Run-Time Compositional Software Platform for Autonomous NXT Robots

Autonomous Robots normally perform tasks in unstructured environments, with little or no continuous human guidance. This calls for context-aware, self-adaptive software systems. This paper aims at providing a flexible adaptive middleware platform to seamlessly integrate multiple adaptation logics during the run-time. To support such an approach, a reconfigurable middleware system "ACCADA" was designed to provide compositional adaptation. During the run-time, context knowledge is used to select the most appropriate adaptation modules so as to compose an adaptive system best-matching the current exogenous and endogenous conditions. Together with a structure modeler, this allows robotic applications' structure to be autonomously (re)-constructed and (re)-configured. This paper applies this model on a Lego NXT robot system. A remote NXT model is designed to wrap and expose native NXT devices into service components that can be managed during the run-time. A dynamic UI is implemented which can be changed and customized according to system conditions. Results show that the framework changes robot adaptation behavior during the run-time.

[1]  Bradley R. Schmerl,et al.  Rainbow: Architecture-Based Self-Adaptation with Reusable Infrastructure , 2004, Computer.

[2]  Fabienne Boyer,et al.  Using components for architecture-based management , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.

[3]  Hong Sun,et al.  An Architecture-Based Framework for Managing Adaptive Real-Time Applications , 2009, 2009 35th Euromicro Conference on Software Engineering and Advanced Applications.

[4]  David Garlan,et al.  Rainbow: architecture-based self-adaptation with reusable infrastructure , 2004 .

[5]  Nandish V. Patel.  Adaptive Evolutionary Information Systems , 2002 .

[6]  Nicoletta Sala,et al.  About the Use of Computational Fluid Dynamics (CFD) in the Framework of Physical Limnological Studies on a Great Lake , 2008 .

[7]  Raymond Chiong.  Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications , 2010, Intelligent Systems for Automated Learning and Adaptation.

[8]  Yuri Pavlov,et al.  Decision Control, Management, and Support in Adaptive and Complex Systems: Quantitative Models , 2013 .

[9]  Hong Sun,et al.  A Generic Adaptation Framework for Mobile Communication , 2011, Int. J. Adapt. Resilient Auton. Syst..

[10]  Gary G. Yen.  Evolutionary Based Adaptive User Interfaces in Complex Supervisory Tasks , 2010, Intelligent Systems for Automated Learning and Adaptation.

[11]  Fumio Kojima,et al.  Perceptual system for a mobile robot under a dynamic environment , 2003, Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694).

[12]  Mina Ryoke,et al.  Analyzing the Business Strategies of Mobile Phone Operators Using Agent-Based Simulation , 2013, Int. J. Knowl. Syst. Sci..

[13]  Dirk Riehle,et al.  Pattern Languages of Program Design 3 , 1997 .

[14]  Vincenzo De Florio,et al.  Innovations and Approaches for Resilient and Adaptive Systems , 2012 .

[15]  Olac Fuentes,et al.  Using Evolution Strategies to perform stellar population synthesis for galaxy spectra from SDSS , 2007, 2007 IEEE Congress on Evolutionary Computation.

[16]  Richard S. Hall,et al.  Challenges in building service-oriented applications for OSGi , 2004, IEEE Communications Magazine.

[17]  Masayuki Inaba,et al.  User adaptation of human-robot interaction model based on Bayesian network and introspection of interaction experience , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[18]  Ladan Tahvildari,et al.  Self-adaptive software: Landscape and research challenges , 2009, TAAS.

[19]  Bradley R. Schmerl,et al.  Rainbow: architecture-based self-adaptation with reusable infrastructure , 2004, International Conference on Autonomic Computing, 2004. Proceedings..

[20]  Jeffrey O. Kephart,et al.  The Vision of Autonomic Computing , 2003, Computer.

[21]  Hong Sun,et al.  A framework for adaptive real-time applications: the declarative real-time OSGi component model , 2008, ARM '08.

[22]  Yijun Liu,et al.  Expert Mining and Traditional Chinese Medicine Knowledge , 2010, Int. J. Knowl. Syst. Sci..

[23]  Hong Sun,et al.  ACCADA: A Framework for Continuous Context-Aware Deployment and Adaptation , 2009, SSS.

[24]  Kinshuk,et al.  Intelligent and Adaptive Learning Systems: Technology Enhanced Support for Learners and Teachers , 2011 .

[25]  Wei-Chiang Samuelson Hong.  Principal Concepts in Applied Evolutionary Computation: Emerging Trends , 2012 .

[26]  Faiza Charfi,et al.  Performance Study of a New CSMA/CA Access Method with QoS Based on 802.11b and Comparison with 802.15.4/ZigBee , 2013, Int. J. Syst. Dyn. Appl..

[27]  原田 秀逸.  私の computer 環境 , 1998 .

[28]  W. B. Lee.  Systems Approaches to Knowledge Management, Transfer, and Resource Development , 2012 .

[29]  Leif Edvinsson,et al.  Evolution of IC Science and Beyond , 2010, Int. J. Knowl. Syst. Sci..

[30]  Nicoletta Sala,et al.  Reflexing Interfaces: The Complex Coevolution of Information Technology Ecosystems , 2008 .

[31]  James O. Coplien,et al.  Pattern languages of program design , 1995 .

[32]  K. Boulding.  General Systems Theory---The Skeleton of Science , 1956 .