第六章 結論與未來展望
6.2 未來展望
本論文模擬 SLAM 演算法所用的感測器為雷射測距儀,雷射測距儀雖然有著 極高的準確度,但往往只能感測同一平面,或是在長廊地形裡所接收的感測資料 太過相似,而無法準確定位,雷射造價也較為昂貴,未來可以考慮使用視覺感測 器作為替代,將定位與建圖擴展至 3D 空間,更符合現實環境。
本論文 5.1 節中所提到的rdiff 門檻值設定為量測距離最大值的 5%,此參數為 多次統計的結果,並沒有詳細探討如何給定此參數,未來可以考慮根據所使用的 雷射測距儀誤差來設計此參數,以達到更精確的同時定位與建圖結果。
本論文所提出的雲端 ESLAM 架構,並沒有將 ESLAM 演算法中能夠平行處 理的部分全部平行化,只針對部分演算法進行平行化,在未來可以更詳細規畫將 演算法能平行運算的部分完全平行化。在雲端運算上雖然運用了雲端的快速運算,
卻沒有運用雲端大量儲存的功能,此外,使用雲端運算所產生的資料傳輸時間,
雖然取決於網路傳輸速度,但是未來若將演算法中每個步驟的計算結果儲存在雲 端資料庫中,只回傳最後的定位與建圖結果,應該能大幅降低傳輸資料所花的時 間,更提升計算效率。
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參考文獻
[1]. H. Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: part I,”
IEEE Robot Automation Magazine, vol. 13, no. 2, pp. 99-110, 2006.
[2]. J. J. Leonard and H. F. Durrant-Whyte, “Mobile robot localization by tracking geometric beacons,” IEEE Trans. on Robotics and Automation, vol. 7, no. 3, pp.
376-382, 1991.
[3]. R. Chatila and J. P. Laumond, “Position referencing and consistent world modeling for mobile robots,” in Proc. IEEE International Conference on Robotics and Automation, St. Louis, 1985, pp. 138-145.
[4]. H. Durrant-Whyte, D. Rye, and E. Nebot, “Localization of automatic guided vehicles,” in Proc. 7th International Symposium on Robotics Research (ISRR’95), 1996, pp. 613-625.
[5]. J. J. Leonard and H. J. S. Feder, “A computationally efficient method for large-scale concurrent mapping and localization,” in Proc. Ninth International Symposium on Robotics Research (ISRR’99), 2000, pp. 169-176.
[6]. J. Guivant, E. Nebot, and S. Baiker, “Localization and map building using laser range sensors in outdoor applications,” Journal of Robotic Systems, vol. 17, no.
10, pp. 565-583, 2000.
[7]. S. B. Williams, P. Newman, G. Dissanayake, and H. F. Durrant-Whyte,
“Autonomous underwater simultaneous localisation and map building,” in Proc.
IEEE International Conference on Robotics and Automation (ICRA), San Francisco, 2000, pp. 1793-1798.
[8]. R. C. Smith and P. Cheeseman, “On the representation and estimation of spatial uncertainty,” International Journal of Robotics, vol. 5, no. 4, pp. 56-58, 1986.
[9]. S. J. Julier and J. K. Uhlmann, “A counter example to the theory of simultaneous localization and map building,” in Proc. IEEE International Conference on Robotics and Automation, 2001, pp. 4238-4243.
[10]. J. E. Guivant and E. M. Nebot, “Optimization of the simultaneous localization and map-building algorithm for real-time implementation,” IEEE Trans. on Robotics and Automation, vol. 17, no. 3, pp. 242-257, 2001.
[11]. J. Neira and J. D. Tardos, “Data association in stochastic mapping using the joint
compatibility test,” IEEE Trans. on Robotics and Automation, vol. 17, no. 6, pp.
890-897, 2001.
[12]. A. Chatterjee and F. Matsuno, “A neuro-fuzzy assisted extended Kalman filter based approach for Simultaneous Localization and Mapping (SLAM) problems,”
IEEE Trans. on Fuzzy Systems, vol. 15, no. 5, pp. 984-997, 2007.
[13]. A. Chatterjee and F. Matsuno, “A Geese PSO tuned fuzzy supervisor for EKF based solutions of simultaneous localization and mapping (SLAM) problems in mobile robots,” Expert Systems with Applications, vol. 37, no. 8, pp. 5542-5548, 2010.
[14]. A. Chatterjee, “Differential evolution tuned fuzzy supervisor adapted, extended Kalman filtering for SLAM problems in mobile robots,” Robotica, vol. 27, no. 3, pp. 411-423, 2009.
[15]. S. B. Williams, H. Durrant-Whyte, and G. Dissanayake, “Constrained initialization of the simultaneous localization and mapping algorithm,”
International Journal of Robotics Research, vol. 22, no. 7-8, pp. 541-564, 2003.
