Combined pose-wrench and state machine representation for modeling Robotic Assembly Skills

A new Robotic Assembly Skill (RAS) modeling framework is proposed. An assembly skill is a primitive that encapsulates the capabilities to coordinate, control and supervise an elementary robot task. To gain reusability of a primitive in alike robot tasks, the primitives are represented as generic templates that are parametrized for each situation with data from an assembly specification. A skill is represented in two ways, namely as a trajectory describing compliant motions in pose-wrench space and as a finite state machine. This approach comes with the potential to simplify robot programming and to improve robustness in robotic assembly due to inherent quality checking. The approach is implemented on an ABB YuMi robot performing the assembly of a programmable logic controller (PLC) I/O module.

[1]  Joris De Schutter,et al.  Constraint-based Task Specification and Estimation for Sensor-Based Robot Systems in the Presence of Geometric Uncertainty , 2007, Int. J. Robotics Res..

[2]  Bernhard Rumpe,et al.  A new skill based robot programming language using UML/P Statecharts , 2013, 2013 IEEE International Conference on Robotics and Automation.

[3]  Alois Knoll,et al.  Adaptive Control for Human-Robot Skilltransfer: Trajectory Planning Based on Fluid Dynamics , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[4]  T. Suehiro,et al.  A model-based manipulation system with skill-based execution in unstructured environment , 1991 .

[5]  Ruben Smits.  Robot Skills: Design of a Constraint-Based Methodology and Software Support (Robot vaardigheden: Ontwerp van een beperkingsgebaseerde methodologie en software ondersteuning) , 2010 .

[6]  Paul R. Bonenberger,et al.  The First Snap-Fit Handbook , 2000 .

[7]  Magnus Linderoth,et al.  Robotic Work-Space Sensing and Control , 2011 .

[8]  Andreas Stolt,et al.  A System for High-Level Task Specification Using Complex Sensor-Based Skills , 2013, RSS 2013.

[9]  Ivan Lundberg,et al.  Robot concept for scalable, flexible assembly automation: A technology study on a harmless dual-armed robot , 2011, 2011 IEEE International Symposium on Assembly and Manufacturing (ISAM).

[10]  Anders Robertsson,et al.  Force controlled robotic assembly without a force sensor , 2012, 2012 IEEE International Conference on Robotics and Automation.

[11]  Sungchul Kang,et al.  External force estimation using joint torque sensors and its application to impedance control of a robot manipulator , 2013, 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013).

[12]  S. Aramaki,et al.  The representation method of robotic assembly task with click action , 2008, 2008 International Symposium on Power Electronics, Electrical Drives, Automation and Motion.

[13]  Geir Hovland,et al.  Hybrid dynamic modeling and control of constrained manipulation systems , 1997, IEEE Robotics Autom. Mag..

[14]  Joris De Schutter,et al.  iTASC: a tool for multi-sensor integration in robot manipulation , 2008, 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.

[15]  Ulrike Thomas,et al.  Compliant motion programming: The task frame formalism revisited , 2004 .

[16]  Jun Nakanishi,et al.  Learning Attractor Landscapes for Learning Motor Primitives , 2002, NIPS.

[17]  Arne Wahrburg,et al.  Contact force estimation for robotic assembly using motor torques , 2014, 2014 IEEE International Conference on Automation Science and Engineering (CASE).

[18]  Ole Madsen,et al.  Does your Robot have Skills , 2012 .

[19]  Oliver Kroemer,et al.  Robot Skill Learning , 2012, ECAI.