跳到主要內容

臺灣博碩士論文加值系統

(44.192.247.184) 您好!臺灣時間:2023/02/06 10:14
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:林柏佑
研究生(外文):Po-Yu Lin
論文名稱:基於標誌物多視角姿態估測之雙手臂協同物體定位系統用於工件上料自動化
論文名稱(外文):Coordinated Object Locating of Dual-arm Robotic System Using Marker-based Multi-view Pose Estimation for Autonomous Workpiece Loading
指導教授:連豊力
指導教授(外文):Feng-Li Lian
口試委員:李後燦黃正民許志明
口試委員(外文):Hou-Tsan LeeCheng-Ming HuangChih-Ming Hsu
口試日期:2021-10-28
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
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:2021
畢業學年度:110
語文別:中文
論文頁數:210
中文關鍵詞:製造自動化工件上料雙手臂機器人最佳視角規劃點對特徵路徑規劃工件定位控制
外文關鍵詞:manufacturing automationworkpiece loadingdual-arm robotic systemnext-best view planningpoint pair featuremotion planningobject-locating control
DOI:10.6342/NTU202104465
相關次數:
  • 被引用被引用:0
  • 點閱點閱:119
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
在機械加工與製造自動化中,機械手臂扮演著關鍵的角色。最近,越來越多高重複性及危險的任務已經改由機械手臂來完成。然而,現今的機械手臂自動化加工系統仍然不夠靈活與強健,因為它們無法處理周遭的變化且受限於預訂條件。此外,還需要耗費大量的時間與額外的成本在事先定義工件上料的路徑與設計工件料盤等前置作業上。
本論文提出了一個配備深度相機的雙手臂機器人物件定位系統,用於自動化工件上料的程序,以提高機器人加工系統的靈活性與強健性。此系統能自動定位工件,然後透過將夾爪爪片插入夾持位置來上料。首先,根據點對特徵,藉由提出的基於標誌物多視角姿態估測方法來估測受到機器人夾爪遮蔽的被夾持工件的六維姿態,該方法在標誌物的協助下進行最佳視角規劃,從多個視角來獲取更多與工件相關的資訊以進行姿態估測。根據姿態估測的結果,考量方向限制生成一組定位被夾持工件抓取位置的雙臂姿態,並透過基於採樣的規劃演算法在線規劃達到該姿態的雙臂運動。然而,由於系統建模誤差與姿態估測誤差等等的誤差因素,可能存在定位誤差導致上料失敗。因此,本論文開發了一種物體定位控制策略來補償定位誤差。
最後,透過進行模擬和實驗,分析所得到的結果來評估所提出方法的性能並驗證所提出系統的可行性。
Industrial robots play crucial roles on machining and manufacturing automation. Recently, more and more highly repetitive and hazardous jobs have been done by industrial robots. However, current automatic machining systems by robots are still not flexible and robust enough because they are unable to handle surrounding changes and limited to predetermined conditions. In addition, a lot of time and additional costs might be spent on preparatory work, such as predefining trajectories to load workpieces and designing fixtures for workpieces.
In this thesis, a dual-arm robotic object-locating system equipped with a depth camera is proposed for autonomous workpiece loading to improve the flexibility and robustness of the robotic machining systems. It can automatically locate the workpiece, and then load it by inserting the gripper fingers into the grasping position. Firstly, 6D pose of the in-hand workpiece occluded by the robotic gripper is estimated by the proposed marker-based multi-view pose estimation method based on point pair features (PPFs), which acquires more visual information related to the workpiece for pose estimation from multiple views planned by the next-best view planning with the help of marker. According to the estimated pose, a dual-arm pose is generated for locating grasping position of the in-hand workpiece considering the orientational constraint, and the dual-arm motion to reach that pose is planned online by a sampling-based planning algorithm. However, due to some error factors, such as system modeling error and pose estimation error, there may be a locating error which leads to the failed loading. Therefore, in this thesis, an object-locating control strategy is developed for compensating the locating error.
Finally, simulations as well as experiments are conducted to evaluate the performance of the proposed methods and to verify the feasibility of the proposed system. Analysis related to the experimental results is provided.
