Automatic concrete tunnel inspection robot system

This paper describes the development of an automatic concrete tunnel inspection system by an autonomous mobile robot. This work was motivated by the accidents in recent years that were caused by falling parts of the inner wall of concrete tunnels. This brought about serious damage to national property and led to the great concern throughout the whole society. Here, we proposed a non-destructive automatic tunnel inspection method. In this method, we aim to inspect the tunnel automatically and completely at high speed by using non-destructive sensors. For the non-destructive sensors, we employ 24 ultrasonic range sensors and six video cameras. These sensors are mounted on the same plane in the shape of a semi-ring. This ultrasonic range sensor video camera semi-ring is called a URS/VC semi-ring. This URS/VC semi-ring is mounted on an autonomous mobile robot to inspect the concrete tunnel. Experiment results show that this system can detect deformed inner walls at divisions of 14 mm when the robot moves at 20 mm/s.     

Real-Time Omnidirectional Image Sensors

Conventional T.V. cameras are limited in their field of view. A real-time omnidirectional camera which can acquire an omnidirectional (360 degrees) field of view at video rate and which could be applied in a variety of fields, such as autonomous navigation, telepresence, virtual reality and remote monitoring, is presented. We have developed three different types of omnidirectional image sensors, and two different types of multiple-image sensing systems which consist of an omnidirectional image sensor and binocular vision. In this paper, we describe the outlines and fundamental optics of our developed sensors and show examples of applications for robot navigation.     

Human-assistance robotic system based on distributed computing technology

Population aging is becoming an increasingly pressing problem for society. Thus, the timely development of supporting systems to improve care cost and the quality of life of the elderly is becoming particularly important. Because of the dispersal of the elderly and disabled, a standard software architecture that can implement network-distributed software sharing and improve the cost of writing and maintaining software is in high demand. This paper proposes using distributing computing technology CORBA to integrate network-distributed software and robotic systems for supporting the aged or disabled. Using CORBA as a communication architecture, we implemented a networkdistributed human-assistance robotic system. We developed a hardware base including a robot arm and an omnidirectional mobile robot, developed key technologies such as localization of a mobile robot, real-time recognition, real-time multimedia, and implemented several CORBA servers including a Web user management server, a task-level robot arm control server, a live image and video feedback server, a mobile robot control server, and an indoor Global Positioning System server. With the user controlling a robot arm with 5 d.o.f. to cooperate with an omnidirectional mobile robot, the developed system can provide basic services to support the aged and disabled.     

A closed-form expression for the uncertainty in odometry position estimate of an autonomous vehicle

Using internal and external sensors to provide position estimates in a two-dimensional space is necessary to solve the localization and navigation problems for a robot or an autonomous vehicle (AV). Usually, a unique source of position information is not enough, so researchers try to fuse data from different sensors using several methods, as, for example, Kalman filtering. Those methods need an estimation of the uncertainty in the position estimates obtained from the sensory system. This uncertainty is expressed by a covariance matrix, which is usually obtained from experimental data, assuming, by the nature of this matrix, general and unconstrained motion. We propose in this paper a closed-form expression for the uncertainty in the odometry position estimate of a mobile vehicle, using a covariance matrix whose form is derived from the cinematic model. We then particularize for a nonholonomic Ackerman driving-type AV. Its cinematic model relates the two measures being obtained for internal sensors: the velocity, translated into the instantaneous displacement; and the instantaneous steering angle. The proposed method is validated experimentally, and compared against Kalman filtering.     

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.     

A nonparametric learning approach to range sensing from omnidirectional vision

We present a novel approach to estimating depth from single omnidirectional camera images by learning the relationship between visual features and range measurements available during a training phase. Our model not only yields the most likely distance to obstacles in all directions, but also the predictive uncertainties for these estimates. This information can be utilized by a mobile robot to build an occupancy grid map of the environment or to avoid obstacles during exploration—tasks that typically require dedicated proximity sensors such as laser range finders or sonars. We show in this paper how an omnidirectional camera can be used as an alternative to such range sensors. As the learning engine, we apply Gaussian processes, a nonparametric approach to function regression, as well as a recently developed extension for dealing with input-dependent noise. In practical experiments carried out in different indoor environments with a mobile robot equipped with an omnidirectional camera system, we demonstrate that our system is able to estimate range with an accuracy comparable to that of dedicated sensors based on sonar or infrared light.     

