Name
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 diferent 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. In various application domains, software product line (SPL) development has proven to be the most effective approach to face this kind of challenges. In a previous paper [D. Brugali and N. Hochgeschwender, Managing the functional variability of robotic perception systems, in First IEEE Int. Conf. Robotic Computing, 2017, pp. 277-283.] we have presented a model-based approach to the development of SPL for robotic perception systems, which integrates two modeling technologies developed by the authors: The HyperFlex toolkit [L. Gherardi and D. Brugali, Modeling and reusing robotic software architectures: The HyperFlex toolchain, in IEEE Int. Conf. Robotics and Automation, 2014, pp. 6414-6420.] and the Robot Perception Specification Language (RPSL) [N. Hochgeschwender, S. Schneider, H. Voos and G. K. Kraetzschmar, Declarative specification of robot perception architectures, in 4th Int. Conf. Simulation, Modeling, and Programming for Autonomous Robots, 2014, pp. 291-302.]. This paper extends our previous work by illustrating the entire development process of an SPL for robot perception systems with a real case study. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1142/S1793351X18400056 | INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING |
Keywords | Field | DocType |
Robot perception, software variability, model-driven engineering | Software engineering,Computer science,Model-driven architecture,Software system,Software,Software product line,Artificial intelligence,Control system,Robot,Robot control system,Perception,Machine learning | Journal |
Volume | Issue | ISSN |
12 | 1 | 1793-351X |
Citations | PageRank | References |
2 | 0.41 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Davide Brugali | 1 | 370 | 38.83 |
| Nico Hochgeschwender | 2 | 126 | 15.75 |