A Survey on Domain-Specific Languages in Robotics

2017 First IEEE International Conference on Robotic Computing (IRC)

2017

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Abstract-Control systems for autonomous robots are concurrent, 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, taking 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 Domainspecific Language (DSL) enabling domain experts to express the architectural variability of robot perception systems.

Section: Discussionmentioning

confidence: 99%

Managing the Functional Variability of Robotic Perception Systems

Brugali

2017 First IEEE International Conference on Robotic Computing (IRC)

2017

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“…Model-based approaches are getting popular in robotics [10] to structure and manage specific aspects of a design space. Those approaches propose domainspecific languages allowing the intuitive representation of functional [3], architectural [2][11] [12][13] and platform [2][11] [12] variabilities.…”

Section: Introductionmentioning

confidence: 99%

RPSL meets lightning: A model-based approach to design space exploration of robot perception systems

Gammaitoni

2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

2016

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Abstract-The design space of a robotic application defines at a meta level what are all of its possible implementations. Those possibilities are called design alternatives and differ on many different aspects, one being preferred to the other depending on how, where, when or what the application should do. Design Space Exploration (DSE) is the process of reviewing those design alternatives, prior to their implementation, with intention to verify that the set of all design alternatives to be implemented covers all the possible scenarios in which the application is to be executed. In this paper we address two challenges related to DSE, namely, (1) the formal definitions of design spaces, a non-trivial task due to the many dimensions to be taken into consideration, and (2) the automatisation of DSE, that is, enabling a domain expert to review design alternatives corresponding to a given design space effortlessly. In this paper, we address those challenges in the context of robot perception software systems by combining two already existing technologies, namely RPSL for the specification of robot perception system's design spaces and Lightning, a language workbench that we use to formalise RPSL and obtain, from RPSL specifications, corresponding design alternatives.

“…Recently, the robotics community has developed a growing interest in MDE [2] as a means to capture domain knowledge of the various robotics fields explicitly in the form of domain models [3]. Such domain models are described by domainspecific languages (DSL).…”

Section: Introductionmentioning

confidence: 99%

“…The latter point, i.e. code generation, is most frequently associated with MDE and a major focus in nowadays robotics software engineering [3]. In such an approach a part of or a complete robot system is modeled at design time, then the software is generated and often the modeling effort is forgotten.…”

Section: Introductionmentioning

confidence: 99%

Graph-based software knowledge: Storage and semantic querying of domain models for run-time adaptation

2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

Schneider

Voos

et al. 2016

Self Cite

Abstract-Software development for robots is a knowledgeintensive exercise. To capture this knowledge explicitly and formally in the form of various domain models, roboticists have recently employed model-driven engineering (MDE) approaches. However, these models are merely seen as a way to support humans during the robot's software design process. We argue that the robots themselves should be first-class consumers of this knowledge to autonomously adapt their software to the various and changing run-time requirements induced, for instance, by the robot's tasks or environment.Motivated by knowledge-enabled approaches, we address this problem by employing a graph-based knowledge representation that allows us not only to persistently store domain models, but also to formulate powerful queries for the sake of run time adaptation. We have evaluated our approach in an integrated, real-world system using the neo4j graph database and we report some lessons learned. Further, we show that the graph database imposes only little overhead on the system's overall performance.