Modeling Robotics Tasks for Better Separation of Concerns, Platform-Independence, and Reuse
Results of the iserveU Federal Research Project
1st Edition
Published on 31. December 2017
Book
Paperback/Softback
136 pages
978-3-8440-5319-7 (ISBN)
Description
Deploying robotics applications requires expertise from multiple domains, including general software engineering and the application domain itself. Consequently, successful robotics applications are developed by teams of software experts, robotics experts, and application domain experts. The conceptual gap between application domain challenges and implementation domain solutions gives rise to accidental complexities from solving problem domain challenges with programming domain details. This complicates development and may lead to failure. Domain and robotics experts are rarely software engineering experts. Their involvement into the engineering of reusable robotics applications either requires to become software experts or abstracting from implementation details.
Model-driven engineering reduces the conceptual gap by leveraging models to primary development artifacts. This supports domain experts in formulating solutions in established known vocabulary. Model transformations can embody the software engineering expertise required to translate such models into robust and reusable programming language solutions. Proper separation of expert concerns is crucial to enable transformations and ultimately improve engineering of robotics applications.
We present a collection of domain-specific languages to describe service robotics applications. They enable formulating domains, tasks, actions, and properties of a robot and its environment free of GPL complexities. Their models are translated into component implementations of a MontiArcAutomaton software architecture model and interpreted at system runtime. The architecture executes tasks via their transformation to Planning Domain Definition Language models, solves these, and executes the resulting plans with a robotics middleware. Leveraging separation of concerns and abstraction of modeling languages, this reduces the effort of describing robot tasks, facilitates extension of the system with new components, and decouples logical task solving from the robot platform. This supports collaboration with domain experts and reuse of infrastructure constituents in different contexts and with different platforms.
Model-driven engineering reduces the conceptual gap by leveraging models to primary development artifacts. This supports domain experts in formulating solutions in established known vocabulary. Model transformations can embody the software engineering expertise required to translate such models into robust and reusable programming language solutions. Proper separation of expert concerns is crucial to enable transformations and ultimately improve engineering of robotics applications.
We present a collection of domain-specific languages to describe service robotics applications. They enable formulating domains, tasks, actions, and properties of a robot and its environment free of GPL complexities. Their models are translated into component implementations of a MontiArcAutomaton software architecture model and interpreted at system runtime. The architecture executes tasks via their transformation to Planning Domain Definition Language models, solves these, and executes the resulting plans with a robotics middleware. Leveraging separation of concerns and abstraction of modeling languages, this reduces the effort of describing robot tasks, facilitates extension of the system with new components, and decouples logical task solving from the robot platform. This supports collaboration with domain experts and reuse of infrastructure constituents in different contexts and with different platforms.