derived from the geometry analysis of an omnidirectional camera and four laser modules. In experiments, the performance of the proposed method is evaluated by comparing the result of our algorithm with the measurement value of a specifically designed sensor, which is a kind of a gyroscope.     

Exoskeletal Force-Sensing End-Effectors With Embedded Optical Fiber-Bragg-Grating Sensors

Force sensing is an essential requirement for dexterous robot manipulation. We describe composite robot end-effectors that incorporate optical fibers for accurate force sensing and estimation of contact locations. The design is inspired by the sensors in arthropod exoskeletons that allow them to detect contacts and loads on their limbs. In this paper, we present a fabrication process that allows us to create hollow multimaterial structures with embedded fibers and the results of experiments to characterize the sensors and controlling contact forces in a system involving an industrial robot and a two-fingered dexterous hand. We also briefly describe the optical-interrogation method used to measure multiple sensors along a single fiber at kilohertz rates for closed-loop force control.      

基于内部传感器的轮式移动机器人运动速度估计

讨论了在无速度传感器的情况下轮式移动机器人的速度估计问题, 采用了加速度传感器和位置传感器的输出实时估计轮式移动机器人速度, 并用一种按加速度扰动调整权值的方法融合来自不同传感器的数据. 实验验证了方法的有效性.     

基于电信数据的室外定位技术研究

越来越多的移动计算依赖位置信息提供基于位置的服务,移动设备的室外定位技术至关重要。目前广为采用的方式是GPS,但移动设备端的GPS位置信息依赖移动设备如手机的GPS传感器获取,电信运营商虽然为用户提供通话和数据服务,却无法获得用户的精确GPS位置。针对这种情况,提出利用手机端和电信基站之间的连接信号数据(简称电信数据),实现移动设备的定位服务。考虑到电信运营商积累了海量的电信数据,因此通过研究基于电信数据的室外定位技术,使得运营商获取用户位置成为可能。提取电信特征数据、以手机所在GPS位置作为标签数据,研究了五种基于机器学习模型的室外定位算法,实现了从基站信号数据到GPS坐标点的预测,通过大量的实验对比了这些方法的定位精度和运行时间、不同数据收集模式的定位精度、不同特征的定位精度以及探索了后处理对定位精度的提升效果。最终通过实验可知,基于栅格化的随机森林分类模型是效果最好的方法,能够达到15~20m的平均误差和10m的中位误差,比前期回归算法在2G和4G数据分别实现了39.46%和54.28%的精度提升,取得与GPS定位接近的定位精度。     

自主导航小车AGV定位方法的研究

AGV是一种用于FMS中以实现物料自动运输的轮式移动机器人,对其进行定位是实现物料准确运输的基本保证.通过对移动机器人现有定位方法及其定位精度的分析,提出了利用安装于AGV侧两模拟量超声波传感器为其实现定位的定位方法.理论分析证明了该方法是可行的,实验验证了该定位方法是可靠的.实验结果表明,采用所设计的定位方法为AGV进行定位,其定位精度可满足FMS对AGV定位精度的要求.     

A system for self-diagnosis of an autonomous mobile robot using an internal state sensory system: fault detection and coping with the internal condition

Considering that intelligent robotic systems work in a real environment, it is important that they themselves have the ability to determine their own internal conditions. Therefore, we consider it necessary to pay some attention to the diagnosis of such intelligent systems and to construct a system for the self-diagnosis of an autonomous mobile robot. Autonomous mobile systems must have a self-contained diagnostic system and therefore there are restrictions to building such a system on a mobile robot. In this paper, we describe an internal state sensory system and a method for diagnosing conditions in an autonomous mobile robot. The prototype of our internal sensory system consists of voltage sensors, current sensors and encoders. We show experimental results of the diagnosis using an omnidirectional mobile robot and the developed system. Also, we propose a method that is able to cope with the internal condition using internal sensory information. We focus on the functional units in a single robot system and also examine a method in which the faulty condition is categorized into three levels. The measures taken to cope with the faulty condition are set for each level to enable the robot to continue to execute the task. We show experimental results using an omnidirectional mobile robot with a self-diagnosis system and our proposed method.     

移动机器人基于多传感器信息融合的室外场景理解

本文研究了移动机器人多传感器信息融合技术,提出一种融合激光测距与视觉信息的实时室外场景理解方法．基于三维激光测距数据构建了高程图描述场景地形特征,同时利用条件随机场模型从视觉信息中获取地貌特征,并以高程图中的栅格作为载体,应用投影变换和信息统计方法将激光信息与视觉信息进行有效融合．在此基础上,对融合后的环境模型分别在地形和地貌两个层面进行可通过性评估,从而实现自主移动机器人实时室外场景理解．实验结果和数据分析验证了所提方法的有效性和实用性．     

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     

Kinematic Modeling and Analysis of Skid-Steered Mobile Robots With Applications to Low-Cost Inertial-Measurement-Unit-Based Motion Estimation

Skid-steered mobile robots are widely used because of their simple mechanism and high reliability. Understanding the kinematics and dynamics of such a robotic platform is, however, challenging due to the complex wheel/ground interactions and kinematic constraints. In this paper, we develop a kinematic modeling scheme to analyze the skid-steered mobile robot. Based on the analysis of the kinematics of the skid-steered mobile robot, we reveal the underlying geometric and kinematic relationships between the wheel slips and locations of the instantaneous rotation centers. As an application example, we also present how to utilize the modeling and analysis for robot positioning and wheel slip estimation using only low-cost strapdown inertial measurement units. The robot positioning and wheel slip-estimation scheme is based on an extended Kalman filter (EKF) design that incorporates the kinematic constraints for accuracy enhancement. The performance of the EKF-based positioning and wheel slip-estimation scheme are also presented. The estimation methodology is tested and validated experimentally on a robotic test bed.