A Robotic Peg-in-Hole Assembly Strategy Based on Variable Compliance Center

Many kinds of peg-in-hole assembly strategies for an industrial robot have been reported in recent years. Most of these strategies are realized by utilizing visual and force sensors to assist robots. However, complex control algorithms that are based on visual and force sensors will reduce the assembly efficiency of a robot. This issue is thoughtless in traditional assembly strategies but is critical to further improve the efficiency of assembly automation. In this work, a new assembly strategy that is based on a displacement sensor and a variable compliant center is proposed to improve robot performance in assembly tasks. First, an elastic displacement device for this assembly strategy is designed, and its performance is analyzed. The displacement signal generated by the displacement sensor is used to detect the contact state of the peg and hole and to guide the robot to adjust the posture. Second, an assembly strategy, including the advantages of passive compliance and active compliance, and a simple assembly control system are designed to improve the assembly efficiency. Last, the effectiveness of the proposed assembly method is experimentally verified using a robot with 6 degrees of freedom and a chamferless peg and hole with a small clearance (0.1 mm). The experimental results show that the assembly strategy can successfully complete the precision peg-in-hole assembly and assist the robot in accurate assembly in industrial applications.

[1]  Fuliang Zhao,et al.  VRCC : a variable remote center compliance device , 1998 .

[2]  Yuehong Yin,et al.  Dynamic analysis for peg-in-hole assembly with contact deformation , 2006 .

[3]  Ying Liu,et al.  Target Tracking robotic Manipulation Theories Applied to force/Position Control in Peg-in-Hole assembly Tasks , 2008, Int. J. Robotics Autom..

[4]  Jörg Krüger,et al.  Dual arm robot for flexible and cooperative assembly , 2011 .

[5]  Hong Qiao,et al.  The Concept of “Attractive Region in Environment” and its Application in High-Precision Tasks With Low-Precision Systems , 2015, IEEE/ASME Transactions on Mechatronics.

[6]  Tae-Yong Choi,et al.  Automatic assembly method with the passive compliant device , 2017, 2017 11th Asian Control Conference (ASCC).

[7]  Francesco Braghin,et al.  Optimal Impedance Force-Tracking Control Design With Impact Formulation for Interaction Tasks , 2016, IEEE Robotics and Automation Letters.

[8]  Lorenzo Sciavicco,et al.  Force/Position Control of Manipulators in Task Space with Dominance in Force , 1988 .

[9]  Jeremy A. Marvel,et al.  Multi-Robot Assembly Strategies and Metrics , 2018, ACM Comput. Surv..

[10]  Giovanni De Magistris,et al.  Deep reinforcement learning for high precision assembly tasks , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[11]  W. Marsden I and J , 2012 .

[12]  C.-C. Chen,et al.  Robust Adaptive Position and Force Tracking Control Strategy for Door-Opening Behaviour , 2016 .

[13]  Lorenzo Molinari Tosatti,et al.  Force-tracking impedance control for manipulators mounted on compliant bases , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[14]  Miroslav Pajic,et al.  Fuzzy inference mechanism for recognition of contact states in intelligent robotic assembly , 2014, J. Intell. Manuf..

[15]  Mohammad S. Alam,et al.  Optimizing coverage performance of multiple random path-planning robots , 2012, Paladyn J. Behav. Robotics.

[16]  Lining Sun,et al.  Multi-sensor control for precise assembly of optical components , 2014 .

[17]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation , 1984, 1984 American Control Conference.

[18]  Kevin Kelly,et al.  Comparative Peg-in-Hole Testing of a Force-Based Manipulation Controlled Robotic Hand , 2018, IEEE Transactions on Robotics.

[19]  Masayoshi Tomizuka,et al.  A Learning Framework for High Precision Industrial Assembly , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[20]  Shuzhi Sam Ge,et al.  Neural networks impedance control of robots interacting with environments , 2013 .

[21]  John J. Craig,et al.  Hybrid position/force control of manipulators , 1981 .

[22]  Andreas Kugi,et al.  Modeling and Force Control for the Collaborative Manipulation of Deformable Strip-Like Materials , 2016 .

[23]  Ken Chen,et al.  Feedback Deep Deterministic Policy Gradient With Fuzzy Reward for Robotic Multiple Peg-in-Hole Assembly Tasks , 2019, IEEE Transactions on Industrial Informatics.

[24]  Qiang Fang,et al.  Control system designing for correcting wing–fuselage assembly deformation of a large aircraft , 2017 .

[25]  Moonhong Baeg,et al.  Compliance-Based Robotic Peg-in-Hole Assembly Strategy Without Force Feedback , 2017, IEEE Transactions on Industrial Electronics.

[26]  Haitao Liu,et al.  Force/Torque Sensorless Compliant Control Strategy for Assembly Tasks Using a 6-DOF Collaborative Robot , 2019, IEEE Access.

[27]  Michael S. Branicky,et al.  Search strategies for peg-in-hole assemblies with position uncertainty , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[28]  Silvio Savarese,et al.  Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[29]  Daniel E. Whitney,et al.  Concurrent Design of Products and Processes: A Strategy for the Next Generation in Manufacturing , 1989 .

[30]  Sangcheol Lee,et al.  Development of a new variable remote center compliance (VRCC) with modified elastomer shear pad (ESP) for robot assembly , 2005, IEEE Transactions on Automation Science and Engineering.