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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.
We present the design and development of a benchmarking testbed for the Factory of the Future. The testbed as a physical installation enables to study, compare and assess robotics scenarios involving the integration of mobile robots and manipulators with automation equipment, large-scale integration of service robots and industrial robots, cohabitation of robots and humans, and cooperation of multiple robots and/or humans. We also report on the lessons learned of using the testbed in recent robotic competitions.
In this article we describe the architecture, algorithms and real-world benchmarks performed by Johnny Jackanapes, an autonomous service robot for domestic environments. Johnny serves as a research and development platform to explore, develop and integrate capabilities required for real-world domestic service applications. We present a control architecture which allows to cope with various and changing domestic service robot tasks. A software architecture supporting the rapid integration of functionality into a complete system is as well presented. Further, we describe novel and robust algorithms centered around multi-modal human robot interaction, semantic scene understanding and SLAM. Evaluation of the complete system has been performed during the last years in the RoboCup@Home competition where Johnnys outstanding performance led to successful participation. The results and lessons learned of these benchmarks are explained in more detail.
Most of the recent robot software frameworks follow a component-oriented development approach. They allow developers to compose a distributed application from a set of interacting components. Though these frameworks provide rich functionality, often they fail to cope with non-functional aspects (e.g., network scalability, predictability of system behavior) involved in system design, especially in distributed settings. This research sets out to address aforementioned quality attributes by introducing a pragmatic model, Protocol Stack View (PSV), for the analysis of distributed robotic software. The model relies on the fact that a distributed software can be viewed in terms of three main elements: components, ports and connectors. It specifically focuses on structure and semantics of software connectors on the implementation level. To prove effectiveness and usefulness of PSV a set of experiments were conducted to analyze scalability and to determine the configurable elements that affect it. The experiments are based on the comparison of communication infrastructure provided by two existing software packages, namely ROS and ZeroMQ.
The development of robot control programs is a complex task. Many robots are different in their electrical and mechanical structure which is also reflected in the software. Specific robot software environments support the program development, but are mainly text-based and usually applied by experts in the field with profound knowledge of the target robot. This paper presents a graphical programming environment which aims to ease the development of robot control programs. In contrast to existing graphical robot programming environments, our approach focuses on the composition of parallel action sequences. The developed environment allows to schedule independent robot actions on parallel execution lines and provides mechanism to avoid side-effects of parallel actions. The developed environment is platform-independent and based on the model-driven paradigm. The feasibility of our approach is shown by the application of the sequencer to a simulated service robot and a robot for educational purpose.
This paper presents the b-it-bots RoboCup@Work team and its current hardware and functional architecture for the KUKA youBot robot. We describe the underlying software framework and the developed capabilities required for operating in industrial environments including features such as reliable and precise navigation, flexible manipulation and robust object recognition.
This paper presents the b-it-bots RoboCup@Work team and its current hardware and functional architecture for the KUKA youBot robot.We describe the underlying software framework and the developed capabilities required for operating in industrial environments including features such as reliable and precise navigation, flexible manipulation and robust object recognition.
The design, simulation and programming of robotics systems is challenging as expertise from multiple domains needs to be integrated conceptually and technically. Domain-specific modeling promises an efficient and flexible concept for developing robotics applications that copes with this challenge. It allows to raise the level of abstraction through the use of specific concepts that are closer to the respective domain concerns and easier to understand and validate. Furthermore, it focuses on increasing the level of automation, e.g. through code generation, to bridge the gap between the modeling and the implementation levels and to improve the efficiency and quality of the software development process. Within this contribution, we survey the literature available on domain-specific (modeling) languages in robotics required to realize a state-of-the-art real-world example from the RoboCup@Work competition. We classify 41 publications in the field as reference for potential DSL users. Furthermore, we analyze these contributions from a DSL-engineering viewpoint and discuss quantitative and qualitative aspects such as the methods and tools used for DSL implementation as well as their documentation status and platform integration. Finally, we conclude with some recommendations for discussion in the robotics programming and simulation community based on the insights gained with this survey.
