RRA: Models and tools for robotics run-time adaptation

Abstract

Robotics applications are characterized by a huge amount of variability. Their design requires the developers to choose between several variants, which relate to both functionalities and hardware. Some of these choices can be taken at deployment-time, however others should be taken at run-time, when more information about the context is known. To make this possible, a software system needs to be able to reason about its current state and to adapt its architecture to provide the configuration that best suites the context. This paper presents a model-based approach for run-time adaptation of robotic systems. It defines a set of orthogonal models that represent the system architecture, its variability, and the state of the context. Additionally it introduces a set of algorithms that reason about the knowledge represented in our models to resolve the run-time variability and to adapt the system architecture. The paper discusses and evaluates the approach by means of two case studies.