Towards robust personal assistant robots: Experience gained in the SRS project

SRS is a European research project for building robust personal assistant robots using ROS (Robotic Operating System) and Care-O-bot (COB) 3 as the initial demonstration platform. In this paper, experience gained while building the SRS system is presented. A main contribution of the paper is the SRS autonomous control framework. The framework is divided into two parts. First, it has an automatic task planner, which initialises actions on the symbolic level. The planner produces proactive robotic behaviours based on updated semantic knowledge. Second, it has an action executive for coordination actions at the level of sensing and actuation. The executive produces reactive behaviours in well-defined domains. The two parts are integrated by fuzzy logic based symbolic grounding. As a whole, they represent the framework for autonomous control. Based on the framework, several new components and user interfaces are integrated on top of COB's existing capabilities to enable robust fetch and carry in unstructured environments. The implementation strategy and results are discussed at the end of the paper.

[1]  D. Pham,et al.  THE BEES ALGORITHM, A NOVEL TOOL FOR COMPLEX OPTIMISATION PROBLEMS , 2006 .

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

[3]  Martin Hägele,et al.  Robotic home assistant Care-O-bot® 3 - product vision and innovation platform , 2009, 2009 IEEE Workshop on Advanced Robotics and its Social Impacts.

[4]  Geoffrey A. Hollinger,et al.  HERB: a home exploring robotic butler , 2010, Auton. Robots.

[5]  Wolfram Burgard,et al.  An Integrated Robotic System for Spatial Understanding and Situated Interaction in Indoor Environments , 2007, AAAI.

[6]  Florian Schmidt,et al.  Rollin' Justin - Design considerations and realization of a mobile platform for a humanoid upper body , 2009, 2009 IEEE International Conference on Robotics and Automation.

[7]  Duc Truong Pham,et al.  Optimal design of mechanical components using the Bees Algorithm , 2009 .

[8]  Deb Roy,et al.  Grounded Situation Models: Where Words and Percepts Meet , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Pierre-Brice Wieber,et al.  Fast resolution of hierarchized inverse kinematics with inequality constraints , 2010, 2010 IEEE International Conference on Robotics and Automation.

[10]  Moritz Tenorth,et al.  Towards Practical and Grounded Knowledge Representation Systems for Autonomous Household Robots , 2008 .

[11]  Alexander Verl,et al.  Care-O-bot® 3 - creating a product vision for service robot applications by integrating design and technology , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Mohan M. Trivedi,et al.  Task planning and action coordination in integrated sensor-based robots , 1995, IEEE Trans. Syst. Man Cybern..

[13]  Tamim Asfour,et al.  Toward humanoid manipulation in human-centred environments , 2008, Robotics Auton. Syst..

[14]  Alessandro Saffiotti,et al.  Robot task planning using semantic maps , 2008, Robotics Auton. Syst..

[15]  Renxi Qiu,et al.  User-centered design of a dynamic-autonomy remote interaction concept for manipulation-capable robots to assist elderly people in the home , 2012, HRI 2012.

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

[17]  Duc Truong Pham,et al.  Adaptive Bees Algorithm - Bioinspiration from Honeybee Foraging to Optimize Fuel Economy of a Semi-Track Air-Cushion Vehicle , 2011, Comput. J..

[18]  Christopher W. Geib,et al.  Title of the Deliverable: Publication about Multi-level Learning Sys- Tem Attachment 1 Attachment 2 a Formal Definition of Object-action Complexes and Examples at Different Levels of the Processing Hierarchy , 2022 .

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

[20]  Leslie Pack Kaelbling,et al.  Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..

[21]  Fan Yu,et al.  Bio-Inspired Binary Bees Algorithm for a Two-Level Distribution Optimisation Problem , 2010 .

[22]  Sridhar Mahadevan,et al.  Approximate planning with hierarchical partially observable Markov decision process models for robot navigation , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[23]  Nicolas Mansard,et al.  Task Sequencing for High-Level Sensor-Based Control , 2007, IEEE Transactions on Robotics.

[24]  Shuo Xu,et al.  Integration of Symbolic Task Planning into Operations within an Unstructured Environment , 2012, Int. J. Intell. Mechatronics Robotics.

[25]  Hamid Lesani,et al.  Adaptive autonomy: Smart cooperative cybernetic systems for more humane automation solutions , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

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

[27]  Moritz Tenorth,et al.  KNOWROB — knowledge processing for autonomous personal robots , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  Joris De Schutter,et al.  Extending iTaSC to support inequality constraints and non-instantaneous task specification , 2009, 2009 IEEE International Conference on Robotics and Automation.

[29]  Stefan Holzer,et al.  Towards autonomous robotic butlers: Lessons learned with the PR2 , 2011, 2011 IEEE International Conference on Robotics and Automation.

[30]  Kurt Konolige,et al.  Autonomous door opening and plugging in with a personal robot , 2010, 2010 IEEE International Conference on Robotics and Automation.

[31]  Duc Truong Pham,et al.  FUNCTIONAL ANALYSIS AND T-S FUZZY SYSTEM DESIGN , 2005 .