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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.
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.
Motivation is a key ingredient for learning: Only if the learner is motivated, successful learning is possible. Educational robotics has proven to be an excellent tool for motivating students at all ages from 8 to 80. Robot competitions for kids, like RoboCupJunior, are instrumental to sustain motivation over a significant period of time. This increases the chances that the learner acquires more in-depth knowledge about the subject area and develops a genuine interest in the field.
Speech understanding is a fundamental feature for many applications focused on human-robot interaction. Although many techniques and several services for speech recognition and natural language understanding have been developed in the last years, specific implementation and validation on domestic service robots have not been performed. In this paper, we describe the implementation and the results of a functional benchmark for speech understanding in service robotics that has been developed and tested in the context of different robot competitions: RoboCup@Home, RoCKIn@Home and within the European Robotics League on Service Robots. Different approaches used by the teams in the competitions are presented and the evaluation results obtained in the competitions are discussed.
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.
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.
Adapting plans to changes in the environment by finding alternatives and taking advantage of opportunities is a common human behavior. The need for such behavior is often rooted in the uncertainty produced by our incomplete knowledge of the environment. While several existing planning approaches deal with such issues, artificial agents still lack the robustness that humans display in accomplishing their tasks. In this work, we address this brittleness by combining Hierarchical Task Network planning, Description Logics, and the notions of affordances and conceptual similarity. The approach allows a domestic service robot to find ways to get a job done by making substitutions. We show how knowledge is modeled, how the reasoning process is used to create a constrained planning problem, and how the system handles cases where plan generation fails due to missing/unavailable objects. The results of the evaluation for two tasks in a domestic service domain show the viability of the approach in finding and making the appropriate goal transformations.
This paper describes activities that promote robot competitions in Europe, using and expanding RoboCup concepts and best practices, through two projects funded by the European Commission under its FP7 and Horizon2020 programmes. The RoCKIn project ended in December 2015 and its goal was to speed up the progress towards smarter robots through scientific competitions. Two challenges have been selected for the competitions due to their high relevance and impact on Europes societal and industrial needs: domestic service robots (RoCKIn@Home) and innovative robot applications in industry (RoCKIn@Work). RoCKIn extended the corresponding RoboCup leagues by introducing new and prevailing research topics, such as networking mobile robots with sensors and actuators spread over the environment, in addition to specifying objective scoring and benchmark criteria and methods to assess progress. The European Robotics League (ERL) started recently and includes indoor competitions related to domestic and industrial robots, extending RoCKIn's rulebooks. Teams participating in the ERL must compete in at least two tournaments per year, which can take place either in a certified test bed (i.e., based on the rulebooks) located in a European laboratory, or as part of a major robot competition event. The scores accumulated by the teams in their best two participations are used to rank them over an year.
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.
The RoCKIn@Home 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, 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. RoCKIn develops two competitions, one for domestic service robots (RoCKIn@Home) and one for industrial robots in factories (RoCKIn@Work). The integration of benchmarking technology with the competition concept is one of the main objectives of RoCKIn.
Mobile manipulators are of high interest to industry because of the increased flexibility and effectiveness they offer. The combination and coordination of the mobility provided by a mobile platform and of the manipulation capabilities provided by a robot arm leads to complex analytical problems for research. These problems can be studied very well on the KUKA youBot, a mobile manipulator designed for education and research applications. Issues still open in research include solving the inverse kinematics problem for the unified kinematics of the mobile manipulator, including handling the kinematic redundancy introduced by the holonomic platform of the KUKA youBot. As the KUKA youBot arm has only 5 degrees of freedom, a unified platform and manipulator system is needed to compensate for the missing degree of freedom. We present the KUKA youBot as an 8 degree of freedom serial kinematic chain, suggest appropriate redundancy parameters, and solve the inverse kinematics for the 8 degrees of freedom. This enables us to perform manipulation tasks more efficiently. We discuss implementation issues, present example applications and some preliminary experimental evaluation along with discussion about redundancies.
This paper proposes an Artificial Plasmodium Algorithm (APA) mimicked a contraction wave of a plasmodium of physarum polucephalum. Plasmodia can live using the contracion wave in their body to communicate to others and transport a nutriments. In the APA, each plasmodium has two information as the wave information: the direction and food index. We apply the APA to 4 types of mazes and confirm that the APA can solve the mazes.
