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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃漢邦(Han-Pang Huang) | |
dc.contributor.author | Shan-Qian Ji | en |
dc.contributor.author | 季善前 | zh_TW |
dc.date.accessioned | 2021-06-08T03:57:36Z | - |
dc.date.copyright | 2018-08-20 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-08-14 | |
dc.identifier.citation | [1] R. Ala, D. H. Kim, S. Y. Shin, C. Kim, and S. K. Park, “A 3D-Grasp Synthesis Algorithm to Grasp Unknown Objects based on Graspable Boundary and Convex Segments,” Information Sciences, Vol. 295, pp. 91–106, 2015.
[2] J. B. Alayrac, J. Sivic, I. Laptev, and S. Lacoste-Julien, “Joint Discovery of Object States and Manipulation Actions,” Proceeding of the IEEE International Conference on Computer Vision, pp. 2146–2155, 2017. [3] J. A. Alcazar and L. G. Barajas, “Estimating Object Grasp Sliding via Pressure Array Sensing,” Proceeding of IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA, pp. 1740–1746, 2012. [4] N. S. Altman, “An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression,” The American Statistician, Vol. 46, No. 3, pp. 175–185, 1992. [5] T. P. Banerjee and S. Das, “Multi-Sensor Data Fusion using Support Vector Machine for Motor Fault Detection,” Information Sciences, Vol. 217, pp. 96–107, 2012. [6] H. . Barber, C.B., Dobkin, D.P., and Huhdanpaa, “Qhull Library,” 2016. Qhull. 2016. <http://www.qhull.org/>. [7] F. Baumgart, “Stiffness-an Unknown World of Mechanical Science?,” Injury-International Journal for the Care of the Injured, Vol. 31, No. 2, pp. 14–23, 2000. [8] J. Baumgartl and D. Henrich, “Fast Vision-based Grasp and Delivery Planning for Unknown Objects,” Proceeding of 7th German Conference on Robotics (ROBOTIK 2012), Munich, Germany, Germany, pp. 1–5, 2012. [9] J. J. Benedetto, Wavelets: Mathematics and Applications, Vol. 13, CRC press, 1993. [10] J. L. Bentley, “Multidimensional Binary Search Trees used for Associative Searching,” Communications of the ACM, Vol. 18, No. 9, pp. 509–517, 1975. [11] A. Bicchi and V. Kumar, “Robotic Grasping and Contact: a Review,” Proceeding of IEEE Int. Conf. on Robotics and Automation (ICRA), San Francisco, CA, USA, Vol. 1, pp. 348–353, 2000. [12] A. Boggess and F. J. Narcowich, A First Course in Wavelets with Fourier Analysis, 1st Edition, John Wiley & Sons, 2015. [13] G. M. Bone, A. Lambert, and M. Edwards, “Automated Modeling and Robotic Grasping of Unknown Three-Dimensional Objects,” Proceeding of IEEE International Conference on Robotics and Automation, Pasadena, CA, USA, pp. 292–298, 2008. [14] C.-C. Chang and C.-J. Lin, “LIBSVM: A Library for Support Vector Machines,” ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 2, No. 3, p. 27, 2011. [15] H.-Y. Chao, “Intelligent Grasping Based on Database,” Master Thesis, Department of Mechanical Engineering, National Taiwan University, 2017. [16] C. Choi, S. H. Yoon, C.-N. Chen, and K. Ramani, “Robust Hand Pose Estimation during the Interaction with an Unknown Object,” Proceeding of 2017 IEEE International Conference on Computer Vision, Venice, Italy, No. i, pp. 3142–3151, 2017. [17] A. Collet and M. Martinez, “MOPED: Object Recognition and Pose Estimation for Manipulation,” The International Journal of Robotics Research, Vol. 30, No. 10, pp. 1284–1306, 2011. [18] D. D. Damian, T. H. Newton, R. Pfeifer, and A. M. Okamura, “Artificial Tactile Sensing of Position and Slip Speed by Exploiting Geometrical Features,” IEEE/ASME Transactions on Mechatronics, Vol. 20, No. 1, pp. 263–274, 2015. [19] D. V. Dao, S. Sugiyama, and S. Hirai, “Development and Analysis of a Sliding Tactile Soft Fingertip Embedded with a Microforce/Moment Sensor,” IEEE Transactions on Robotics, Vol. 