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.     

Integrated tracking and accident avoidance system for mobile robots

In the intelligent transportation field, various accident avoidance techniques have been applied. One of the most common issues with these is the collision, which remains an unsolved problem. To this end, we developed a Collision Warning and Avoidance System (CWAS), which was implemented in the wheeled mobile robot. Path planning is crucial for a mobile robot to perform a given task correctly. Here, a tracking system for mobile robots that follow an object is presented. Thus, we implemented an integrated tracking system and CWAS in a mobile robot. Both systems can be activated independently. Using the CWAS, the robot is controlled through a remotely controlled device, and collision warning and avoidance functions are performed. Using the tracking system, the robot performs tasks autonomously and maintains a constant distance from the followed object. Information on the surroundings is obtained through range sensors, and the control functions are performed through the microcontroller. The front, left, and right sensors are activated to track the object, and all the sensors are used for the CWAS. The proposed system was tested using the binary logic controller and the Fuzzy Logic Controller (FLC). The efficiency of the robot was improved by increasing the smoothness of motion via the FLC, achieving accuracy in tracking and increasing the safety of the CWAS. Finally, simulations and experimental outcomes have shown the usefulness of the system.     

Dynamic omnidirectional vision for mobile robots

Mobile robotic devices hold great promise for a variety of applications in industry. A key step in the design of a mobile robot is to determine the navigation method for mobility control. The purpose of this paper is to describe a new algorithm for omnidirectional vision navigation. A prototype omnidirectional vision system and the implementation of the navigation techniques using this modern sensor and an advanced automatic image processor is described. The significance of this work is in the development of a new and novel approach—dynamic omnidirectional vision for mobile robots and autonomous guided vehicles.     

The study of path error for an omnidirectional home care mobile robot

The first objective of this research was to develop an omnidirectional home care mobile robot. A PC-based controller controls the mobile robot platform. This service mobile robot is equipped with an “indoor positioning system” and an obstacle avoidance system. The indoor positioning system is used for rapid and precise positioning and guidance of the mobile robot. The obstacle avoidance system can detect static and dynamic obstacles. In order to understand the stability of a three-wheeled omnidirectional mobile robot, we carried out some experiments to measure the rectangular and circular path errors of the proposed mobile robot in this research. From the experimental results, we found that the path error was smaller with the guidance of the localization system. The mobile robot can also return to its starting point. The localization system can successfully maintain the robot’s heading angle along a circular path.     

基于行为的移动机器人避障系统的设计

随着机器人应用范围的不断扩展,机器人所面临的工作环境也越来越复杂,多数是未知的、动态的和非结构化的。通过对基于行为的机器人控制技术的研究,提出了一种实用的自主移动机器人智能避障控制方案,阐述了其具体实现技术;将基于行为的控制技术融合进模糊控制的思想中,使移动机器人的行为通过运用模糊控制和基于优先度的行为决策来实现。试验证明了这一方法的有效性和实时性。     

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.     

一种基于视觉的移动机器人定位系统

具有自主的全局定位能力是自主式稳定机器人传感器系统的一项重要功能,为了实现这个目的,国内外均在不断地研究发展各种定位传感器系统,这里介绍了一种采用光学蝗全方位位置传感器系统,该传感器系统由主动式路标、视觉传感器、图象采集与数据处理系统组成,其视觉传感器和数据处理系统可安装在移动机器人上,然后可通过观测路标物「视角定位的方法,计算出机器人在世界坐标系中的位置和方向,实验证明,该系统可以只的在线定位,     

室内自主式移动机器人定位方法

定位是确定机器人在其工作环境中所处位置的过程.应用各种传感器感知信息实现可靠的定位是自主式移动机器人最基本、也是最重要的一项功能之一.本文对室内自主式移动机器人的定位技术进行了综述,介绍了当前自主式移动机器人定位方法的研究现状.同时,对国内外具有典型性的研究方法进行了较洋细的介绍,并重点提出了几种室内自主式移动机器人通用的定位方法,对其中的地图构造、位姿估计方法进行了详细介绍.最后,论述了自主式移动机器人定位系统与地图构造中所面临的主要问题及其解决方法并指出了该领域今后的研究方向.     