摘要 iii
ABSTRACT v
CONTENTS vii
LIST OF FIGURES ix
LIST OF TABLES xvi
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Problem Formulation 10
1.2.1 In-hand Object Pose Estimation 11
1.2.2 Dual-arm Motion Planning 12
1.2.3 Object-locating Control Strategy Design 13
1.3 Contributions 14
1.4 Organization of the Thesis 15
Chapter 2 Background and Literature Survey 17
2.1 Dual-/Multi-arm Robot for Manufacturing 17
2.2 View Planning 23
2.3 In-hand Object Pose Estimation 28
Chapter 3 Related Algorithms 32
3.1 Pinhole Camera Model 32
3.2 Hand-eye Calibration 35
3.3 Base Frame Calibration 40
3.4 Octomap 44
Chapter 4 System Overview 47
4.1 Coordinate Systems 47
4.2 System Architecture 49
Chapter 5 Marker-based Multi-view Object Pose Estimation 53
5.1 Workflow 54
5.2 Marker-based Next-best View Planning Strategy 56
5.2.1 Generation of Candidate Views 56
5.2.2 Simulation of Visual Perception 58
5.2.3 Selection of Next-best View 59
5.3 Data Processing 60
5.3.1 Point Cloud Segmentation 60
5.3.2 Surface Normal Estimation 61
5.4 6D Object Pose Estimation 63
5.4.1 Point Pair Feature Extraction 63
5.4.2 Hash Map Construction 64
5.4.3 Object Pose Hypotheses Generation 65
5.4.4 Pose Clustering 67
5.4.5 Iterative Closest Point Refinement 68
Chapter 6 Dual-arm Object Locating 70
6.1 Dual-arm Motion for Locating Object 70
6.2 Object-locating Control Strategy 76
Chapter 7 Experimental Results and Analysis 79
7.1 Experimental Setup 79
7.1.1 Overview of the Procedures of the Simulations and Experiments 79
7.1.2 Hardware Platform 84
7.1.3 Software Platform 89
7.2 Calibration and Evaluation 90
7.2.1 Hand-eye Calibration 90
7.2.2 Base Frame Calibration 99
7.3 Pose Estimation for In-hand Workpiece 104
7.3.1 Simulation 106
7.3.2 Experiment 124
7.4 Robotic Workpiece-loading Application 140
7.4.1 Without Object-locating Control 144
7.4.2 With Object-locating Control 162
7.5 Summary 188
7.5.1 Discussion of Calibration 188
7.5.2 Pose Estimation for In-hand Workpiece 191
7.5.3 Robotic Workpiece-loading Application 194
Chapter 8 Conclusions and Future Works 197
8.1 Conclusions 197
8.2 Future Works 199
References 201
[1: Yang et al. 2020]Hsuan-Yu Yang, Chih-Hsuan Shih, Yuan-Chieh Lo, and Feng-Li Lian, “Zero-tuning Grinding Process Methodology of Cyber-Physical Robot System,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, NV, USA (Virtual), Oct. 25-29, 2020, pp. 4270-4275.
[2: Chen et al. 2020]Hao Chen, Juncheng Li, Weiwei Wan, Zhifeng Huang, and Kensuke Harada, “Integrating Combined Task and Motion Planning with Compliant Control,” International Journal of Intelligent Robotics and Applications, Vol. 4, No. 2, Jun. 2020, pp. 149-163.
[3: Digani et al. 2015]Valerio Digani, Lorenzo Sabattini, Cristian Secchi, and Cesare Fantuzzi, “Ensemble Coordination Approach in Multi-AGV Systems Applied to Industrial Warehouses,” IEEE Transactions on Automation Science and Engineering, Vol. 12, No. 3, Jul. 2015, pp. 922-934.
[4: Smith et al. 2012]Christian Smith, Yiannis Karayiannidis, Lazaros Nalpantidis, Xavi Gratal, Peng Qi, Dimos V. Dimarogonas, and Danica Kragic, “Dual arm manipulation—A survey,” Robotics and Autonomous Systems, Vol. 60, No. 10, Oct. 2012, pp. 1340-1353.
[5: Zeng et al. 2017]Andy Zeng, Kuan-Ting Yu, Shuran Song, Daniel Suo, Ed Walker Jr., Alberto Rodriguez, and Jianxiong Xiao, “Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge,” in Proceedings of IEEE International Conference on Robotics and Automation, Singapore, May 29 - Jun. 3, 2017, pp. 1386-1393.