27, No. 3, pp. 411–424, 2011. [20] H. Deng, G. Zhong, X. Li, and W. Nie, “Slippage and Deformation Preventive Control of Bionic Prosthetic Hands,” IEEE/ASME Transactions on Mechatronics, Vol. 22, No. 2, pp. 888–897, 2017. [21] M. R. Dogar and S. S. Srinivasa, “Push-grasping with Dexterous Hands: Mechanics and a Method,” Proceeding of IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, Taipei, Taiwan, pp. 2123–2130, 2010. [22] D. Dornfeld and C. Handy, “Slip Detection using Acoustic Emission Signal Analysis,” Proceeding of IEEE International Conference on Robotics and Automation., Raleigh, NC, USA, Vol. 4, pp. 1868–1875, 1987. [23] C. Dune, E. Marchand, C. Collowet, and C. Leroux, “Active Rough Shape Estimation of Unknown Objects,” Proceeding of 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France, pp. 3622–3627, 2008. [24] J. Esteban, A. Starr, R. Willetts, P. Hannah, and P. Bryanston-Cross, “A Review of Data Fusion Models and Architectures: Towards Engineering Guidelines,” Neural Computing and Applications, Vol. 14, No. 4, pp. 273–281, 2005. [25] D. R. Faria and J. Dias, “3D Hand Trajectory Segmentation by Curvatures and Hand Orientation for Classification Through a Probabilistic Approach,” Proceeding of 2009 IEEE International Conference on Robotics and Automation (ICRA 2009), St. Louis, MO, USA, pp. 1284–1289, 2009. [26] C. Ferrari and J. Canny, “Planning Optimal Grasps,” Proceeding of 1992 IEEE International Conference on Robotics and Automation, Nice, France, France, pp. 2290–2295, 1992. [27] H. Gu, S. Fan, H. Zong, M. Jin, and H. Liu, “Haptic Perception of Unknown Object by Robot Hand: Exploration Strategy and Recognition Approach,” International Journal of Humanoid Robotics, Vol. 13, No. 3, pp. 1–29, 2016. [28] Y. Guo, M. Bennamoun, F. Sohel, M. Lu, and J. Wan, “3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, No. 11, pp. 2270–2287, 2014. [29] M. Gupta, J. Müller, and G. S. Sukhatme, “Using Manipulation Primitives for Object Sorting in Cluttered Environments,” IEEE Transactions on Automation Science and Engineering, Vol. 12, No. 2, pp. 608–614, 2015. [30] K. Hausman et al., “Force Estimation and Slip Detection for Grip Control using a Biomimetic Tactile Sensor,” Proceeding of 2015 IEEE-RAS 15th International Conference on Humanoid Robots, Seoul, South Korea, No. October, pp. 297–303, 2015. [31] Y. Hirano, K. I. Kitahama, and S. Yoshizawa, “Image-based Object Recognition and Dexterous Hand/arm Motion Planning using RRTs for Grasping in Cluttered Scene,” Proceeding of 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Alta., Canada, pp. 3981–3986, 2005. [32] A. M. D. and R. D. Howe, “Joint Coupling Design of Underactuated Grippers,” Proceeding of 30th Annual Mechanisms and Robotics Conference, Philadelphia, Pennsylvania, USA, pp. 903–911, 2006. [33] K. Huebner, S. Ruthotto, and D. Kragic, “Minimum Volume Bounding Box Decomposition for Shape Approximation in Robot Grasping,” Proceeding of 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA, pp. 1628–1633, 2008. [34] B. Jähne, Digital Image Processing, 6th Edition, Springer, 2005. [35] H. Y. Jang, H. Moradi, P. Le Minh, S. Lee, and J. Han, “Visibility-based Spatial Reasoning for Object Manipulation in Cluttered Environments,” CAD Computer Aided Design, Vol. 40, No. 4, pp. 422–438, 2008. [36] Y. Jiang, S. Moseson, and A. Saxena, “Efficient Grasping from RGBD Images: Learning using a New Rectangle Representation,” Proceeding of IEEE International Conference on Robotics and Automation, Shanghai, China, pp. 3304–3311, 2011. [37] R. S. Johansson and G. Westling, “Roles of Glabrous Skin Receptors and Sensorimotor Memory in Automatic Control of Precision Grip when Lifting