A framework for end-user instruction of a robot assistant for manufacturing

Small Manufacturing Entities (SMEs) have not incorporated robotic automation as readily as large companies due to rapidly changing product lines, complex and dexterous tasks, and the high cost of start-up. While recent low-cost robots such as the Universal Robots UR5 and Rethink Robotics Baxter are more economical and feature improved programming interfaces, based on our discussions with manufacturers further incorporation of robots into the manufacturing work flow is limited by the ability of these systems to generalize across tasks and handle environmental variation. Our goal is to create a system designed for small manufacturers that contains a set of capabilities useful for a wide range of tasks, is both powerful and easy to use, allows for perceptually grounded actions, and is able to accumulate, abstract, and reuse plans that have been taught. We present an extension to Behavior Trees that allows for representing the system capabilities of a robot as a set of generalizable operations that are exposed to an end-user for creating task plans. We implement this framework in CoSTAR, the Collaborative System for Task Automation and Recognition, and demonstrate its effectiveness with two case studies. We first perform a complex tool-based object manipulation task in a laboratory setting. We then show the deployment of our system in an SME where we automate a machine tending task that was not possible with current off the shelf robots.

[1]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[2]  Petter Ögren,et al.  Towards a unified behavior trees framework for robot control , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[3]  Pieter Abbeel,et al.  A textured object recognition pipeline for color and depth image data , 2012, 2012 IEEE International Conference on Robotics and Automation.

[4]  Petter Ögren,et al.  How Behavior Trees modularize robustness and safety in hybrid systems , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Satyandra K. Gupta,et al.  Knowledge driven robotics for kitting applications , 2013, Robotics Auton. Syst..

[6]  Ian Millington,et al.  Artificial Intelligence for Games , 2006, The Morgan Kaufmann series in interactive 3D technology.

[7]  Richard Fikes,et al.  STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.

[8]  Norman I. Badler,et al.  Parameterizing Behavior Trees , 2011, MIG.

[9]  Steve Cousins,et al.  The SMACH High-Level Executive , 2010 .

[10]  Henrik I. Christensen,et al.  Modeling Robot Assembly Tasks in Manufacturing Using SysML , 2014, ISR 2014.

[11]  Aaron F. Bobick,et al.  Anticipating human actions for collaboration in the presence of task and sensor uncertainty , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[12]  Jörg Krüger,et al.  Safe Physical Human-Robot Interaction through Sensorless External Force Estimation for Industrial Robots , 2013, HCI.

[13]  Rolf Dieter Schraft,et al.  PowerMate – A Safe and Intuitive Robot Assistant for Handling and Assembly Tasks , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[14]  Volker Krüger,et al.  Intuitive skill-level programming of industrial handling tasks on a mobile manipulator , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Gregory D. Hager,et al.  Adjutant: A framework for flexible human-machine collaborative systems , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  M. Hagele,et al.  rob@work: Robot assistant in industrial environments , 2002, Proceedings. 11th IEEE International Workshop on Robot and Human Interactive Communication.

[17]  Alessandro De Luca,et al.  Safe physical human-robot collaboration , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Jonathan Bohren,et al.  The SMACH High-Level Executive [ROS News] , 2010 .

[19]  Simon Colton,et al.  Evolving Behaviour Trees for the Commercial Game DEFCON , 2010, EvoApplications.

[20]  Matei T. Ciocarlie,et al.  ROS commander (ROSCo): Behavior creation for home robots , 2013, 2013 IEEE International Conference on Robotics and Automation.

[21]  Moritz Tenorth,et al.  KnowRob: A knowledge processing infrastructure for cognition-enabled robots , 2013, Int. J. Robotics Res..

[22]  Neil T. Dantam,et al.  The Motion Grammar for physical human-robot games , 2011, 2011 IEEE International Conference on Robotics and Automation.