[6: Schwarz et al. 2018]Max Schwarz, Anton Milan, Arul Selvam Periyasamy, and Sven Behnke, “RGB-D object detection and semantic segmentation for autonomous manipulation in clutter,” The International Journal of Robotics Research, Vol. 37, No. 4-5, Apr. 2018, pp. 437-451.
[7: Liu et al. 2012]Ming-Yu Liu, Oncel Tuzel, Ashok Veeraraghavan, Yuichi Taguchi, Tim K Marks, and Rama Chellappa, “Fast object localization and pose estimation in heavy clutter for robotic bin picking,” The International Journal of Robotics Research, Vol. 31, No. 8, Jul. 2012, pp. 951-973.
[8: Perez et al. 2011]Alejandro Perez, Sertac Karaman, Alexander Shkolnik, Emilio Frazzoli, Seth Teller, and Matthew R. Walter, “Asymptotically-optimal Path Planning for Manipulation using Incremental Sampling-based Algorithms,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA, Sep. 25-30, 2011, pp. 4307-4313.
[9: Ji & Wang 2019]Wei Ji and Lihui Wang, “Industrial robotic machining: a review,” The International Journal of Advanced Manufacturing Technology, Vol. 103, No. 1-4, Apr. 2019, pp. 1239-1255.
[10: Chen et al. 2020]Hao Chen, Juncheng Li, Weiwei Wan, Zhifeng Huang, and Kensuke Harada, “Integrating Combined Task and Motion Planning with Compliant Control,” International Journal of Intelligent Robotics and Applications, Vol. 4, No. 2, Jun. 2020, pp. 149-163.
[11: Huang et al. 2017]Yanjiang Huang, Xianmin Zhang, Xunman Chen, and Jun Ota, “Vision-guided peg-in-hole assembly by Baxter robot,” Advances in Mechanical Engineering, Vol. 9, No. 12, 2017.
[12: Moriyama et al. 2019]Ryota Moriyama, Weiwei Wan, and Kensuke Harada, “Dual-arm Assembly Planning Considering Gravitational Constraints,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Macau, China, Nov. 4-8, 2019, pp. 5566-5572.
[13: Polverini et al. 2019]Matteo Parigi Polverini, Andrea Maria Zanchettin, and Paolo Rocco, “A constraint-based programming approach for robotic assembly skills implementation,” Robotics and Computer-Integrated Manufacturing, Vol. 59, Oct. 2019, pp. 69-81.
[14: Stavridis & Doulgeri 2018]Sotiris Stavridis and Zoe Doulgeri, “Bimanual Assembly of Two Parts with Relative Motion Generation and Task Related Optimization,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Madrid, Spain, Oct. 1-5, 2018, pp. 7131-7136.
[15: Tarbouriech et al. 2018]Sonny Tarbouriech, Benjamin Navarro, Philippe Fraisse, André Crosnier, Andrea Cherubini, and Damien Sallé, “Dual-arm relative tasks performance using sparse kinematic control,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Madrid, Spain, Oct. 1-5, 2018, pp. 6003-6009.
[16: Domae et al. 2020]Yukiyasu Domae, Akio Noda, Tatsuya Nagatani, and Weiwei Wan, “Robotic General Parts Feeder: Bin-picking, Regrasping, and Kitting,” in Proceedings of IEEE International Conference on Robotics and Automation, Paris, France, 31 May – 31 Aug., 2020, pp. 5004-5010.
[17: Huang et al. 2015]Yanjiang Huang, Ryosuke Chiba, Tamio Arai, Tsuyoshi Ueyama, and Jun Ota, “Robust multi-robot coordination in pick-and-place tasks based on part-dispatching rules,” Robotics and Autonomous Systems, Vol. 64, Feb. 2015, pp. 70-83.
[18: Saut et al. 2010]Jean-Philippe Saut, Mokhtar Gharbi, Juan Cortés, Daniel Sidobre, and Thierry Siméon, “Planning Pick-and-Place tasks with two-hand regrasping,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, Oct. 18-22, 2010, pp. 4528-4533.
[19: Shome & Bekris 2019]Rahul Shome and Kostas E. Bekris, “Anytime Multi-arm Task and Motion Planning for Pick-and-Place of Individual Objects via Handoffs,” in Proceedings of International Symposium on Multi-Robot and Multi-Agent Systems, New Brunswick, NJ, USA, Aug. 22-23, 2019, pp. 37-43.
