|
[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
|