Rougher or more Slippery Objects,” Experimental Brain Research, Vol. 56, No. 3, pp. 550–564, 1984. [38] P. Kiechle, “Evaluation of Tactile Sensors,” Master Thesis, Institute of Computer Science, University of Innsbruck, 2015. [39] K. Klasing, D. Althoff, D. Wollherr, and M. Buss, “Comparison of Surface Normal Estimation Methods for Range Sensing Applications,” Proceeding of 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan, pp. 3206–3211, 2009. [40] L. Kozma, “k Nearest Neighbors algorithm (kNN),” Helsinki University of Technology, 2008. Special Course in Computer and Information Science. 2008. <http://www.lkozma.net/knn2.pdf>. [41] J. J. Kuffner, S. Kagami, K. Nishiwaki, M. Inaba, and H. Inoue, “Dynamically-stable Motion Planning for Humanoid Robots,” Autonomous Robots, Vol. 12, No. 1, pp. 105–118, 2002. [42] Q. Lei, J. Meijer, and M. Wisse, “A Survey of Unknown Object Grasping and our Fast Grasping Algorithm-C Shape Grasping,” Proceeding of 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), Nagoya, Japan, pp. 150–157, 2017. [43] Q. Lei and M. Wisse, “Unknown Object Grasping using Force Balance Exploration on a Partial Point Cloud,” Proceeding of IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, Busan, South Korea, Vol. 2015–Augus, pp. 7–14, 2015. [44] Y. LI, T. TSUGAMA, M. KAMIJO, and H. YOSHIDA, “Study on Cardiovascular and Respiratory Responses Relevant to Tactile Softness Evaluation,” International Journal of Affective Engineering, Vol. 13, No. 4, pp. 269–277, 2014. [45] M. Likhachev, “Search-based Planning with Motion Primitives,” Proceeding of IEEE Int. Conf. Robot. Autom. (ICRA), pp. 2902–2908, 2010. [46] Y. Lin, S. Wei, and L. Fu, “Grasping Unknown Objects using Depth Gradient Feature with Eye-in-hand RGB-D Sensor,” Proceeding of 2014 IEEE International Conference on Automation Science and Engineering (CASE), Taipei, Taiwan, pp. 1258–1263, 2014. [47] V. Lippiello, F. Ruggiero, B. Siciliano, and L. Villani, “Visual Grasp Planning for Unknown Objects using a Multifingered Robotic Hand,” IEEE/ASME Transactions on Mechatronics, Vol. 18, No. 3, pp. 1050–1059, 2013. [48] Y.-R. Liu, “Automated Grasp Planning and Path Planning for a Robot Hand-Arm System,” Master Thesis, Department of Mechanical Engineering, National Taiwan University, 2016. [49] Microsoft, “Microsoft Kinect,” 2016. 2016. <https://developer.microsoft.com/>. [50] Microsoft, “Xbox,” 2016. 2016. <http://www.xbox.com/zh-TW/kinect>. [51] A. Montaño and R. Suárez, “Manipulation of Unknown Objects to Improve the Grasp Quality using Tactile Information,” Sensors, Vol. 18, No. 5, 2018. [52] R. M. Murray, A Mathematical Introduction to Robotic Manipulation, 1st Edition, Boca Raton: CRC press, 2017. [53] M. Popović, G. Kootstra, J. A. Jørgensen, D. Kragic, and N. Krüger, “Grasping Unknown Objects using an Early Cognitive Vision System for General Scene Understanding,” Proceeding of IEEE International Conference on Intelligent Robots and Systems, San Francisco, CA, USA, pp. 987–994, 2011. [54] Pybullet.org, “Bullet Physics,” 2016. Pybullet. 2016. <http://bulletphysics.org/wordpress/>. [55] J. Reinecke, A. Dietrich, F. Schmidt, and M. Chalon, “Experimental Comparison of Slip Detection Strategies by Tactile Sensing with the BioTac® on the DLR Hand Arm System,” Proceeding of 2014 IEEE International Conference on Robotics and Automation, Hong Kong, China, pp. 2742–2748, 2014. [56] T. A. Runkler, M. Sturm, and H. Hellendoorn, “Model based Sensor Fusion with Fuzzy Clustering,” Proceeding of IEEE World Congress on Computational Intelligence, Anchorage, AK, USA, USA, Vol. 2, pp. 1377–1382, 1998. [57] R. B. Rusu, “Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments,” KI - Künstliche