Motion Planning through Demonstration to Deal with Complex Motions in Assembly Process

Complex and skillful motions in actual assembly process are challenging for the robot to generate with existing motion planning approaches, because some key poses during the human assembly can be too skillful for the robot to realize automatically. In order to deal with this problem, this paper develops a motion planning method using skillful motions from demonstration, which can be applied to complete robotic assembly process including complex and skillful motions. In order to demonstrate conveniently without redundant third-party devices, we attach augmented reality (AR) markers to the manipulated object to track and capture poses of the object during the human assembly process, which are employed as key poses to execute motion planning by the planner. Derivative of every key pose serves as criterion to determine the priority of use of key poses in order to accelerate the motion planning. The effectiveness of the presented method is verified through some numerical examples and actual robot experiments.

[1]  Danica Kragic,et al.  Object recognition and pose estimation using color cooccurrence histograms and geometric modeling , 2005, Image Vis. Comput..

[2]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[3]  Steven M. LaValle,et al.  RRT-connect: An efficient approach to single-query path planning , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[4]  Aude Billard,et al.  On Learning, Representing, and Generalizing a Task in a Humanoid Robot , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Takashi Suehiro,et al.  Teaching by demonstration of assembly motion in VR - non-deterministic search-type motion in the teaching stage , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Huosheng Hu,et al.  Robot Learning from Demonstration in Robotic Assembly: A Survey , 2018, Robotics.

[7]  Kin Hong Wong,et al.  An Improvement on ArUco Marker for Pose Tracking Using Kalman Filter , 2018, 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD).

[8]  Steven M. LaValle,et al.  Rapidly-Exploring Random Trees: Progress and Prospects , 2000 .

[9]  Thierry Siméon,et al.  Manipulation Planning with Probabilistic Roadmaps , 2004, Int. J. Robotics Res..

[10]  Darius Burschka,et al.  A pilot study in vision-based augmented telemanipulation for remote assembly over high-latency networks , 2013, 2013 IEEE International Conference on Robotics and Automation.

[11]  Jean-Claude Latombe,et al.  Experiments in dual-arm manipulation planning , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[12]  Dmitry Berenson,et al.  A robot path planning framework that learns from experience , 2012, 2012 IEEE International Conference on Robotics and Automation.

[13]  Jean-Claude Latombe,et al.  Planning motions with intentions , 1994, SIGGRAPH.

[14]  Maxim Likhachev,et al.  Speeding up heuristic computation in planning with Experience Graphs , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[15]  Dariu Gavrila,et al.  Pedestrian Detection from a Moving Vehicle , 2000, ECCV.

[16]  Francisco José Madrid-Cuevas,et al.  Automatic generation and detection of highly reliable fiducial markers under occlusion , 2014, Pattern Recognit..

[17]  Thomas B. Moeslund,et al.  Pose Estimation Using Structured Light and Harmonic Shape Contexts , 2006, VISIGRAPP.

[18]  Ales Ude,et al.  Programming full-body movements for humanoid robots by observation , 2004, Robotics Auton. Syst..

[19]  Larry S. Davis,et al.  Hierarchical Part-Template Matching for Human Detection and Segmentation , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[20]  Peter Hubinský,et al.  Visual Localization of Mobile Robot Using Artificial Markers , 2014 .

[21]  Philip David,et al.  SoftPOSIT: Simultaneous Pose and Correspondence Determination , 2002, International Journal of Computer Vision.

[22]  Gary R. Bradski,et al.  Fast 3D recognition and pose using the Viewpoint Feature Histogram , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Alexander Zelinsky,et al.  Programing by Demonstration: Coping with Suboptimal Teaching Actions , 2003 .