SummaryIn recent years the Robot Operating System (Quigley et al. 2009) (ROS) has become the 'de facto' standard framework for robotics software development. The ros_control framework provides the capability to implement and manage robot controllers with a focus on both real-time performance and sharing of controllers in a robot-agnostic way. The primary motivation for a sepate robot-control framework is the lack of realtimesafe communication layer in ROS. Furthermore, the framework implements solutions for controller-lifecycle and hardware resource management as well as abstractions on hardware interfaces with minimal assumptions on hardware or operating system. The clear, modular design of ros_control makes it ideal for both research and industrial use and has indeed seen many such applications to date. The idea of ros_control originates from the pr2_controller_manager framework specific to the PR2 robot but ros_control is fully robot-agnostic. Controllers expose standard ROS interfaces for out-of-the box 3rd party solutions to robotics problems like manipulation path planning (MoveIt! (Chitta, Sucan, and Cousins 2012)) and autonomous navigation (the ROS navigation stack). Hence, a robot made up of a mobile base and an arm that support ros_control doesn't need any additional code to be written, only a few controller configuration files and it is ready to navigate autonomously and do path planning for the arm. ros_control also provides several libraries to support writing custom controllers.

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In principle, Model-Driven Engineering (MDE) addresses central aspects of robotics software development. Domain experts could leverage the expressiveness of models; implementation details over different hardware could be handled by automatic code generation. In practice, most evidence points to manual code development as the norm, despite several MDE efforts in robotics. Possible reasons for this disconnect are the wide ranges of applications and target platforms making allencompassing MDE IDEs hard to develop and maintain, with developers reverting to writing code manually. Acknowledging this, and given the opportunity to leverage a large corpus of opensource software widely adopted by the robotics community, we pursue modeling as a complement, rather than an alternative, to manually written code. Our previous work introduced metamodels to describe components, their interactions, and their resulting composition, as inspired by, but not limited to, the de-facto standard Robot Operating System (ROS). In this paper we put such metamodels into use through two contributions [1]. First, we automate the generation of models from manually written artifacts through extraction from source code and runtime system monitoring. Second, we make available an easy-to-use web infrastructure to perform the extraction, together with a growing database of models so generated. Our aim with this tooling, publicly available both as-a-service and as source code, is to lower the MDE barrier for practitioners and leverage models to 1) improve the understanding of manually written code; 2) perform correctness checks; and 3) systematize the definition and adoption of best practices through large-scale generation of models from existing code. A comprehensive example is provided as a walk-through for robotics software practitioners.

Ten years after its first release, the Robot Operating System (ROS) is arguably the most popular software framework used to program robots. It achieved such status despite its shortcomings compared to alternatives similarly centered on manual programming and, perhaps surprisingly, to model-driven engineering (MDE) approaches. Based on our experience as users and developers of both ROS and MDE tools, we identified possible ways to leverage the accessibility of ROS and its large software ecosystem, while providing quality assurance measures through selected MDE techniques. After describing our vision on how to combine MDE and manually written code, we present the first technical contribution in this pursuit: a family of three metamodels to respectively model ROS nodes, communication interfaces, and systems composed from subsystems. Such metamodels can be used, through the accompanying Eclipse-based tooling made publicly available, to model ROS systems of arbitrary complexity and generate with correctness guarantees the software artifacts for their composition and deployment. Furthermore, they account for specifications on these aspects by the Object Management Group (OMG), in order to be amenable to hybrid systems coupling ROS and other frameworks. We also report on our experience with a large and complex corpus of ROS software used in a commercially deployed robot (the Care-O-bot 4), to explain the rationale of the presented work, including the shortcomings of standard ROS tools and of previous efforts on ROS modeling.

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