This paper proposes an Artificial Plasmodium Algorithm (APA) mimicked a contraction wave of a plasmodium of physarum polucephalum. Plasmodia can live using the contracion wave in their body to communicate to others and transport a nutriments. In the APA, each plasmodium has two information as the wave information: the direction and food index. We apply APA to a maze solving and route planning of road map.
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.
Just as humans behave according to the social norms of their groups, autonomous systems that become part of these groups also need to behave in socially-expected and accepted ways. For humans these social norms are learned through interaction with members of the group. In this work, we propose that the functional affordances of objects, what objects are meant to be used for, provide us with a starting point for the socialization of such agents. We model these functional affordances in description logics and show how this enables the socially-expected human behavior of substituting objects as needed to achieve a goal. In addition, we propose to combine these affordances with conceptual similarity and proximity in order to make more complex substitutions, which are socially acceptable in their given context. Finally, we describe how their use would allow the agent to take advantage of opportunities and how they are modified and extended through interaction with humans.
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.
Humans exhibit flexible and robust behavior in achieving their goals. We make suitable substitutions for objects, actions, or tools to get the job done. When opportunities that would allow us to reach our goals with less effort arise, we often take advantage of them. Robots are not nearly as robust in handling such situations. Enabling a domestic service robot to find ways to get a job done by making substitutions is the goal of our work. In this paper, we highlight the challenges faced in our approach to combine Hierarchical Task Network planning, Description Logics, and the notions of affordances and conceptual similarity. We present open questions in modeling the necessary knowledge, creating planning problems, and enabling the system to handle cases where plan generation fails due to missing/unavailable objects.
Vision-based motion detection, an important skill for an autonomous mobile robot operating in dynamic environments, is particularly challenging when the robot's camera is in motion. In this paper, we use a Fourier-Mellin transform-based image registration method to compensate for camera motion before applying temporal differencing for motion detection. The approach is evaluated online as well as offline on a set of sequences recorded with a Care-O-bot 3, and compared with a feature-based method for image registration. In comparison to the feature-based method, our method performs better both in terms of robustness of the registration and the false discovery rate.
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 developed a scene text recognition system with active vision capabilities, namely: auto-focus, adaptive aperture control and auto-zoom. Our localization system is able to delimit text regions in images with complex backgrounds, and is based on an attentional cascade, asymmetric adaboost, decision trees and Gaussian mixture models. We think that text could become a valuable source of semantic information for robots, and we aim to raise interest in it within the robotics community. Moreover, thanks to the robot’s pan-tilt-zoom camera and to the active vision behaviors, the robot can use its affordances to overcome hindrances to the performance of the perceptual task. Detrimental conditions, such as poor illumination, blur, low resolution, etc. are very hard to deal with once an image has been captured and can often be prevented. We evaluated the localization algorithm on a public dataset and one of our own with encouraging results. Furthermore, we offer an interesting experiment in active vision, which makes us consider that active sensing in general should be considered early on when addressing complex perceptual problems in embodied agents.
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.
This work presents a person independent pointing gesture recognition application. It uses simple but effective features for the robust tracking of the head and the hand of the user in an undefined environment. The application is able to detect if the tracking is lost and can be reinitialized automatically. The pointing gesture recognition accuracy is improved by the proposed fingertip detection algorithm and by the detection of the width of the face. The experimental evaluation with eight different subjects shows that the overall average pointing gesture recognition rate of the system for distances up to 250 cm (head to pointing target) is 86.63% (with a distance between objects of 23 cm). Considering just frontal pointing gestures for distances up to 250 cm the gesture recognition rate is 90.97% and for distances up to 194 cm even 95.31%. The average error angle is 7.28◦.
We propose an artificial slime mould model (ASMM) inspired by the plasmodium of Physarum polucephalum (P. polucephalum). ASMM consists of plural slimes, and each slime shares energy via a tube with neighboring slimes. Outer slimes sense their environment and conform to it. Outer slimes periodically transmit information about their surrounding environment via a contraction wave to inner slimes. Thus, ASMM shows how slimes can sense a better environment even if that environment is not adjacent to the slimes. The slimes subsequently can move in the direction of an attractant.