[20: Schwarz et al. 2019]Max Schwarz, Christian Lenz, Germán Martín García, Seongyong Koo, Arul Selvam Periyasamy, Michael Schreiber, and Sven Behnke, “Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packing,” in Proceedings of IEEE International Conference on Robotics and Automation, Brisbane, Australia, May 21-25, 2018, pp. 3347-3354.
[21: Harada et al. 2012]Kensuke Harada, Torea Foissotte, Tokuo Tsuji, Kazuyuki Nagata, Natsuki Yamanobe, Akira Nakamura, and Yoshihiro Kawai, “Pick and Place Planning for Dual-Arm Manipulators,” in Proceedings of IEEE International Conference on Robotics and Automation, Saint Paul, Minnesota, USA, May 14-18, 2012, pp. 2281-2286.
[22: Gan et al. 2019]Yahui Gan, Jinjun Duan, Ming Chen, and Xianzhong Dai, “Multi-Robot Trajectory Planning and Position/Force Coordination Control in Complex Welding Tasks,” Applied Sciences, Vol. 9, No.5, Mar. 2019.
[23: Zhou et al. 2016]B. Zhou, L. Xu, Z. Meng, and X. Dai, “Kinematic Cooperated Welding Trajectory Planning for Master-slave Multi-robot Systems,” in Proceedings of Chinese Control Conference, Chengdu, China, Jul. 27-29, 2016, pp. 6369-6374.
[24: Zhang et al. 2012]T. Zhang and F. Ouyang, “Offline motion planning and simulation of two-robot welding coordination,” Frontiers of Mechanical Engineering, Vol. 7, No. 1, 2012, pp. 81-92.
[25: Pellegrinelli et al. 2017]Stefania Pellegrinelli, Nicola Pedrocchi, Lorenzo Molinari Tosatti, Anath Fischer, and Tullio Tolio, “Multi-robot spot-welding cells for car-body assembly: Design and motion planning,” Robotics and Computer-Integrated Manufacturing, Vol. 44, Apr. 2017, pp. 97-116.
[26: Kabir et al. 2019]Ariyan M. Kabir, Alec Kanyuck, Rishi K. Malhan, Aniruddha V. Shembekar, Shantanu Thakar, Brual C. Shah, and Satyandra K. Gupta, “Generation of Synchronized Configuration Space Trajectories of Multi-Robot Systems,” in Proceedings of IEEE International Conference on Robotics and Automation, Montreal, Canada, May 20-24, 2019, pp. 8683-8690.
[27: Owen et al. 2008]W.S. Owen, E.A. Croft, B. Benhabib, “A multi-arm robotic system for optimal sculpting,” Robotics and Computer-Integrated Manufacturing, Vol. 24, No. 1, Feb. 2008, pp. 92-104.
[28: Ruan et al. 2017]Chengming Ruan, Xing Gu, Youhao Li, Gong Zhang, Weijun Wang, and Zhicheng Hou, “Base Frame Calibration for Multi-robot Cooperative Grinding Station by Binocular Vision,” in Proceedings of International Conference on Robotics and Automation Engineering, Shanghai, China, Dec. 29-31, 2017, pp. 115-120.
[29: Tereshchuk et al. 2019]Veniamin Tereshchuk, John Stewart, Nikolay Bykov, Samuel Pedigo, Santosh Devasia, and Ashis G. Banerjee, “An Efficient Scheduling Algorithm for Multi-Robot Task Allocation in Assembling Aircraft Structures,” IEEE Robotics and Automation Letters, Vol. 4, No. 4, Oct. 2019, pp. 3844-3851.
[30: Vergnano et al. 2012]Alberto Vergnano, Carl Thorstensson, Bengt Lennartson, Petter Falkman, Marcello Pellicciari, Francesco Leali, and Stephan Biller, “Modeling and Optimization of Energy Consumption in Cooperative Multi-Robot Systems,” IEEE Transactions on Automation Science and Engineering, Vol. 9, No. 2, Apr. 2012, pp. 423-428.