Development of distance recognition using an ocellus camera for an autonomous personal robot

We are attempting to develop an autonomous personal robot that has the ability to perform practical tasks in a human living environment by using information derived from sensors. When a robot operates in a human environment, the issue of safety must be considered in regard to its autonomous movement. Thus, robots absolutely require systems that can recognize the external world and perform correct driving control. We have thus developed a navigation system for an autonomous robot. The system requires only image data captured by an ocellus CCD camera. In this system, we allow the robot to search for obstacles present on the floor. Then, the robot obtains distance recognition necessary for evasion of the object, including data of the obstacle’s width, height, and depth by calculating the angles of images taken by the CCD camera. We applied the system to a robot in an indoor environment and evaluated its performance, and we consider the resulting problems in the discussion of our experimental results. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

A robot self-localization system based on omnidirectional color images

A self-localization system for autonomous mobile robots is presented. This system estimates the robot position in previously learned environments, using data provided solely by an omnidirectional visual perception subsystem composed of a camera and of a special conical reflecting surface. It performs an optical pre-processing of the environment, allowing a compact representation of the collected data. These data are then fed to a learning subsystem that associates the perceived image to an estimate of the actual robot position. Both neural networks and statistical methods have been tested and compared as learning subsystems. The system has been implemented and tested and results are presented.     

Neural Networks for Autonomous Path-Following with an Omnidirectional Image Sensor

This paper presents a path-following system implemented with two different types of neural networks, that enables an autonomous mobile robot to return along a previously learned path in a dynamic environment. The path-following is based on data provided by an omnidirectional conical visual system, derived from the COPIS sensor, but with different optical reflective properties. The system uses optical and software processing and a neural network to learn the path, described as a sequence of selected points. In the navigation phase it drives the robot along this learned path. Interesting results have been achieved using low cost equipment. Test and results are presented.     

An integrated approach to the conceptual design and development of an intelligent autonomous mobile robot

This paper proposes a new approach to the design and development of an intelligent mobile robot and discusses the complex functional structure of such systems, providing solutions to some typical design problems. The proposed design approach provides a clearer view of the design problems from a function-oriented interdisciplinary point of view. The approach proves to be a useful tool in allowing the designer to cross the boundaries of technical disciplines and optimise the interdisciplinary system’s components, thereby improving the overall system performance. The paper also presents a case study where it takes the design problem from the most abstract level up to the final stage, in which the developed autonomous mobile robot’s specifications are verified and validated. The issues addressed in this paper include the design, development, systems integration, and experimental testing of the intelligent mobile robot.     

Sensor Fault Tolerance Method by Using a Bayesian Network for Robot Behavior

This paper presents FTBN, a new framework that performs learning autonomous mobile robot behavior and fault tolerance simultaneously. For learning behavior in the presence of a robot sensor fault this framework uses a Bayesian network. In the proposed framework, sensor data are used to detect a faulty sensor. Fault isolation is accomplished by changing the Bayesian network structure using interpreted evidence from robot sensors. Experiments including both simulation and a real robot are performed for door-crossing behavior using prior knowledge and sensor data at several maps. This paper explains the learning behavior, optimal tracking, exprimental setup and structure of the proposed framework. The robot uses laser and sonar sensors for door-crossing behavior, such that each sensor can be corrupted during the behavior. Experimental results show FTBN leads to robust behavior in the presence of a sensor fault as well as performing better compared to the conventional Bayesian method.     

Distributed Telerobotics System Based on Common Object Request Broker Architecture

This paper proposes using CORBA as communication architecture to integrate network-distributed software and robotic systems in support systems for the aged or disabled. The proposed method keeps system costs low and expands availability. Its high scaling and inter-operating ability allows clients and server objects that are written in different languages, run in different operating systems, and connected to different networks to inter-operate. It also enables the system to be extended and integrated with other technologies and applications distributed over the Internet. Based on CORBA, we developed hardware base including a robot arm and an omnidirectional mobile robot and application servers including a task-level robot arm control server, live feedback image server, mobile robot control server and iGPS server. By remotely controlling mobile robot to cooperate with the robot arm, the caregivers or family member can use the developed system for some basic services to the aged or disabled.