基於霍爾效應的任意曲面上定位方法｜國立臺灣科技大學博碩士論文系統

簡易檢索 / 詳目顯示

回結果列表

研究生：	鍾賢廣 Xian-Guang Zhong
論文名稱：	基於霍爾效應的任意曲面上定位方法 Positioning on arbitrary surface based on hall effect
指導教授：	賴祐吉 Yu-Chi Lai 姚智原 Chih-Yuan Yao
口試委員:	朱宏國 Hung-Huo Chu 林士勛 Shih-Syun Lin
學位類別：	碩士 Master
系所名稱：	電資學院 - 資訊工程系 Department of Computer Science and Information Engineering
論文出版年：	2019
畢業學年度：	108
語文別：	中文
論文頁數：	69
中文關鍵詞：	物體定位、霍爾效應
外文關鍵詞：	Object positioning, Hall Effect
相關次數：	點閱：173 下載：0
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

上一筆

在虛擬實境(Virtual reality) 環境中，可透過使用真實物體進一步讓使用者得到定位的物理回饋(Physical feedback)。透過物理回饋(Physical feedback) 資訊讓使用者可以更穩定的在物體表面上操作定位裝置，改善使用體驗(User experience)。然而虛擬實境(Virtual reality) 應用中，常使用的相機定位(Camera positioning) 方法存在遮擋問題(Occlusion problem)，令手持裝置定位產生誤差。雖然，觸控面板(Touch panel) 技術，可以利用擺設在平面上的感應電路，進行偵測碰觸位置能不受遮擋影響達成平面定位。但是，該類方法應用在三維表面上時，會遇到觸碰電路無法均勻貼附在三維表面上的困境。為了克服上述限制與改善三維表面上的定位結果，本研究採用電路較方便設置的霍爾效應(Hall effect) 感應器根據定位磁鐵大小與模型表面法向量(Normal vector) 在模型背後放置感應器(Hall sensor) 包覆模型表面。並且，考慮表面方向對感應器(Hall sensor) 讀值的影響，將模型表面方向資訊應用於計算三維表面上的定位。藉此達到不受遮擋影響，並且，具有實際物理回饋(Physical feedback) 的三維表面上定位方法。並且，也提出使用機器學習(Machine learning) 預測磁場進行定位的方法，利用較少感應器預測較多感應器讀值，減少定位系統(Positioning system) 所需的感應器數量。為了驗證本系統的有效性，本論文建立利用定位系統(Positioning system) 在虛擬實境(Virtual reality) 中進行繪畫的應用程式，並且，比較在有無觸碰與遮擋的情況下使用者操作的流暢性(Operational fluency)。也蒐集本系統在路徑、定點和曲率(Curvature) 改變與機器學習(Machine learning) 預測的定位誤差數據驗證本系統的定位精準度。本論文達到不受遮擋影響的表面定位方法並能夠給予表面物理回饋(Physical feedback)，並且硬體設計能夠延伸到更複雜的三維表面。

In a virtual reality environment, the user can be further given physical feedback by using real objects. The physical feedback information allows the user to operate the positioning device more stably on the surface of the object to improve the user experience. However, in the virtual reality application, the commonly used camera positioning method has an occlusion problem, causing errors in the positioning of the handheld device. Although the touch panel technology can utilize the sensing circuit disposed on the plane to detect the touch position so that the plane position can be achieved without being affected by the occlusion,however , when such a method is applied to a three-dimensional surface, it is necessary to overcome the difficulty of placing a touch circuit on the surface. In order to overcome the above limitation and improve the positioning results on the three-dimensional surface, this study uses a Hall effect sensor that is more convenient to set up the circuit. The sensor is placed behind the model to cover the model surface according to the size of the positioning magnet and the model surface normal vector. In addition, considering the influence of the surface orientation on the sensor readings, the model surface orientation information is used to calculate the positioning on the three-dimensional surface. Thereby, the three-dimensional surface positioning method with unobstructed influence and with actual object touch feedback is achieved. Moreover, a method of using machine learning to predict the magnetic field for positioning is also proposed, which uses less sensors to predict more sensor readings and reduces the number of sensors required for system positioning. In order to verify the effectiveness of the system, this paper establishes an application for painting in a virtual environment using a positioning system, and compares the fluency of user operations in the presence or absence of touch and occlusion. The system also collects the positioning error data of path, fixed point and curvature change and machine learning prediction to verify the positioning accuracy of the system. This paper achieves an unobstructed surface positioning method and can give surface touch feedback, and the hardware design can extend to more complex three-dimensional surfaces.

