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研究生:張安宏
研究生(外文):An-Hung Chang
論文名稱:應用感測器融合技術於室內移動機器人同步定位與地圖建構系統開發
論文名稱(外文):Applying Sensor Fusion Technique for Indoor Mobile Robot SLAM System Development
指導教授:郭重顯郭重顯引用關係
指導教授(外文):Chung-Hsien Kuo
口試委員:蕭俊祥蘇順豐劉孟昆
口試委員(外文):Jin-Siang ShawShun-Feng SuMeng-Kun Liu
口試日期:2019-01-24
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:60
中文關鍵詞:室內移動機器人自主導航卡爾曼濾波同步定位與地圖建構
外文關鍵詞:mobile robotnavigationkalman filterSLAM
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本論文針對室內移動機器人開發一應用感測器融合技術於室內移動機器人同步定位與地圖建構系統。一般室內移動機器人如果依靠單一感測器進行室內定位與導航時,容易受到感測器之累積誤差、機器人車輪打滑等不確定因素影響,造成定位資訊可信度較低;尤其當機器人快速移動時,可能造成其定位資訊不穩定。因此本論文透過基本型卡爾曼濾波器融合一組裝設於室內移動機器人之左右輪輪軸上的相對式編碼器與陀螺儀用來提供可信度較高之里程計,再利用無損型卡爾曼濾波器融合先前之里程計與Hector SLAM(Hector Simultaneous Localization and Mapping),在室內移動機器人進行路徑跟隨等動態移動任務與目標點停靠時,提供穩定性高的定位資訊於室內移動機器人。此系統包含一個使用者操作介面,主要用於供給使用者操控室內移動機器人進行地圖建構、地圖儲存與地圖讀取,並透過預先建立好之地圖,結合A*搜尋演算法與貝茲曲線進行路徑規劃,產生一較為平滑之室內機器人行走路徑,依此路徑使室內機器人進行路徑跟隨與目標點停靠任務。最後將透過卡爾曼濾波器融合感測器之里程計準確度實驗、動態定位準確度實驗、重現精度實驗、軌跡追蹤精度實驗、目標點停靠精度實驗等各項實驗來驗證本論文所提之系統在實際環境運作下之效能表現,最後實驗結果證明無損型卡爾曼濾波器之結果在動態路徑跟隨之效能表現較為優勢。
This thesis presents the approaches of applying sensor fusion technique for realizing indoor mobile robot simultaneous localization and mapping (SLAM) system. In general, the precision and robustness of mobile robot localization may be affected by the uncertain factors of environments if a single sensor was used. To overcome this problem, this work presented the Kalman filter (KF) approach to implement the sensor fusion technique in term of recruiting wheel-encoder based odometry and gyro to provide a better odometry precision. The KF-based odometry was further combined with the Hector SLAM in terms of unscented Kalman filter (UKF) to propose a more stable and reliable mobile robot localization performance. In addition, this work implemented a human machine interface (HMI) for the mobile robot operation. Furthermore, this work combined the A* algorithm with Bezier curve generator to plan the mobile robot trajectory automatically in a given map information for autonomous navigation. Finally, a number of experiments were done to validate the performance of original odometry, KF and UKF. The experiments showed that the UKF outperformed the KF; the UKF performed better localization performance at curvature paths.
指導教授推薦書 i
口試委員會審定書 ii
誌謝 iii
摘要 iv
Abstract v
目錄 vi
圖目錄 viii
表目錄 x
符號說明 xi
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3文獻回顧 4
1.3.1室內定位之相關研究 4
1.3.2車輛導航之相關研究 6
1.3.3路徑追蹤之相關研究 8
1.4論文架構 10
第二章 系統架構與方法 11
2.1 系統組織 11
2.2 ROS(Robot Operating System)機器人作業系統 16
2.3 Hector SLAM演算法導入 17
2.4系統運作流程與設計 22
第三章 感測器融合導航定位與地圖建構系統設計 23
3.1 卡爾曼濾波器 23
3.2無損型卡爾曼濾波器 26
3.3 感測器融合導航定位與地圖建構系統 30
第四章 路徑規劃與路徑跟隨 34
4.1路徑規劃 34
4.1.1 A*路徑規劃演算法 34
4.1.2 A*演算法結合貝茲曲線之路徑平滑化 36
4.2 路徑跟隨 39
第五章 實驗結果與分析 43
5.1 卡爾曼濾波器融合感測器之里程計準確度實驗 43
5.2動態定位準確度實驗 46
5.2.1 手推移動之動態定位準確度實驗 46
5.2.2 自動移動之動態定位準確度實驗 48
5.3 重現精度實驗 49
5.4 軌跡精度與目標點停靠實驗 51
5.4.1 軌跡精度實驗 51
5.4.2 目標點停靠精度實驗 54
第六章 結論與未來研究方向 56
6.1 結論 56
6.2 未來研究方向 56
參考文獻 57
[1]K. Krinkin, A. Filatov, A. Y. Filatov, A. Huletski, and D. Kartashov, “Evaluation of Modern Laser Based Indoor SLAM Algorithms,” 22nd Conference of Open Innovations Association (FRUCT), pp. 101–106, 2018.
[2]R. Wang, Y. Wang, W. Wan, and K. Di, “A Point-Line Feature based Visual SLAM Method in Dynamic Indoor Scene,” Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS), pp. 1–6, 2018.