Intelligenz, Vol. 24, No. 4, pp. 345–348, 2010. [58] X. Song, H. Liu, K. Althoefer, T. Nanayakkara, and L. D. Seneviratne, “Efficient Break-away Friction Ratio and Slip Prediction based on Haptic Surface Exploration,” IEEE Transactions on Robotics, Vol. 30, No. 1, pp. 203–219, 2014. [59] M. Stachowsky, T. Hummel, M. Moussa, and H. A. Abdullah, “A Slip Detection and Correction Strategy for Precision Robot Grasping,” IEEE/ASME Transactions on Mechatronics, Vol. 21, No. 5, pp. 2214–2226, 2016. [60] Y. Sun, L. Bo, and D. Fox, “Learning to Identify New Objects,” Proceeding of 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, pp. 3165–3172, 2014. [61] S. Suranthiran and S. Jayasuriya, “Nonlinear Averaging of Multi-Sensor data,” Proceeding of International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Chicago, Illinois, USA, pp. 1733–1738, 2003. [62] T. Suzuki and T. Oka, “Grasping of Unknown Objects on a Planar Surface using a Single Depth Image,” Proceeding of IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Banff, AB, Canada, Vol. 2016–Septe, pp. 572–577, 2016. [63] Tekscan, “Pressure Mapping Sensor 4256e,” 2017. Tekscan. 2017. <https://www.tekscan.com/products-solutions/pressure-mapping-sensors/4256e>. [64] S. Teshigawara, K. Tadakuma, A. Ming, M. Ishikawa, and M. Shimojo, “High Sensitivity Initial Slip Sensor for Dexterous Grasp,” Proceeding of 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, USA, pp. 4867–4872, 2010. [65] M. R. Tremblay and M. R. Cutkosky, “Estimating Friction using Incipient Slip Sensing during a Manipulation Task,” Proceeding of IEEE International Conference on Robotics and Automation, Atlanta, GA, USA, USA, pp. 429–434, 1993. [66] N. Tsujiuchi et al., “Slip Detection with Distributed-type Tactile Sensor,” Proceeding of IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 1, pp. 331–336, 2004. [67] Tuxfamily, “Eigen Library,” 2017. <http://eigen.tuxfamily.org>. [68] V. Vapnik, The Nature of Statistical Learning Theory, 2nd Edition, New York, NY, USA: Springer science & business media, 2013. [69] Wacoh-tech, 2017, “Piezoresistive 3-axis Force Sensor.” 2017. <http://www.wacoh-tech.com/en/products/mudynpick/maf-3.html>. [70] H. William, Y. Ibrahim, and B. Richardson, “A Tactile Sensor for Incipient Slip Detection,” International journal of Optomechatronics, Vol. 1, No. 1, pp. 46–62, 2007. [71] D. Wren and R. B. Fisher, “Dextrous Hand Grasping Strategies using Preshapes and Digit Trajectories,” Proceeding of IEEE International Conference on Intelligent Systems for the 21st Century, Vancouver, BC, Canada, Canada, Vol. 1, pp. 910–915, 1995. [72] K. Yamazaki, M. Tomono, and T. Tsubouchi, “Picking up an unknown object through autonomous modeling and grasp planning by a mobile manipulator,” Springer Tracts in Advanced Robotics, Vol. 42, pp. 563–571, 2008. [73] K. Yang and S. Sukkarieh, “3D Smooth Path Planning for a UAV in Cluttered Natural Environments,” Proceeding of 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France, pp. 794–800, 2008. [74] H. Yussof, M. Ohka, A. R. Omar, and M. A. Ayub, “Determination of Object Stiffness Control Parameters in Robot Manipulation using a Prototype Optical Three-axis Tactile Sensor,” Proceeding of Sensors, Lecce, Italy, pp. 992–995, 2008. [75] Y. Zhang, Y. Mukaibo, and T. Maeno, “A Multi-purpose Tactile Sensor Inspired by Human Finger for Texture and Tissue Stiffness Detection,” Proceeding of 2006 IEEE International Conference on Robotics and Biomimetics, Kunming, China, pp. 159–164, 2006. [76] Y. Zheng, “An Efficient Algorithm for a Grasp Quality Measure,” IEEE Transactions on Robotics, Vol. 29, No. 2, pp. 579–585, 2013. [77] “Point Cloud Library,” 2016. Pointclouds. 2016. <http://pointclouds.org/>. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/22007 | - |
