Run-Time Compositional Software Platform for Autonomous NXT Robots

Abstract

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.