[16]. G. Dissanayake, S. B. Williams, H. Durrant-Whyte, and T. Bailey, “Map management for efficient simultaneous localization and mapping (SLAM),”
Autonomous Robots, vol. 12, no. 3, pp. 267-286, 2002.
[17]. S. B. Williams, G. Dissanayake, and H. Durrant-Whyte, “An efficient approach to the simultaneous localisation and mapping problem,” in Proc. IEEE International Conference on Robotics and Automation, 2002, pp. 406-411.
[18]. S. B. Williams, G. Dissanayake, and H. Durrant-Whyte, “Towards multi-vehicle simultaneous localisation and mapping,” in Proc. IEEE International Conference on Robotics and Automation, Washington, 2002, pp. 2743-2748.
[19]. K. Murphy, “Bayesian map learning in dynamic environments,” Neural Information Proceedings System, vol. 12, pp. 1015-1021, 2000.
[20]. M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, “FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges,” in Proc. International Joint Conference on Artificial Intelligence, 2003, pp. 1151-1156.
[21]. M. Montemerlo and S. Thrun, “Simultaneous localization and mapping with
72
unknown data association using FastSLAM,” in Proc. IEEE International Conference on Robotics and Automation, 2003, pp. 1985-1991.
[22]. C.-K. Yang, C.-C. Hsu, and Y.-T. Wang, “Computationally efficient algorithm for simultaneous localization and mapping (SLAM),” in Proc. IEEE International Conference on Networking, Sensing and Control (ICNSC), 2013, pp. 328-332.
[23]. 鄧宏志,結合粒子群最佳化法之雙層粒子濾波器於移動機器人的定位與地圖
建置,博士論文,淡江大學電機工程學系,民國 100 年。
[24]. R. Arumugam, V. Enti, B. Liu, X. Wu, K. Baskaran, F. Kong, A. Kumar, D. Meng, and G. Kit, “DAvinCi: A cloud computing framework for service robots,” in Proc.
2010 IEEE International Conference on Robotics and Automation, Anchorage, USA, May 3-7, 2010, pp. 3084-3089.
[25]. Y. C. Ho and R. Lee, “A Bayesian approach to problems in stochastic estimation and control,” IEEE Trans. on Automatic Control, vol. 9, no. 4, pp. 333-339, 1964.
[26]. Markov property - from Wikipedia Website http://en.wikipedia.org/wiki/Markov_property
[27]. P. S. Maybeck, Stochastic Models, Estimation, and Control, Volume 1, Academic Press, Inc., 1979.
[28]. R. G. Brown and P. Y. C. Hwang, Introduction to Random Signals and Applied Kalman Filtering, Second Edition, John Wiley & Sons, Inc., 1992.
[29]. O. L. R. Jacobs, Introduction to Control Theory, 2nd Edition. Oxford University Press., 1993.
[30]. G. Welch and G. Bishop, “An introduction to the Kalman filter,” UNC-Chapel Hill, TR 95-041, July 24, 2006.
[31]. A. Doucet, N. De Freitas, and N.J. Gordon, Sequential Monte Carlo Methods in Practice, Springer, 2001.
[32]. S. J. Julier and J. K. Uhlmann “A new extension of the Kalman filter to nonlinear systems,” in Proc. AeroSense: The 11th Int. Symp. on Aerospace/Defence Sensing, Simulation and Controls, Orlando, USA, vol. 3068, 1997, pp. 182-193.
[33]. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. on Signal Processing, vol. 50, no. 2, pp. 174-188, 2002.
[34]. G. Kitagawa, “Monte Carlo filter and smoother for non-Gaussian nonlinear state space models,” Journal of Computational and Graphical Statistics, vol. 5, no. 1, pp. 1-25, 1996.
[35]. I. Rekleitis, “A particle filter tutorial for mobile robot localization,” Technical Report TR-CIM-04-02, Centre for Intelligent Machines, McGill University, Montreal, Quebec, Canada, 2004.
[36]. F. Dellaert, D. Fox, W. Burgard, and S. Thrun, “Monte Carlo localization for mobile robots,” in Proc. IEEE International Conference on Robotics and Automation, 1999, pp. 1322-1328.
[37]. S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics, the MIT Press, 2005.
[38]. C.-C. Hsu, C.-C. Wong, H.-C. Teng, and C.-Y. Ho, “Localization of mobile robots via an enhanced particle filter incorporating tournament selection and nelder-mead simplex search,” International Journal of Innovative Computing, Information and Control, vol. 7, no. 7A, pp. 3725-3737, July 2011.
[39]. Tom White, Hadoop: The Definitive Guide, O'Reilly Media, Third Edition, 2012.
[40]. J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” OSDI 2004.
[41]. K. Ayush and N. K. Agarwal, “Real time visual SLAM using cloud computing,”
in Proc. IEEE International Conference on Computer, Communication Networking Techonologies (ICCCNT), July 2013, pp. 1-7.
