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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.
The BRICS component model: a model-based development paradigm for complex robotics software systems
(2013)
Because robotic systems get more complex all the time, developers around the world have, during the last decade, created component-based software frameworks (Orocos, Open-RTM, ROS, OPRoS, SmartSoft) to support the development and reuse of "large grained" pieces of robotics software. This paper introduces the BRICS Component Model (BCM) to provide robotics developers with a set of guidelines, metamodels and tools for structuring as much as possible the development of, both, individual components and component-based architectures, using one or more of the aforementioned software frameworks at the same time, without introducing any framework- or application-specific details. The BCM is built upon two complementary paradigms: the "5Cs" (separation of concerns between the development aspects of Computation, Communication, Coordination, Configuration and Composition) and the meta-modeling approach from Model-Driven Engineering.
Deploying a complex robot software architecture on real robot systems and getting it to run reliably is a challenging task. We argue that software deployment decisions should be separated as much as possible from the core development of software functionalities. This will make the developed software more independent of a particular hardware architecture (and thus more reusable) and allow it to be deployed more flexibly on a wider variety of robot platforms. This paper presents a domain-specific language (DSL) which supports this idea and demonstrates how the DSL is used in a model-driven engineering-based development process. A practical example of applying the DSL to the development of an application for the KUKA youBot platform is given.
This work discusses how to use OSM for robotic applications and aims at starting a discussion between the OSM and the robotics community. OSM contains much topological and semantic information that can be directly used in robotics and offers various advantages: 1) Standardized format with existing tooling. 2) The graph structure allows to compose the OSM models with domain-specific semantics by adding custom nodes, relations, and key-value pairs. 3) Information about many places is already available and can be used by robots since it is driven by a community effort.
In this work a graph-based, semantic mapping approach for indoor robotics applications is presented, which is extending OpenStreetMap (OSM) with robotic-specific, semantic, topological, and geometrical information. Models for common indoor structures (such as walls, doors, corridors, elevators, etc.) are introduced. The architectural principles support composition with additional domain and application specific knowledge. As an example, a model for an area is introduced and it is explained how this can be used in navigation. A key advantages of the proposed graph-based map representation is that it allows seamless transitions between maps, e.g., indoor and outdoor maps by exploiting the hierarchical structure of the graphs. Finally, the compatibility of the approach with existing, grid-based motion planning algorithms is shown.
This paper presents a distributed world model that is able to adapt to changes in the Quality of Service (QoS) of the communication layer by online reconfiguration of perception algorithms. The approach consists of (a) a mechanism for storage, exchange and processing of world model data and (b) a feedback loop that incorporates reasoning techniques to adapt to QoS changes immediately. The latter introduces a Level of Detail (LoD) metric based on a spatial resolution in order to infer an upper bound for the amount of data that can be transmitted without violating an application specific transmission delay.
In the past, the process of developing a new robot application has had more of the design of a piece of artwork or of an act of ingenious engineering than of a structured and formalized process. The prime objective of BRICS is to structure and formalize the robot development process itself and to provide tools, models, and functional libraries, which allow reducing the development time by a magnitude. BRICS is working together with academic as well as industrial providers of robotics "components" (hardware and software), to identify and document best practices in the development of complex robotics systems, to refactor (together) the existing components in order to achieve a much higher level of reusability and robustness, and to support the robot development process with a structured tool chain and code repository. BRICS is a joint research project funded by the European Commission ICT Challenge 2 under grant number 231940. First results include the analysis of existing robot development processes, the first steps towards harmonizing robot control interfaces and component models and the set-up of robot systems for best practice analyses.
This paper presents a novel approach for representing and maintaining a shared 3D world model for robotic applications. This approach is based on the scene graph concept which has been adapted to the requirements of the robotic domain. A key feature is the temporal and centralized sharing of all available 3D data in the leaves of the graph structure. The approach enables tracking of dynamic objects, incorporates uncertainty and allows for annotations by semantic tags. A demonstration is given for a perception application that exploits the temporal sharing of 3D data. A Region of Interest (ROI) is extracted from the stored scene data in order to accelerate processing cycle times.
