Robotic systems are complex, software intensive and heterogeneous composite systems. Software systems engineering and system integration is still a major challenge in robotics. We describe how component based software engineering (CBSE), model-driven software development (MDSD) and domain-specific languages (DSLs) for variability management complement each other in addressing the robotics software challenge. We outline how these approaches pave the way towards a software business ecosystem in robotics. We put a focus onto challenges still being considered as open and worth being addressed next.

No abstract

Complex systems are executed in environments with a huge number of potential situations and contingencies, therefore a mechanism is required to express dynamic variability at design-time that can be efficiently resolved in the application at run-time based on the then available information. We present an approach for dynamic variability modeling and its exploitation at run-time. It supports different developer roles and allows the separation of two different kinds of dynamic variability at design-time: (i) variability related to the system operation, and (ii) variability associated with QoS. The former provides robustness to contingencies, maintaining a high success rate in task fulfillment. The latter focuses on the quality of the application execution (defined in terms of non-functional properties like safety or task efficiency) under changing situations and limited resources. The authors also discuss different alternatives for the run-time integration of the two variability management mechanisms, and show real-world robotic examples to illustrate them.

No abstract

No abstract