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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林沛群(Pei-Chun Lin) | |
| dc.contributor.author | Geng-Ping Ren | en |
| dc.contributor.author | 任耕平 | zh_TW |
| dc.date.accessioned | 2021-06-16T10:18:27Z | - |
| dc.date.available | 2016-09-06 | |
| dc.date.copyright | 2013-09-06 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-08-16 | |
| dc.identifier.citation | [1] Robot. Available: http://en.wikipedia.org/wiki/Robot#History
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60448 | - |
| dc.description.abstract | 本論文主要開發機器人在室外三度空間中運動時機器人本體狀態（body state）估測器，並以此估測器為基礎建立機器人的避障導航演算法。本體狀態估測器中使用了三種位置感測器GPS（Global Position System）、編碼器、和加速規，以及三種姿態感測器磁力計、傾斜儀、和陀螺儀，以無損型卡爾曼濾波器（Unscented Kalman Filter）架構，估測出完整的機器人本體狀態。其後，並以雙輪機器人與履帶機器人兩個不同的實驗平台，進行實驗驗證，探討估測器在室外的估測表現及評估估測器受到各感測器量測值變化所產生之影響。室外避障導航演算法是以前述估測器所計算出機器人的位置與姿態為基礎，配合以超音波陣列達到環境障礙偵測與避障，來規劃出機器人適當的導航機制，來調控機器人運行之前進速度與角速度。避障導航演算法並先以電腦模擬分析方式，探討系統中各參數對導航特性之影響。之後如同位置與姿態估測器之方式，以雙輪機器人與履帶機器人兩個不同的實驗平台，進行導航的實驗驗證。 | zh_TW |
| dc.description.abstract | The main purpose of this paper is to design a body state estimator on mobile robots with NOAA(Navigation with Obstacle Avoidance Algorithm) in three-dimensional space of outdoor environments. The body state estimator is based on unscented Kalman filter theory, using three positional sensors such as GPS(Global Position System), encoders and accelerometers as well as three attitudinal sensors including magnetometers, inclinometers and gyroscopes. We verified its outdoor estimation abilities as well as the effects of the signal variation of each individual sensor on the estimator through experiments on the tracked mobile robot and the two-wheeled mobile robot. The NOAA strategy is based on the position and attitude information which computed by the estimator, and it is to apply obstacle avoidance algorithm by using array of the ultrasonic sensors for environments sensing, while using two control parameters - speed and angular velocity controlling constant - respectively. Last, we simulated the coupling effects between parameters and situation during the navigation period on the NOAA strategy with computer and validated it through experiments on the tracked and two wheeled mobile robots. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T10:18:27Z (GMT). No. of bitstreams: 1 ntu-102-R00522831-1.pdf: 3471489 bytes, checksum: 36a4981a638dfb1500894bcbdcff311e (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii 目錄 iv 圖目錄 vii 表目錄 xi 第1章 緒論 1 1.1 前言 1 1.2 研究動機 3 1.3 文獻回顧 4 1.4 貢獻 8 1.5 論文架構 8 第2章 系統架構和實驗機器人介紹 10 2.1 全球導航衛星系統介紹 10 2.1.1 美國GPS系統介紹 11 2.1.2 星基擴增系統介紹 14 2.1.3 座標系統簡介 15 2.1.4 GPS接收器介紹 18 2.2 慣性導航系統 19 2.3 GPS/INS整合系統介紹 21 2.4 磁力計 24 2.5 實驗機器人介紹 25 2.6 系統架構 30 2.7 實驗結果 31 2.7.1 GPS接收器靜態誤差分析 31 2.7.2 磁力計誤差分析 33 第3章 估測器的設計與實驗 37 3.1 卡爾曼濾波器理論 37 3.1.1 卡爾曼濾波器 38 3.1.2 擴展型卡爾曼濾波器 40 3.1.3 無損型卡爾曼濾波器 42 3.2 二維位置量測模型推導 45 3.3 二維位置量測模型實驗 49 3.3.1 比較EKF與UKF估測能力 49 3.3.2 比較UKF不同取樣時間的狀況 50 3.4 三維位置量測模型推導 52 3.5 三維位置量測模型實驗 56 3.5.1 比較二維與三維模型在斜坡上的估測能力 56 3.5.2 驗證長時間長距離下UKF的估測能力 58 3.5.3 驗證崎嶇地估測能力 61 3.6 本章結論 63 第4章 避障導航演算法 64 4.1 導航策略 64 4.2 導航策略模擬 66 4.3 避障演算法 71 4.4 避障導航演算法 72 4.5 避障導航實驗 73 4.6 本章結論 78 第5章 結論與未來展望 79 5.1 結論 79 5.2 未來展望 79 參考文獻 81 | |
| dc.language.iso | zh-TW | |
| dc.subject | 本體狀態估測器 | zh_TW |
| dc.subject | GPS | zh_TW |
| dc.subject | 機器人 | zh_TW |
| dc.subject | 避障 | zh_TW |
| dc.subject | 室外導航 | zh_TW |
| dc.subject | 卡爾曼濾波器 | zh_TW |
| dc.subject | outdoor navigation | en |
| dc.subject | body state estimator | en |
| dc.subject | GPS | en |
| dc.subject | mobile robot | en |
| dc.subject | Kalman filter | en |
| dc.subject | obstacle avoidance | en |
| dc.title | 以多感測器融合建構機器人於戶外環境之智慧型導航 | zh_TW |
| dc.title | Multi-sensor fusion on mobile robots for intelligent navigation in outdoor environments | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃光裕(Kuang-Yuh Huang),李綱(Kang Li),顏炳郎 | |
| dc.subject.keyword | 本體狀態估測器,GPS,機器人,避障,室外導航,卡爾曼濾波器, | zh_TW |
| dc.subject.keyword | body state estimator,GPS,mobile robot,obstacle avoidance,outdoor navigation,Kalman filter, | en |
| dc.relation.page | 84 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-08-17 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
| 顯示於系所單位： | 機械工程學系 | |
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