We are happy to present you the special issue on Best Practice in Robot Software Development of the Journal on Software Engineering for Robotics! The spark for this special issue came during the eighth workshop on Software Development and Integration in Robotics (SDIR) at the 2013 IEEE International Conference on Robotics and Automation. The workshop focused on Robot Software Architectures, and the fruitful discussions made it clear that the design, development, and deployment of robot software is always an interplay between competing aspects. These are often couched in antagonistic pairs, such as dependability versus performance, and prominently include quality attributes as well as functional, nonfunctional, and application requirements.
The RoCKIn@Work Challenge
(2014)
RoCKIn is a EU-funded project aiming to foster scientific progress and innovation in cognitive systems and robotics through the design and implementation of competitions. An additional objective of RoCKIn is to increase public awareness of the current state-of-the-art in robotics in Europe and to demonstrate the innovation potential of robotics applications for solving societal challenges and improving the competitiveness of Europe in the global markets. In order to achieve these objectives, RoCKIn develops two competitions, one for domestic service robots (RoCKIn@Home) and one for industrial robots in factories (RoCKIn@Work). These competitions are designed around challenges that are based on easy-to-communicate and convincing user stories, which catch the interest of both the general public and the scientific community. The latter is in particular interested in solving open scientific challenges and to thoroughly assess, compare, and evaluate the developed approaches with competing ones. To allow this to happen, the competitions are designed to meet the requirements of benchmarking procedures and good experimental methods. The integration of benchmarking technology with the competition concept is one of the main objectives of RoCKIn.
RPSL meets lightning: A model-based approach to design space exploration of robot perception systems
(2016)
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.
RoCKIn@Work was focused on benchmarks in the domain of industrial robots. Both task and functionality benchmarks were derived from real world applications. All of them were part of a bigger user story painting the picture of a scaled down real world factory scenario. Elements used to build the testbed were chosen from common materials in modern manufacturing environments. Networked devices, machines controllable through a central software component, were also part of the testbed and introduced a dynamic component to the task benchmarks. Strict guidelines on data logging were imposed on participating teams to ensure gathered data could be automatically evaluated. This also had the positive effect that teams were made aware of the importance of data logging, not only during a competition but also during research as useful utility in their own laboratory. Tasks and functionality benchmarks are explained in detail, starting with their use case in industry, further detailing their execution and providing information on scoring and ranking mechanisms for the specific benchmark.
Robot programming is an interdisciplinary and knowledge-intensive task. All too often, knowledge of the different robotics domains remains implicit. Although, this is slowly changing with the rising interest in explicit knowledge representations through domain-specific languages (DSL), very little is known about the DSL design and development processes themselves. To this end, we present and discuss the reverse-engineered process from the development of our Grasp Domain Definition Language (GDDL), a declarative DSL for the explicit specification of grasping problems. An important finding is that the process comprises similar building blocks as existing software development processes, like the Unified Process.
Competitions for Benchmarking: Task and Functionality Scoring Complete Performance Assessment
(2015)
Scientific experiments and robotic competitions share some common traits that can put the debate about developing better experimental methodologies and replicability of results in robotics research on more solid ground. In this context, the Robot Competitions Kick Innovation in Cognitive Systems and Robotics (RoCKIn) project aims to develop competitions that come close to scientific experiments, providing an objective performance evaluation of robot systems under controlled and replicable conditions. In this article, by further articulating replicability into reproducibility and repeatability and by considering some results from the 2014 first RoCKIn competition, we show that the RoCKIn approach offers tools that enable the replicability of experimental results.
Service robots become increasingly capable and deliver a broader spectrum of services which all require a wide range of perceptual capabilities. These capabilities must cope with dynamically changing requirements which make the design and implementation of a robot perception architecture a complex and tedious exercise which is prone to error. We suggest to specify the integral parts of robot perception architectures using explicit models, which allows to easily configure, modify, and validate them. The paper presents the domain-specific language RPSL, some examples of its application, the current state of implementation and some validation experiments.