[31: Zeng et al. 2020]Rui Zeng, Yuhui Wen, Wang Zhao, and Yong-Jin Liu, “View planning in robot active vision: A survey of systems, algorithms, and applications,” Computational Visual Media, Vol. 6, No. 3, Sep. 2020, pp. 225-245.
[32: Kriegel et al. 2012]Simon Kriegel, Christian Rink, Tim Bodenmuller, Alexander Narr, Michael Suppa, and Gerd Hirzinger, “Next-Best-Scan Planning for Autonomous 3D Modeling,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Algarve, Portugal, Oct. 7-12, 2012, pp. 2850-2856.
[33: Krainin et al. 2011]Michael Krainin, Brian Curless, and Dieter Fox, “Autonomous Generation of Complete 3D Object Models Using Next Best View Manipulation Planning,” in Proceedings of IEEE International Conference on Robotics and Automation, Shanghai, China, May 9-13, 2011, pp. 5031-5037.
[34: Bircher et al. 2016]Andreas Bircher, Mina Kamel, Kostas Alexis, Helen Oleynikova, and Roland Siegwart, “Receding Horizon “Next–Best–View” Planner for 3D Exploration,” in Proceedings of IEEE International Conference on Robotics and Automation, Stockholm, Sweden, May 16-21, 2016, pp. 1462-1468.
[35: Monica & Aleotti 2018]Riccardo Monica and Jacopo Aleotti, “Contour-based next-best view planning from point cloud segmentation of unknown objects,” Autonomous Robots, Vol. 42, No. 2, Feb. 2018, pp. 443–458.
[36: Kriegel et al. 2013]Simon Kriegel, Manuel Brucker, Zoltan-Csaba Marton, Tim Bodenmuller, and Michael Suppa, “Combining Object Modeling and Recognition for Active Scene Exploration,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan, Nov. 3-7, 2013, pp. 2384-2391.
[37: Wu et al. 2015]Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao, “3D ShapeNets: A Deep Representation for Volumetric Shapes,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, Jun. 7-12, 2015, pp. 1912-1920.
[38: Eidenberger & Scharinger 2010]Robert Eidenberger and Josef Scharinger, “Active Perception and Scene Modeling by Planning with Probabilistic 6D Object Poses,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, Oct. 18-22, 2010, pp. 1036-1042.
[39: Atanasov et al. 2014]Nikolay Atanasov, Bharath Sankaran, Jerome Le Ny, George J. Pappas, and Kostas Daniilidis, “Nonmyopic View Planning for Active Object Classification and Pose Estimation,” IEEE Transactions on Robotics, Vol. 30, No. 5, Oct. 2014, pp. 1078-1090.
[40: Wu et al. 2015]Kanzhi Wu, Ravindra Ranasinghe, and Gamini Dissanayake, “Active Recognition and Pose Estimation of Household Objects in Clutter,” in Proceedings of IEEE International Conference on Robotics and Automation, Seattle, Washington, May 26-30, 2015, pp. 4230-4237.
[41: Sock et al. 2017]Juil Sock, S. Hamidreza Kasaei, Luis Seabra Lopes, and Tae-Kyun Kim, “Multi-View 6D Object Pose Estimation and Camera Motion Planning Using RGBD Images,” in Proceedings of IEEE International Conference on Computer Vision, Venice, Italy, Oct. 22-29, 2017, pp. 2228-2235.
[42: Chalon et al. 2013]Maxime Chalon, Jens Reinecke, and Martin Pfanne, “Online in-hand object localization,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan, Nov. 3-7, 2013, pp. 2977-3004.
[43: Bimbo et al. 2016]Joao Bimbo, Shan Luo, Kaspar Althoefer, and Hongbin Liu, “In-Hand Object Pose Estimation Using Covariance-Based Tactile To Geometry Matching,” IEEE Robotics and Automation Letters, Vol. 1, No. 1, Jan. 2016, pp. 570-577.
[44: Liang et al. 2020]Jacky Liang, Ankur Handa, Karl Van Wyk, Viktor Makoviychuk, Oliver Kroemer, and Dieter Fox, “In-Hand Object Pose Tracking via Contact Feedback and GPU-Accelerated Robotic Simulation,” in Proceedings of IEEE International Conference on Robotics and Automation, Paris, France, 31 May – 31 Aug., 2020, pp. 6203-6209.
