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Robotic systems blend hardware and software in a holistic way that intrinsically raises many crosscutting concerns such as concurrency, uncertainty, and time constraints. These concerns make programming robotic systems challenging as expertise from multiple domains needs to be integrated conceptually and technically. Programming languages play a central role in providing a higher level of abstraction. This briefing presents a case study on the evolution of domain-specific languages based on modular robotics. The case study on the evolution of domain-specific languages is based on a series of DSL prototypes developed over five years for the domain of modular, self-reconfigurable robots.
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 different implementations may be available. The difficult challenge in robotic system engineering consists in selecting acoherent set of components, which provide the functionality required by theapplication requirements, taking into account their mutual dependencies. This challenge is exacerbated by the fact that robotics system integrators andapplication developers are usually not specifically trained in softwareengineering. Current approaches to variability management in complex software systemsconsists in explicitly modeling variation points and variants in softwarearchitectures in terms of Feature Models. The novel contribution of this paper is the description of the integration oftwo modeling languages and toolkit, namelyHyperFlex for functional variability modeling and the Robot Perception Specification Language (RPSL), a Domain-specific Language (DSL) enabling domain experts to express the architectural variability of robotperception systems.
Exploring Gridmap-based Interfaces for the Remote Control of UAVs under Bandwidth Limitations
(2017)
The successes of teleoperation scenarios for mobile robots depends on a stable and reliable communication link. The environment information collected by the robot -- represented by 2D or 3D images -- has to be provided with a high resolution and a low delay to ensure a fast and precise system response. But in most realistic applications, the communication parameters fluctuate strongly over time. It is necessary to monitor the communication link continuously to react in case of reduced bandwidth and increased delay. But which environment information and correspondingly which bandwidth is necessary to control a robot safely? Due to a missing reliable rule set we investigated this question for a UAV scenario based on two different environment representations (camera images, gridmaps). We designed a simulator based study and evaluated the capability of the participants to control a robot in case of delayed or coarsely rasterized information.
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.
The ability to detect people in domestic and unconstrained environments is crucial for every service robot. The knowledge where people are is required to perform several tasks such as navigation with dynamic obstacle avoidance and human-robot-interaction. In this paper we propose a people detection approach based on 3d data provided by a RGB-D camera. We introduce a novel 3d feature descriptor based on Local Surface Normals (LSN) which is used to learn a classifier in a supervised machine learning manner. In order to increase the systems flexibility and to detect people even under partial occlusion we introduce a top-down/bottom-up segmentation. We deployed the people detection system on a real-world service robot operating at a reasonable frame rate of 5Hz. The experimental results show that our approach is able to detect persons in various poses and motions such as sitting, walking, and running.
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.
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.
The successes of teleoperation scenarios for mobile robots depends on a stable and reliable communication link. The environment information collected by the robot — represented by 2D or 3D images — has to be provided with a high resolution and a low delay to ensure a fast and precise system response. But in most realistic applications, the communication parameters fluctuate strongly over time. It is necessary to monitor the communication link continuously to react in case of reduced bandwidth and increased delay. But which environment information and correspondingly which bandwidth is necessary to control a robot safely? Due to a missing reliable rule set we investigated this question for a UAV scenario based on two different environment representations (camera images, gridmaps). We designed a simulator based study and evaluated the capability of the participants to control a robot in case of delayed or coarsely rasterized information. Although our study involved only non-experts, we found some interesting first results. While the performance of our participants depends strongly on the delay, it is nearly independent on the image resolution, which suggests downsampling as a valid response for bandwidth decrease. We also found that participants generally struggle with using grid map for controlling the robot. However, this type of interfaces requires far less bandwidth than images. They also excel in situations with higher delays, which makes them the tool of choice when there are really bad channel conditions.
An efficient object perception is a crucial component of a mobile service robot. In this work we present a solution for visual categorization of objects. We developed a prototypic categorization system which classifies unknown objects based on their visual properties to a corresponding category of predefined domestic object categories. The system uses the Bag of Features approach which does not rely on global geometric object information. A major contribution of our work is the enhancement of the categorization accuracy and robustness through a selected combination of a set of supervised machine learners which are trained with visual information from object instances. Experimental results are provided which benchmark the behavior and verify the performance regarding the accuracy and robustness of the proposed system. The system is integrated on a mobile service robot to enhance its perceptual capabilities, hence computational cost and robot dependent properties are considered as essential design criteria.
Manufacturing industries are changing rapidly towards more flexibility and autonomy. The RoboCup Logistics League (RCLL) and RoboCup@Work tackle research questions in this domain focusing on automated reasoning and planning, and mobile manipulation respectively. However, future scenarios will require both aspects (and more) and will most likely operate with more heterogeneous systems. In this paper, we propose a cross-over challenge to foster closer cooperation among the two leagues to address these challenges. We outline four integration milestones and propose a specific scenario and task for the first milestone. The effort is driven by stakeholders of both leagues.
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.
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.
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.
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 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.
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.
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.
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.
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.
In recent years increased research activity in robotics has led to advancements in both hardware and software technologies. More complex hardware required increasingly sophisticated software infrastructures to operate it, and led to the development of several different robotics software frameworks. The driving forces behind the development of such frameworks is to cope with the heterogeneous and distributed nature of robotics software applications and to exploit more advanced software technologies in the robotics domain. So far, though, there has been not much effort to foster cooperation among these frameworks, neither on conceptual nor on implementation levels. Our research aims to analyse existing robotics software frameworks in order to identify possible levels of interoperability among them. The problem is tackled by determining a set of software concepts, in our case centering around component-based software development, which are used to determine a set of common architectural elements in an analysis of existing robotics software frameworks. The result is that these common elements can be used as interoperability points among software frameworks. Exploiting such interoperability gives developers new architectural design choices and fosters reuse of functionality already developed, albeit in another framework. It is also highly relevant for the development of new robotics software frameworks, as it opens smoother migration paths for developers to switch from one framework to another.
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.
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.
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.
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.
