An Approach for Peg-in-Hole Assembling using Intuitive Search Algorithm based on Human Behavior and Carried by Sensors Guided Industrial Robot

Abstract Automated peg-in-hole assembling approach using Force/Torque sensor and vision system is presented in this research. The complicity of the assembling task due to parts’ geometries or position uncertainty in work area are the main factors for the lack of fully automated assembling approaches nowadays in industry and specially in the automotive sector. Based on human operator handling for the assembling of peg-in-hole, an algorithm consists of six phases is developed. In one of the phases a model is created to determine the location of the hole's center during the insertion by using Force/Torque sensor data. The contact forces and torques in each step of the proposed assembling algorithms that was carried by a six degree-of-freedom (DOF) industrial robot is presented.

[1]  Jeffrey C. Trinkle,et al.  Identifying contact formations in the presence of uncertainty , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[2]  Fazel Naghdy,et al.  Fuzzy control of automatic peg-in-hole insertion , 1995, Proceedings of Third Australian and New Zealand Conference on Intelligent Information Systems. ANZIIS-95.

[3]  Masatoshi Ishikawa,et al.  Fast peg-and-hole alignment using visual compliance , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Peter Plapper,et al.  Contact-state monitoring of force-guided robotic assembly tasks using expectation maximization-based Gaussian mixtures models , 2014 .

[5]  Ales Ude,et al.  Analysis of human peg-in-hole executions in a robotic embodiment using uncertain grasps , 2013, 9th International Workshop on Robot Motion and Control.

[6]  S. Inagaki,et al.  Modeling and analysis of peg-in-hole task based on mode segmentation , 2008, 2008 SICE Annual Conference.

[7]  Wyatt S. Newman,et al.  Interpretation of force and moment signals for compliant peg-in-hole assembly , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[8]  Walter Schumacher,et al.  Enhancements of force-torque map based assembly applied to parallel robots , 2010, 2010 IEEE International Conference on Industrial Technology.

[9]  Dong-Jin Lim,et al.  Active peg-in-hole of chamferless parts using force/moment sensor , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).

[10]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[11]  W. Schneider Automating Small-Lot Electronic Production , 1958 .

[12]  Ralf Koeppe,et al.  Data fusion for robotic assembly tasks based on human skills , 2004, IEEE Transactions on Robotics.

[13]  Brenan J. McCarragher,et al.  Combining force and position measurements for the monitoring of robotic assembly , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.

[14]  Henry Y. K. Lau,et al.  A hidden Markov model-based assembly contact recognition system , 2003 .

[15]  Kazuaki Iwata,et al.  Recognition of contact state based on geometric model , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[16]  Gerald Sommer,et al.  Servoing Mechanisms for Peg-In-Hole Assembly Operations , 2001, RobVis.

[17]  Moonhong Baeg,et al.  Intuitive peg-in-hole assembly strategy with a compliant manipulator , 2013, IEEE ISR 2013.

[18]  Marjorie Skubic,et al.  Identifying contact formations from force signals: a comparison of fuzzy and neural network classifiers , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).