[45: Wen et al. 2020]Bowen Wen, Chaitanya Mitash, Sruthi Soorian, Andrew Kimmel, Avishai Sintov, and Kostas E. Bekris, “Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands,” in Proceedings of IEEE International Conference on Robotics and Automation, Paris, France, 31 May – 31 Aug., 2020, pp. 6210-6217.
[46: Goudie & Galata 2020]Duncan Goudie and Aphrodite Galata, “3D Hand-Object Pose Estimation from Depth with Convolutional Neural Networks,” in Proceedings of 12th IEEE International Conference on Automatic Face & Gesture Recognition, Washington, DC, USA, 30 May – 3 Jun., 2017, pp. 406-413.
[47: Doosti et al. 2020]Bardia Doosti, Shujon Naha, Majid Mirbagheri, and David J. Crandall, “HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation,” in Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, Jun. 13-19, 2020, pp. 6608-6617.
[48: Pfanne et al. 2018]Martin Pfanne, Maxime Chalon, Freek Stulp, and Alin Albu-Schäffer, “Fusing Joint Measurements and Visual Features for In-Hand Object Pose Estimation,” IEEE Robotics and Automation Letters, Vol. 3, No. 4, Oct. 2018, pp. 3497-3504.
[49: Anzai & Takahashi 2020]Tomoki Anzai and Kuniyuki Takahashi, “Deep Gated Multi-modal Learning: In-hand Object Pose Changes Estimation using Tactile and Image Data,” arXiv:1909.12494v3, Aug. 2, 2020.
[50: Szeliski 2011]Richard Szeliski, “Computer Vision: Algorithms and Applications,” 1st ed., Editors: David Gries and F. B. Schneider, London: Springer, 2011.
[51: Li et al. 2010]Aiguo Li, Lin Wang, and Defeng Wu, “Simultaneous robot-world and hand-eye calibration using dual-quaternions and Kronecker product,” International Journal of the Physical Sciences, Vol. 5, No. 10, Sept. 2010, pp. 1530-1536.
[52: Gan & Dai 2011]Gan Yahui, and Dai Xianzhong, “Base frame calibration for coordinated industrial robots,” Robotics and Autonomous Systems, Vol. 59, No. 7-8, Aug. 2011, pp. 563-570.
[53: Hornung et al. 2013]Armin Hornung, Kai M. Wurm, Maren Bennewitz, Cyrill Stachniss, and Wolfram Burgard, "OctoMap: A Probabilistic, Flexible, and Compact 3D Map Representation for Robotic Systems," Autonomous Robots, Vol. 34, No. 3, Apr. 2013, pp. 189-206.
[54: Quigley et al. 2009]Morgan Quigley, Brian Gerkey, Ken Conley, Josh Faust, Tully Foote, Jeremy Leibs, Eric Berger, Rob Wheeler, and Andrew Ng, “ROS: an open-source Robot Operating System,” ICRA Workshop on Open Source Software, 2009.
[55: Drost et al. 2010]Bertram Drost, Markus Ulrich, Nassir Navab, and Slobodan Ilic, “Model Globally, Match Locally: Efficient and Robust 3D Object Recognition,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, Jun. 13-18, 2010, pp. 998-1005.
[56: Mellado et al. 2014]Nicolas Mellado, Dror Aiger, and Niloy J. Mitra, “Super 4PCS Fast Global Pointcloud Registration via Smart Indexing,” Computer Graphics Forum, Vol. 33, No. 5, Aug. 2014, pp. 205-215.
[57: Low 2004]Kok-Lim Low, “Linear Least-Squares Optimization for Point-to-Plane ICP Surface Registration,” Department of Computer Science, University of North Carolina at Chapel Hill, Tech. Rep. TR 04-004, Feb. 2004.
[58: Kuffner & LaValle 2000]J.J. Kuffner and S.M. LaValle, “RRT-Connect: An Efficient Approach to Single-Query Path Planning,” in Proceedings of IEEE International Conference on Robotics and Automation, San Francisco, CA, USA, Apr. 24-28, 2000, pp. 995-1001.
[59: LaValle 1998]S. M. LaValle, “Rapidly-exploring random trees: A new tool for path planning,” Department of Computer Science, Iowa State University, Tech. Rep. TR 98-11, Oct. 1998.
