1、PDF外文：http:/ 中文3220字济南大学泉城学院 毕业设计外文资料翻译 题 目 荧光管检测室内移动机器人 专 业 机械设计制造及其自动化 班 级 08 机设 Q1 &nb
2、sp; 学 生 姜晓亮 学 号 20083006058 指导教师 &nbs
3、p; 张冰 二一 二 年 四 月 八 日 济南大学泉城学院毕业设计外文资料翻译 - 1 - Autonomous Indoor Mobile Robot Navigation by
4、detecting FluorescentTubes Fabien LAUNAY Akihisa OHYA Shin ichi YUTA Intelligent Robot Laboratory, University of Tsukuba 1-1-1 Tennoudai, Tsukuba, Ibaraki 305-8573 JAPAN launay,ohya,yutaroboken.esys.tsukuba.ac.jp Abstract This paper proposes an indoor navigation system for
5、 an autonomous mobile robot including the teaching of its environment. The self-localization of the vehicle is done by detecting the position and orientation of fluorescent tubes located above it s desired path thanks to a camera pointing to the ceiling. A map of the lights based
6、 on odometry data is built in advance by the robot guided by an operator. Then a graphic user interface is used to define the trajectory the robot must follow with respect to the lights. While the robot is moving, the position and orientation of the lights it detects are compared to the map values,
7、which enables the vehicle to cancel odometry errors. 1 Introduction When a wheel type mobile robot navigates on a two dimensional plane, it can use sensors to know its relative localization by summing elementary displacements provided by incremental encoders mounted on its wheels. The m
8、ain default of this method known as odometry is that its estimation error tends to increase unboundedly1. For long distance navigation, odometry and other dead reckoning solutions may be supported by an absolute localization technique providing position information with a low frequency. Absol
9、ute localization in indoor navigation using landmarks located on the ground or on the walls is sometimes difficult to implement since different objects can obstruct them. Therefore a navigation system based on ceiling landmark recognition can be thought as an alternative to this issue.
10、 The navigation system we developed consists in two steps. In the first step, the vehicle is provided with a map of the ceiling lights. Building such a map by hand quickly becomes a heavy task as its size grows. Instead, the robot is guided manually under each light and builds the map a
11、utomatically. The second step consists in defining a navigation path for the vehicleand enabling its position and orientation correction whenever it detects a light recorded previously in the map. Since the map built by the robot is based on odometry whose estimation error grows unboun
12、dedly, the position and orientation of the lights in the map do not correspond to the reality. However, if the trajectory to be followed by the vehicle during the navigation process is defined appropriately above this distorted map, it will be possible for the robot to move along any desired traject
13、ory in the real world. 济南大学泉城学院毕业设计外文资料翻译 - 2 - A GUI has been developed in order to facilitate this map-based path definition process. We equipped a mobile robot with a camera pointing to the ceiling. During the navigation process, when a light is detected, the robot calculates t
14、he position and the orientation of this landmark in its own reference and thanks to a map of the lights built in advance, it can estimate its absolute position and orientation with respect to its map. We define the pose of an object as its position and orientation with respect to a given refe
15、rential. 2 Related work The idea of using lights as landmarks for indoor navigation is not new. Hashino2 developed a fluorescent light sensor in order to detect the inclination angle between an unmanned vehicle and a fluorescent lamp attached to the ceiling. The objective was to c
16、arry out the main part of the process by hardware logic circuit. Instead of lights, openings in the ceiling for aerations have also been used as landmarks to track.Oota et al.3 based this tracking on edge detection, whereas Fukuda4 developed a more complex system using fuzzy template matching
17、. Hashiba et al.5 used the development images of the ceiling to propose a motion planning method. More recently, Amat et al.6 presented a vision based navigation system using several fluorescent light tubes located in captured images whose absolute pose estimation accuracy is better than a GPS syste
18、m. One advantage of the system proposed here is its low memory and processing speed requirements that make its implementation possible on a robot with limited image-processing hardware. Moreover, our navigation system includes a landmarks map construction process entirely based on the robot s
19、 odometry data. The development of a GUI enables the connection between the lights map produced during the teaching process, and the autonomous robot navigation, which results in a complete navigation system. This is the main difference with the previous works which either assume the knowledge of th
20、e ceiling landmarks exact pose thanks to CAD data of building maps, or require the absolute vehicle pose to be entered manually and periodically during the landmarks map construction so as to cancel odometry errors. Figure 1: Target environment consisting of lights of different shapes in corridors exposed to luminosity variations due to sunning. 3 Lights map building In order to cancel odometry errors whenever a light is detected, the robot needs to know in advance