dc.description.abstract | 通常機器人需要在人類環境中工作,並處理各種不同類型的物體,這會使機器人面臨兩個挑戰:人類環境通常是雜亂無章的,以及機器人需要在不知道物體重量,靜摩擦係數和剛性的情況下抓取和移動物體。因此,本文結合視覺和多指機械手動作,實現在雜亂的場景中對物體的抓取。並且根據包圍盒生成機械手無碰撞抓取姿態和路徑,並進一步檢查抓取姿態的抓取品質。最後,通過融合所有可用的感測器資料,實現智慧抓取系統,該系統能夠可靠處理各種未知重量,摩擦和剛性的物體。 | zh_TW |
dc.description.abstract | Robots usually need to work in human environments and handle many different types of objects. There are two problems that make this challenging for robots: Human environments are typically cluttered and the multi-finger robotic hand needs to grasp and lift objects without knowing their weight, coefficient of static friction, and stiffness. Thus, this thesis combines vision and robot hand action to achieve reliable and accurate object grasping in a cluttered scene. An efficient algorithm for collision-free grasping pose generation according to a bounding box is proposed and the grasp pose will be further checked for its grasp quality. Finally, by fusing all available sensor data appropriately, an intelligent grasp system is achieved that is reliable and enough to handle various objects with unknown weight, friction, and stiffness. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T03:57:36Z (GMT). No. of bitstreams: 1 ntu-107-R05522839-1.pdf: 5976296 bytes, checksum: ba93005e37947559b9e51eb1ae011970 (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 誌謝 i
摘要 iii Abstract v List of Tables viii List of Figures x Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Contributions 2 1.3 Organization 3 Chapter 2 Object Reconstruction and Segmentation 5 2.1 Introduction 5 2.2 Point Cloud Processing 5 2.3 Bounding Box 10 2.3.1 Oriented Bounding Box 10 2.3.2 Bounding Box Decomposition 11 2.4 Object Segmentation in a Cluttered Scene 14 2.4.1 The Framework of Unknown Object Segmentation 14 2.4.2 Object States Inference based on Action Feedback 16 Chapter 3 Grasp Planning 19 3.1 Introduction 19 3.2 Grasp Strategy 19 3.3 Path Planning 21 3.3.1 Multi-Goal RRT-Connect Algorithm 21 3.3.2 Path Pruning Algorithm 24 3.4 Grasp Analysis 25 3.4.1 Background 26 3.4.2 Grasp Quality Measure 27 Chapter 4 Grip Force Selection and Control 31 4.1 Introduction 31 4.2 Stiffness Measurement 33 4.2.1 Stiffness Measurement Method 34 4.2.2 Sensors Fusion 35 4.2.3 Feature Selection 39 4.2.4 Stiffness Recognition based on K-NN Algorithm 41 4.3 Slip Detection 42 4.3.1 Slip Detection Method 42 4.3.2 Sensors Signal Detection based on SVM 47 4.4 Grasping Control based on Slip Detection and Stiffness Measurement 50 4.5 Summary 52 Chapter 5 Simulations and Experiments 53 5.1 Integration of the Hardware System 53 5.1.1 Six-DOF Robot Arm 53 5.1.2 NTU Robotic Hand VI 54 5.1.3 Sensors 57 5.2 Software System 60 5.3 Experiment 1: Slip Detection 62 5.4 Experiment 2: Stiffness Measurement 68 5.5 Experiment 3: Grasping in a Cluttered Scene 71 Chapter 6 Conclusions and Future Works 87 6.1 Conclusions 87 6.2 Future Works 88 References 89 | |
dc.language.iso | en | |
dc.title | 基於多感測資訊於未知物體之智慧手抓取 | zh_TW |
dc.title | Robot Intelligent Grasping for Unknown Objects
based on Multi-Sensor Information | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 李祖聖(Tzuu-Hseng S. Li),郭重顯(Chung-Hsien Kuo),程啟正(Chi-Cheng Cheng) | |
dc.subject.keyword | 機器手臂手掌系統,抓取規劃,感測器融合,滑動檢測,剛性測量, | zh_TW |
dc.subject.keyword | Robot Hand-Arm System,Grasping Planning,Sensor Fusion,Slip Detection,Stiffness Measurement, | en |
dc.relation.page | 98 | |
dc.identifier.doi | 10.6342/NTU201803004 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2018-08-14 | |
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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