中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
表目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
圖目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
符號說明. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
緒論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1 研究背景與動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 論文貢獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
3 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
相關研究. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1 相機定位(Camera positioning) . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 藍芽信號定位與超寬頻信號定位. . . . . . . . . . . . . . . . . . . . . . . . . 5
3 壓力式、電容式與磁感式定位. . . . . . . . . . . . . . . . . . . . . . . . . . 6
4 霍爾效應(Hall effect) 感應器定位. . . . . . . . . . . . . . . . . . . . . . . . 7
5 機械手臂定位與物理回饋(Physical feedback) . . . . . . . . . . . . . . . . . . 7
系統概要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
定位系統(Positioning system) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1 定位原理. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2 感應器(Hall sensor) 讀值校正與過濾. . . . . . . . . . . . . . . . . . . . . . . 16
3 取得磁鐵方向與角度差. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.1 距離對應函式建立. . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4 到達時間差(Time difference of arrival) 方法. . . . . . . . . . . . . . . . . . . 21
5 機器學習(Machine learning) 模型套用. . . . . . . . . . . . . . . . . . . . . . 23
系統硬體與整合. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
1 繪圖模型設計與列印. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2 訓練資料定位. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.1 訓練資料取得. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2 機器學習(Machine learning) 網路結構. . . . . . . . . . . . . . . . . . 30
2.2.1 資料正規化(Data Normalization) . . . . . . . . . . . . . . . 30
2.2.2 線性整流函數(ReLU) . . . . . . . . . . . . . . . . . . . . . 31
2.2.3 全連階層(Fully connected layer) . . . . . . . . . . . . . . . . 31
2.2.4 損失函數(Loss Function) . . . . . . . . . . . . . . . . . . . . 31
2.2.5 亞當優化法(AdamOptimizer) 與更新比率(Learning Rate) . 32
實驗結果與討論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
1 路徑精準度量測實驗設置. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2 曲率(Curvature) 對精準度影響量測. . . . . . . . . . . . . . . . . . . . . . . 35
3 定點精準度與晃動量測. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4 機器學習(Machine learning) 訓練資料量影響量測. . . . . . . . . . . . . . . 42
5 系統定位延遲. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
6 物理回饋(Physical feedback) 實驗. . . . . . . . . . . . . . . . . . . . . . . . 44
6.1 實驗環境. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
6.2 結果比較. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
7 使用者繪圖品質比較. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
7.1 實驗環境. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
7.2 結果比較. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
結論與未來工作. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
參考資料. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

                                