The development of advanced robotic systems is challenging as expertise from multiple domains needs to be integrated conceptually and technically. Model-driven engineering promises an efficient and flexible approach for developing robotics applications that copes with this challenge. Domain-specific modeling allows to describe robotics concerns with concepts and notations closer to the respective problem domain. This raises the level of abstraction and results in models that are easier to understand and validate. Furthermore, model-driven engineering allows to increase the level of automation, e.g. through code generation, and to bridge the gap between modeling and implementation. The anticipated results are improved efficiency and quality of the robotics systems engineering process. Within this contribution, we survey the available literature on domain-specific modeling and languages that target core robotics concerns. In total 137 publications were identified that comply with a set of defined criteria, which we consider essential for contributions in this field. With the presented survey, we provide an overview on the state-of-the-art of domain-specific modeling approaches in robotics. The surveyed publications are investigated from the perspective of users and developers of model-based approaches in robotics along a set of quantitative and qualitative research questions. The presented quantitative analysis clearly indicates the rising popularity of applying domain-specific modeling approaches to robotics in the academic community. Beyond this statistical analysis, we map the selected publications to a defined set of robotics subdomains and typical development phases in robotic systems engineering as reference for potential users. Furthermore, we analyze these contributions from a language engineering viewpoint and discuss aspects such as the methods and tools used for their implementation as well as their documentation status, platform integration, typical use cases and the evaluation strategies used for validation of the proposed approaches. Finally, we conclude with recommendations for discussion in the model-driven engineering and robotics community based on the insights gained in this survey.
Mobile manipulators are viewed as an essential component for making the factory of the future become a reality. RoboCup@Work is a competition designed by a group of researchers from the RoboCup community and focuses on the use of mobile manipulators and their integration with automation equipment for performing industrially-relevant tasks. The paper describes the design and implementation of the competition and the experiences made so far.
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.
Robotics applications are characterized by a huge amount of variability. Their design requires the developers to choose between several variants, which relate to both functionalities and hardware. Some of these choices can be taken at deployment-time, however others should be taken at run-time, when more information about the context is known. To make this possible, a software system needs to be able to reason about its current state and to adapt its architecture to provide the configuration that best suites the context. This paper presents a model-based approach for run-time adaptation of robotic systems. It defines a set of orthogonal models that represent the system architecture, its variability, and the state of the context. Additionally it introduces a set of algorithms that reason about the knowledge represented in our models to resolve the run-time variability and to adapt the system architecture. The paper discusses and evaluates the approach by means of two case studies.
Mobile manipulators are intended to be deployed in domestic and industrial environments where they will carry out tasks that require physical interaction with the surrounding world, for example, picking or handing over fragile objects. In-hand slippage, i.e. a grasped object moving within the robot’s grasp, is inherent to many of these tasks and thus, a robot’s ability to detect a slippage is vital for executing a manipulation task successfully. In this paper, we develop a slip detection approach which is based on the robot’s tactile sensors, a force/torque sensor and a combination thereof. The evaluation of our approach, carried out on the Care-O-bot 3 platform, highly suggests that the actions and motions performed by the robot during grasping should be taken into account during slip detection for improved performance. Based on this insight, we propose an in-hand slip detection architecture that is able to adapt to the current robot’s actions at run time.
Grasping objects and using them in a task-oriented manner is challenging for a robot. It requires an understanding of the object, the robot's capabilities, and the task to be executed. We argue that an explicit representation of these domains increases reusability and robustness of the resulting system. We present the declarative Grasp Domain Definition Language (GDDL) which enables the explicit grasp-planner-independent specification of grasping problems. The formal model underlying GDDL enables the definition of formal constraints that are validated during design time and run time. Our approach has been realized on two real robots.
To perform a wide range of tasks service robots need to robustly extract knowledge about the world from the data perceived through the robot's sensors even in the presence of varying context-conditions. This makes the design and development of robot perception architectures a challenging exercise. In this paper we propose a robot perception architecture which enables to select and execute at runtime different perception graphs based on monitored context changes. To achieve this the architecture is structured as a feedback loop and contains a repository of different perception graph configurations suitable for various context conditions.
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.