[3]F. Cao, Y. Zhuang, H. Zhang, and W. Wang, “Robust Place Recognition and Loop Closing in Laser-Based SLAM for UGVs in Urban Environments,” IEEE Sensors Journal, pp. 4242–4252, 2018.
[4]A. Huletski, D. Kartashov, and K. Krinkin, “VinySLAM: An indoor SLAM method for low-cost platforms based on the Transferable Belief Model,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6770–6776, 2017.
[5]W. Lin, J. Hu, H. Xu, C. Ye, X. Ye, and Z. Li, “Graph-based SLAM in indoor environment using corner feature from laser sensor,” 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 1211–1216, 2017.
[6]X. Liang, H. Chen, Y. Li, and Y. Liu, “Visual laser-SLAM in large-scale indoor environments,” IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 19–24, 2016.
[7]Y. Lin, S. Wu, and X. Bai, “An efficient approach of map-learning on service robot in complex office environment using laser radar,” 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1–6, 2016.
[8]J.D. Fossel, K. Tuyls, and J. Sturm, “2D-SDF-SLAM: A signed distance function based SLAM frontend for laser scanners,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1949–1955, 2015.
[9]K. Kamarudi, S. M. Mamduh, A.S.A. Yeon, R. Visvanathan, A.Y.M. Shakaff, A. Zakaria, L.M. Kamarudin, and N.A. Rahim, “Improving performance of 2D SLAM methods by complementing Kinect with laser scanner,” IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), pp. 278–283, 2015.
[10]Y. Liu, X. Fan, C. Lv, J. Wu, L. Li, and D. Ding, “An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles,” Mechanical Systems and Signal Processing, vol. 100, pp. 605–616, 2018.
[11]T.H. Kim, and T.H. Park, “EKF-based simultaneous localization and mapping using laser corner-pattern matching,” IEEE International Conference on Information and Automation (ICIA), pp. 491–497, 2016.
[12]A. Skobeleva, V. Ugrinovskii, and I. Petersen, “Extended Kalman Filter for indoor and outdoor localization of a wheeled mobile robot,” Australian Control Conference (AuCC), pp. 212–216, 2016.
[13]A.B.S.H.M. Saman, and A.H. Lotfy, “An implementation of SLAM with extended Kalman filter,” 6th International Conference on Intelligent and Advanced Systems (ICIAS), pp. 1–4, 2016.
[14]X. Chen, Y. Xu, Q. Li, J. Tang, and C. Shen, “Improving ultrasonic-based seamless navigation for indoor mobile robots utilizing EKF and LS-SVM,” Measurement, vol. 92, pp. 243–251, 2016.
[15]M.A. Mahmud, M.S. Aman, H. Jiang, A. Abdelgawad, and K. Yelamarthi, “Kalman filter based indoor mobile robot navigation,” International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 1949–1953, 2016.
[16]J. Duan, Z. Song, C. Wang, and D. Liu, “Inertial Navigation Algorithm Based on Modified Kalman Filter and Wavelet Technique for Intelligent Vehicle,” 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), vol. 2, pp. 9–12, 2016.
[17]H. Chung, C. Hou, and Y. Chen, “Indoor Intelligent Mobile Robot Localization Using Fuzzy Compensation and Kalman Filter to Fuse the Data of Gyroscope and Magnetometer,” IEEE Transactions on Industrial Electronics, pp. 6436– 6447, 2015.
[18]E. Khatib, M. Jaradat, M. Abdel-Hafez, and M. Roigari, “Multiple sensor fusion for mobile robot localization and navigation using the Extended Kalman Filter,” 10th International Symposium on Mechatronics and its Applications (ISMA), pp. 1–5, 2015.
[19]M. Ghandour, H. Liu, N. Stoll, and K. Thurow, “Improving the navigation of indoor mobile robots using Kalman filter,” IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, pp. 1434–1439, 2015.
[20]G. Wu, P. Liu, Q. Chen, and W. Sun, “Path Following Control Problem for Wheeled Mobile Robots by Using Output Regulation Design,” 37th Chinese Control Conference (CCC), pp. 452–456, 2018.
[21]H. Zhou, L. Güvenç, and Z. Liu, “Design and evaluation of path following controller based on MPC for autonomous vehicle,” 36th Chinese Control Conference (CCC), pp. 9934–9939, 2017.
[22]M. Monllor, F. Roberti, M. Jimenez, A. Frizera, and R. Carelli, “Path following control for assistance robots,” XVII Workshop on Information Processing and Control (RPIC), pp. 1–6, 2017.
[23]R. Wang, C. Hu, F. Yan, and M. Chadli, “Composite Nonlinear Feedback Control for Path Following of Four-Wheel Independently Actuated Autonomous Ground Vehicles,” IEEE Transactions on Intelligent Transportation Systems, pp. 2063–2074, 2016.
[24]S. Barai, A. Dey, and B. Sau, “Path following of autonomous mobile robot using passive RFID tags,” International Conference on Microelectronics, Computing and Communications (MicroCom), pp. 1–6, 2016.
[25]Stefan Kohlbrecher, Oskar von Stryk, Johannes Meyer, Uwe Klingauf, “A flexible and scalable SLAM system with full 3D motion estimation,” 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics, pp. 1–6, 2011
[26]W. Bao, C. Zhang, B. Xiao and R. Chen, “Self-localization of Mobile Robot Based on Binocular Camera and Unscented Kalman Filter”, IEEE International Conference on Automation and Logistics, pp1-5, 2007.
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