[42]. G. Klein and D. Murray, “Parallel tracking and mapping for small AR workspaces,” in Proc. Sixth IEEE and ACM International Symposium on Mixed and Augmented Reality, 2007, pp. 225-234.
[43]. L. Riazuelo, J. Civera, and J. M. M. Montiel, “C2TAM: A Cloud framework for cooperative tracking and mapping,” Robotics and Autonomous Systems, vol. 62, no. 4, pp. 401-413, April, 2014.
[44]. G. Mohanarajah, V. Usenko, M. Singh, R. D’Andrea, and M. Waibel,
“Cloud-based collaborative 3D mapping in real-time with low-cost robots,” IEEE Trans. on Automation Science and Engineering, vol. 12, no. 2, pp. 423-431, 2015.
[45]. S. Ghemawat, H. Gobioff, and S.-T. Leung, “The Google file system,” SOSP’03, Bolton Landing, New York, USA, Oct. 19-22, 2003.
74
[46]. L. George, HBase: The Definitive Guide, O’Reilly Media, Inc., 2011.
[47]. F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T.
Chandra, A. Fikes, and R. E. Gruber, “Bigtable: A Distributed Storage System for Structured Data,” OSDI 2006.
自傳
1、 家庭成長背景
父母都是公教人員,在我小時候因為不懂事時,管的比較嚴格,隨著年齡、
心智的成長,越來越讓我獨立自主,也因此培養了我的自我管理能力。
2、 求學經歷
(一)大學時期
在大學時期我探索自己有興趣的領域,修了像是控制系統、機器人控制等各 種不同的課程,發現自己興趣廣泛,從大三開始加入了許陳鑑教授的團隊並製作 專題“以粒子濾波器實現機器人定位”,開始研究機器人導航相關的領域,大學 畢業後,我先在移民署服替代役,之後才重新回到台師大就讀電機工程學系研究 所。
(二)研究所時期
我的研究方向為智慧型機器人導航相關領域,“同時定位與建圖(SLAM)”
為我主要的研究主題,針對 SLAM 演算法做改良,並融入近年來較為新穎的雲端 技術,模擬機器人的導航。在研究所期間,我學到了如何做研究、發現並解決問 題等,也出國發表了學術論文“Enhanced SLAM for mobile robot”,並以全英文 口頭報告。老師也讓我在課堂中上台報告自己的研究,藉此訓練我的表達能力。
為了比別人多一份競爭力,在研究所兩年中,我不斷的修習各種領域的課程,
像是可變結構控制、影像處理、數值分析等,且都保持著不錯的成績,同時不斷 精進自己撰寫程式的能力,像是 C、C++、Java 等,並學習不同作業系統像是 CentOS 等,不斷學習英文的我,希望能讓自己符合社會未來的趨勢。
3、 個人經驗
76
在大學中,學業固然重要,但不是唯一。在這個小型社會裡,與師長、同學 間的相處、對事情的看法,也是我們必須要增進的部分,我在課餘時間曾經擔任 國小、國中、高中魔術社團老師以及冬夏令營的魔術指導老師,藉由教學魔術與 推廣魔術,除了讓我的表達能力以及與人互動的能力更好之外,更讓我學會如何 面對各種不同年齡層的小朋友,以及與其他學校老師的互動與互助。
學術成就
1. 論文發表
T. Y. Lin, C. C. Hsu, W. Y. Wang, and Y. T. Wang, “Enhanced Simultaneous Localization and Map Building,” IEEE International Automatic Control Conference (CACS 2014), Kaohsiung, November, 2014, pp. 122-127.
T. Y. Lin, C. C. Hsu, W. Y. Wang, Y. T. Wang, and I. H. Li, “Enhanced
Simultaneous Localization and Mapping (ESLAM) for Mobile Robots,” IEEE International Conference on Soft Computing and Intelligent Systems (SCIS) and International Symposium on Advanced Intelligent Systems (ISIS) (SCIS & ISIS 2014), Kitakyushu, Japan, December, 2014, pp. 888-891.
2. 競賽
2013 International Robot Hands on Competition & Symposium 機器人保齡球賽 佳作獎。
3. 參與研究計畫
100B038702-以 SOPC 為基礎利用單一攝影機傾斜攝影之移動式機器人物體 追蹤與定位系統(2/2)。
102B0340-群組機器人之雲端控制技術應用於工業廠房的環安研究-子計畫 三:基於雲端運算之泉組機器人導航系統設計。
102J1A24-邁向頂尖大學計畫-高速眼動儀系統設計。
103J1A24-邁向頂尖大學計畫-高速眼動儀系統設計。
104J1A24-邁向頂尖大學計畫-高速眼動儀系統設計。
103B0398-群組機器人之雲端控制技術應用於工業廠房的環安研究-子計畫 三:基於雲端運算之泉組機器人導航系統設計(II)。