[60: Coleman et al. 2014]David Coleman, Ioan A. Șucan, Sachin Chitta, and Nikolaus Correll, “Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study,” Journal of Software Engineering for Robotics, Vol. 5, No. 1, May 2014, pp. 3-16.
[61: Koenig & Howard 2004]Nathan Koenig and Andrew Howard, “Design and Use Paradigms for Gazebo, An Open-Source Multi-Robot Simulator,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan, Sep. 28-Oct. 2, 2004, pp. 2149-2154.
[62: Shah 2013]Mili Shah, “Solving the Robot-World/Hand-Eye Calibration Problem Using the Kronecker Product,” Journal of Mechanisms and Robotics, Vol. 5, No. 3, Aug. 2013.
[63: Motai & Kosaka 2008]Yuichi Motai and Akio Kosaka, “Hand-Eye Calibration Applied to Viewpoint Selection for Robotic Vision,” IEEE Transactions on Industrial Electronics, Vol. 55, No. 10, Oct. 2008, pp. 3731-3741.
[64: EBC News 2020]EBC News, Eastern Broadcasting Co., Ltd. (Aug. 9, 2020). 傳統攤位轉型電商 燒番麥啟「凍」商機《海峽拚經濟》. [Online]. Available: https://www.youtube.com/watch?v=MQajLxj4bQ4
[65: USTV 2020]Unique Satellite TV, Unique Broadcasting Inc. (Sep. 11, 2020). 疫情推升自動化需求急增 2024全球工廠自動化規模估達2695億美元. [Online]. Available: https://www.youtube.com/watch?v=FjaxzvWGq3c&fbclid=IwAR3NldzS-EExlWe1wkkuxbadJd0mSAnmK8ZeEXjPx0S6KM7J2QMNdHbanxc
[66: EBC Financial News 2020]EBC Financial News, Eastern Broadcasting Co., Ltd. (Jul. 29, 2020). 直擊通路商最強後盾!砸十億打造自動倉儲. [Online]. Available: https://www.youtube.com/watch?v=UmVw-cfmTMw&t=79s
[67: Jiangmen Anmei Industrial Co., Ltd 2016]Jiangmen Anmei Industrial Co., Ltd. (Aug. 12, 2016). Faucet production process. [Online]. Available: http://www.banyanfaucet.com/article/faucet-production-process.html
[68: Da Shiang Automation Co., Ltd]Da Shiang Automation Co., Ltd. Automatic Solution for Investment Casting Process. [Online]. Available: http://www.dsa-auto.com.tw/en/p3_precision-2.php
[69: RAIS Ltd. 2018]RAIS Ltd. (Jun. 4, 2018). Robot cell with pallet changer based on Fanuc robot and two RAIS’s CNC Lathe machines. [Online]. Available: https://www.youtube.com/watch?v=av81-70O0m8&ab_channel=RAISLtd
[70: Wurm & Hornung]Kai M. Wurm and Armin Hornung. OctoMap: An Efficient Probabilistic 3D Mapping Framework Based on Octrees. GitHub repository. [Online]. Available: https://github.com/OctoMap/octomap
[71: OpenCV]OpenCV (Open Source Computer Vision). Detection of ArUco Markers. [Online]. Available: https://docs.opencv.org/master/d5/dae/tutorial_aruco_detection.html
[72: PCL]PCL (Point Cloud Library). Color-based region growing segmentation. [Online]. Available: https://pcl.readthedocs.io/projects/tutorials/en/latest/region_growing_rgb_segmentation.html
[73: Intel RealSense]Intel RealSense. Technical specifications of Intel RealSense Depth Camera D415. [Online]. Available: https://www.intelrealsense.com/depth-camera-d415/
[74: OpenCV]OpenCV (Open Source Computer Vision). Detection of ChArUco Corners. [Online]. Available: https://docs.opencv.org/3.4/df/d4a/tutorial_charuco_detection.html
[75: ROS.org]ROS.org. gazebo_ros_pkgs, interface for using ROS with the Gazebo simulator. [Online]. Available: http://wiki.ros.org/gazebo_ros_pkgs
[76: Intel RealSense]Intel RealSense. Intel RealSense ROS Wrapper for D400 series, SR300 Camera and T265 Tracking Module. GitHub repository. [Online]. Available: https://github.com/IntelRealSense/realsense-ros
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top