[1] CHEN Xuan BIAN Jiaxing, ZHU Rong. Ultra wideband localization system based on improved two-way ranging and time difference of arrival positioning algorithm. Journal of Computer Applications, 2017.
[2] Rong-Hao Liang, Kai-Yin Cheng, Chao-Huai Su, Chien-Ting Weng, Bing-Yu Chen, and De-Nian Yang. Gausssense: attachable stylus sensing using magnetic sensor grid. In UIST ’12: Proceedings of the 25th annual ACM symposium on User interface software and technology, pages 319–326, New York, NY, USA, 2012. ACM.
[3] Rahul Arora, Rubaiat Habib Kazi, Fraser Anderson, Tovi Grossman, Karan Singh, and George Fitzmaurice. Experimental evaluation of sketching on surfaces in vr. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI ’17, pages 5643–5654, New York, NY, USA, 2017. ACM.
[4] Google. Tilt brush. 2016.
[5] 郑州清华园房地产开发有限公司. 清华·大溪地奇趣彩蛋手绘活动温情落幕. 2016.
[6] Google. goodfromyou. 2016.
[7] GaussToys. projectgauss.
[8] A. Silva, O. Ramirez, V. Vega, and J. Oliver. Phantom omni haptic device: Kinematic and manipulability. pages 193–198, 2009.
[9] Aaron Lee. Dentalmadness. 2018.
[10] Ilya Rosenberg and Ken Perlin. The unmousepad - an interpolating multi-touch force-sensing input pad. volume 28, 08 2009.
[11] Simon Rogers, John Williamson, Craig D. Stewart, and Roderick Murray-Smith. Anglepose: robust, precise capacitive touch tracking via 3d orientation estimation. In CHI, 2011.
[12] Simon Rogers, John Williamson, Craig D. Stewart, and Roderick Murray-Smith. Fingercloud: uncertainty and autonomy handover incapacitive sensing. In CHI, 2010. 54
[13] Jonathan Hook, Stuart Taylor, Alex Butler, Nicolas Villar, and Shahram Izadi. A reconfigurable ferromagnetic input device. 2009.
[14] Y. Jiang and V. C. M. Leung. An asymmetric double sided two-way ranging for crystal offset. In 2007 International Symposium on Signals, Systems and Electronics, pages 525–528, July 2007.
[15] Deniz Taşkin. Design of bluetooth low energy based indoor positioning system. Balkan Journal of Electrical and Computer Engineering, pages 60–65, 09 2017.
[16] Y. Zhao, M. Carraro, M. Munaro, and E. Menegatti. Robust multiple object tracking in rgbd camera networks. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6625–6632, Sep. 2017.
[17] Chien-Hua Chen Chien-Wen Chen Jian-Wei Peng Min-Chun Hu Chih-Yuan Yao Hung-Kuo Chu Ya-Kuei Chang, Jui-Wei Huang. A lightweight and efficient system for tracking handheld objects in virtual reality. 2018.
[18] P. J. Lu. Dental training system in virtual reality. 2018.
[19] LU Ren Jie. Virtual reality aid dental training system for tooth preparation. 2019.
[20] Andy Wilson, Shahram Izadi, Otmar Hilliges, Armando Garcia-Mendoza, and David Kirk. Bringing physics to the surface. In UIST ’08 Proceedings of the 21st annual ACM symposium on User interface software and technology, pages 67–76. ACM, October 2008.
[21] J. X. Lee, Z. Lin, and C. P. S. Francois. Symmetric double side two way ranging with unequal reply time. In 2007 IEEE 66th Vehicular Technology Conference, pages 1980–1983, Sep. 2007.
[22] Zheng Zuo, Liang Liu, Lei Zhang, and Yong Fang. Indoor positioning based on bluetooth low-energy beacons adopting graph optimization. Sensors, 18:3736, 11 2018.
[23] M. Kolesnikov, M. Zefran, A. D. Steinberg, and P. G. Bashook. Periosim: Haptic virtual reality simulator for sensorimotor skill acquisition in dentistry. In 2009 IEEE International Conference on Robotics and Automation, pages 689–694, May 2009.
[24] Voxel-Man. Voxel-man dental–tooth preparation training simulator. Retrieved Jul 15, 2018, from the World Wide Web: https://www.voxel-man.com/simulators/dental/, 2018. 55
[25] Yoseph Bar-Cohen, Constantinos Mavroidis, Mourad Bouzit, Benjmain Dolgin, Deborah L, George Kopchok, and Rodney White. Virtual reality robotic telesurgery simulations using memica haptic system. Proc SPIE, 04 2001.
[26] Moog Inc. Haptic technology in the moog simodont dental trainer. 2018.
[27] Sergio Garrido-Jurado, Rafael Muñoz-Salinas, Francisco Madrid-Cuevas, and Rafael Medina-Carnicer. Generation of fiducial marker dictionaries using mixed integer linear programming. Pattern Recognition, 51, 10 2015.
[28] Francisco Romero Ramirez, Rafael Muñoz-Salinas, and Rafael Medina-Carnicer. Speeded up detection of squared fiducial markers. Image and Vision Computing, 76, 06 2018.
[29] Applications of Artificial Vision. Aruco: a minimal library for augmented reality applications based on opencv. 2018.
[30] Qicai Shi, Spyros Kyperountas, Neiyer Correal, and Feng Niu. Performance analysis of relative location estimation for multihop wireless sensor networks. Selected Areas in Communications, IEEE Journal on, 23:830 – 838, 05 2005.
[31] Decawave. Mdek1001 development kit.
[32] estimote. Proximity beacons.
[33] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization, 2014. 56

全文公開日期 2024/11/26 (校內網路)
全文公開日期本全文未授權公開 (校外網路)
全文公開日期本全文未授權公開 (國家圖書館：臺灣博碩士論文系統)

簡易檢索 / 詳目顯示

相關論文