Policy-based architectural adaptation management: Rotics domain case studies

John C. Georgas, Richard N. Taylor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Robotics is a challenging domain that exhibits a clear need for self-adaptive capabilities, as self-adaptation offers the potential for robots to account for their unstable and unpredictable deployment environments. This paper focuses on two case studies in applying a policy- and architecture-based approach to the development of self-adaptive robotic systems. We first describe our domain-independent approach for building self-adaptive systems, discuss two case studies in which we construct self-adaptive Robocode and Mindstorms robots, report on our development experiences, and discuss the challenges we encountered. The paper establishes that it is feasible to apply our approach to the robotics domain, contributes specific examples of supporting novel self-adaptive behavior, offers a discussion of the architectural issues we encountered, and further evaluates our general approach.

Original languageEnglish (US)
Title of host publicationSoftware Engineering for Self-Adaptive Systems
EditorsBetty H.C. Cheng, Rogerio de Lemos, Holger Giese, Paola Inverardi, Jeff Magee
Pages89-108
Number of pages20
DOIs
StatePublished - Oct 21 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5525 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Georgas, J. C., & Taylor, R. N. (2009). Policy-based architectural adaptation management: Rotics domain case studies. In B. H. C. Cheng, R. de Lemos, H. Giese, P. Inverardi, & J. Magee (Eds.), Software Engineering for Self-Adaptive Systems (pp. 89-108). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5525 LNCS). https://doi.org/10.1007/978-3-642-02161-9_5