Control systems for autonomous robots are concur- rent, distributed, embedded, real-time and data intensive software systems. A real-world robot control system is composed of tens of software components. For each component providing robotic functionality, tens of different implementations may be available. The difficult challenge in robotic system engineering consists in selecting a coherent set of components, which provide the functionality required by the application requirements, tak- ing into account their mutual dependencies. This challenge is exacerbated by the fact that robotics system integrators and application developers are usually not specifically trained in software engineering. Current approaches to variability management in complex software systems consists in explicitly modeling variation points and variants in software architectures in terms of Feature Models. The novel contribution of this paper is the description of the integration of two modeling languages and toolkit, namely HyperFlex [14] for functional variability modeling and the Robot Perception Specification Language (RPSL) [17], a Domain- specific Language (DSL) enabling domain experts to express the architectural variability of robot perception systems.

(2017). Managing the Functional Variability of Robotic Perception Systems . Retrieved from http://hdl.handle.net/10446/86223

Managing the Functional Variability of Robotic Perception Systems

Brugali, Davide;
2017-01-01

Abstract

Control systems for autonomous robots are concur- rent, distributed, embedded, real-time and data intensive software systems. A real-world robot control system is composed of tens of software components. For each component providing robotic functionality, tens of different implementations may be available. The difficult challenge in robotic system engineering consists in selecting a coherent set of components, which provide the functionality required by the application requirements, tak- ing into account their mutual dependencies. This challenge is exacerbated by the fact that robotics system integrators and application developers are usually not specifically trained in software engineering. Current approaches to variability management in complex software systems consists in explicitly modeling variation points and variants in software architectures in terms of Feature Models. The novel contribution of this paper is the description of the integration of two modeling languages and toolkit, namely HyperFlex [14] for functional variability modeling and the Robot Perception Specification Language (RPSL) [17], a Domain- specific Language (DSL) enabling domain experts to express the architectural variability of robot perception systems.
Brugali, Davide; Hochgeschwender, Nico
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/86223
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