外文翻譯--- 螢光燈管檢測室內(nèi)移動(dòng)機(jī)器人

1、濟(jì)南大學(xué)泉城學(xué)院 濟(jì)南大學(xué)泉城學(xué)院畢業(yè)設(shè)計(jì)外文資料翻譯 畢業(yè)設(shè)計(jì)外文資料翻譯題 目 熒光管檢測室內(nèi)移動(dòng)機(jī)器人 專 業(yè) 機(jī)械設(shè)計(jì)制造及其自動(dòng)化 班 級 08 機(jī)設(shè) Q1 學(xué) 生 姜曉亮 學(xué) 號 20083006058 指導(dǎo)

2、教師 指導(dǎo)教師 張冰 二〇一 二年 四月 八 日濟(jì)南大學(xué)泉城學(xué)院畢業(yè)設(shè)計(jì)外文資料翻譯- 2 -world. A GUI has been developed in order to facilitate this map-based path definition process.We equipped a mobile robot with a camera pointing

3、to the ceiling. During the navigation process, when a light is detected, the robot calculates the position and the orientation of this landmark in its own reference and thanks to a map of the lights built in advance, it

4、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 referential.2 Related work The idea of using lights as la

5、ndmarks for indoor navigation is not new. Hashino[2] 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

6、 was to carry 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 detec

7、tion, whereas Fukuda[4] developed a more complex system using fuzzy template matching. Hashiba et al.[5] used the development images of the ceiling to propose a motion planning method. More recently, Amat et al.[6] prese

8、nted a vision based navigation system using several fluorescent light tubes located in captured images whose absolute pose estimation accuracy is better than a GPS system.One advantage of the system proposed here is its

9、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

10、 based on the robot’s 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 navigatio

11、n system. This is the main difference with the previous works which either assume the knowledge of the ceiling landmarks’ exact pose thanks to CAD data of building maps, or require the absolute vehicle pose to be entered

12、 manually and periodically during the landmarks map constructionso as to cancel odometry errors.Figure 1: Target environment consisting of lights of different shapes in corridors exposed to luminosity variations due to s