3,485 research outputs found
MROS: Runtime Adaptation For Robot Control Architectures
Known attempts to build autonomous robots rely on complex control
architectures, often implemented with the Robot Operating System platform
(ROS). Runtime adaptation is needed in these systems, to cope with component
failures and with contingencies arising from dynamic environments-otherwise,
these affect the reliability and quality of the mission execution. Existing
proposals on how to build self-adaptive systems in robotics usually require a
major re-design of the control architecture and rely on complex tools
unfamiliar to the robotics community. Moreover, they are hard to reuse across
applications.
This paper presents MROS: a model-based framework for run-time adaptation of
robot control architectures based on ROS. MROS uses a combination of
domain-specific languages to model architectural variants and captures mission
quality concerns, and an ontology-based implementation of the MAPE-K and
meta-control visions for run-time adaptation. The experiment results obtained
applying MROS in two realistic ROS-based robotic demonstrators show the
benefits of our approach in terms of the quality of the mission execution, and
MROS' extensibility and re-usability across robotic applications
Transfer Learning for Improving Model Predictions in Highly Configurable Software
Modern software systems are built to be used in dynamic environments using
configuration capabilities to adapt to changes and external uncertainties. In a
self-adaptation context, we are often interested in reasoning about the
performance of the systems under different configurations. Usually, we learn a
black-box model based on real measurements to predict the performance of the
system given a specific configuration. However, as modern systems become more
complex, there are many configuration parameters that may interact and we end
up learning an exponentially large configuration space. Naturally, this does
not scale when relying on real measurements in the actual changing environment.
We propose a different solution: Instead of taking the measurements from the
real system, we learn the model using samples from other sources, such as
simulators that approximate performance of the real system at low cost. We
define a cost model that transform the traditional view of model learning into
a multi-objective problem that not only takes into account model accuracy but
also measurements effort as well. We evaluate our cost-aware transfer learning
solution using real-world configurable software including (i) a robotic system,
(ii) 3 different stream processing applications, and (iii) a NoSQL database
system. The experimental results demonstrate that our approach can achieve (a)
a high prediction accuracy, as well as (b) a high model reliability.Comment: To be published in the proceedings of the 12th International
Symposium on Software Engineering for Adaptive and Self-Managing Systems
(SEAMS'17
Towards adaptive multi-robot systems: self-organization and self-adaptation
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible
Using the DiaSpec design language and compiler to develop robotics systems
A Sense/Compute/Control (SCC) application is one that interacts with the
physical environment. Such applications are pervasive in domains such as
building automation, assisted living, and autonomic computing. Developing an
SCC application is complex because: (1) the implementation must address both
the interaction with the environment and the application logic; (2) any
evolution in the environment must be reflected in the implementation of the
application; (3) correctness is essential, as effects on the physical
environment can have irreversible consequences. The SCC architectural pattern
and the DiaSpec domain-specific design language propose a framework to guide
the design of such applications. From a design description in DiaSpec, the
DiaSpec compiler is capable of generating a programming framework that guides
the developer in implementing the design and that provides runtime support. In
this paper, we report on an experiment using DiaSpec (both the design language
and compiler) to develop a standard robotics application. We discuss the
benefits and problems of using DiaSpec in a robotics setting and present some
changes that would make DiaSpec a better framework in this setting.Comment: DSLRob'11: Domain-Specific Languages and models for ROBotic systems
(2011
A Case Study on Formal Verification of Self-Adaptive Behaviors in a Decentralized System
Self-adaptation is a promising approach to manage the complexity of modern
software systems. A self-adaptive system is able to adapt autonomously to
internal dynamics and changing conditions in the environment to achieve
particular quality goals. Our particular interest is in decentralized
self-adaptive systems, in which central control of adaptation is not an option.
One important challenge in self-adaptive systems, in particular those with
decentralized control of adaptation, is to provide guarantees about the
intended runtime qualities. In this paper, we present a case study in which we
use model checking to verify behavioral properties of a decentralized
self-adaptive system. Concretely, we contribute with a formalized architecture
model of a decentralized traffic monitoring system and prove a number of
self-adaptation properties for flexibility and robustness. To model the main
processes in the system we use timed automata, and for the specification of the
required properties we use timed computation tree logic. We use the Uppaal tool
to specify the system and verify the flexibility and robustness properties.Comment: In Proceedings FOCLASA 2012, arXiv:1208.432
Using Models@Runtime for Designing Adaptive Robotics Software: an Experience Report
Robotic systems are becoming increasingly complex, as their tasks and working environments become ever richer. As a result, there is an urgent need to provide robots with self-awareness and self-adaptation capabilities that allow them to autonomously deal, among other things,
with software and hardware failures, changes in the environment, or interactions with other systems. The use of high-level models that can be adapted at run-time by the robot itself, promises to signi cantly boost the applicability and performance of robotic systems. This paper reports our experience in applying the DiVA model-driven adaptive approach to a robotics case study, describing its bene ts and limitations for robotics.This work has been partially funded by the EXPLORE project (Spanish MICINN,
TIN2009-08572,http://www.dsie.upct.es/proyectos/web_explore/) and the DiVA
project (EU FP7 STREP, contract 215412, http://www.ict-diva.eu/
A Software Suite for the Control and the Monitoring of Adaptive Robotic Ecologies
Adaptive robotic ecologies are networks of heterogeneous robotic devices (sensors, actuators, automated appliances) pervasively embedded in everyday environments, where they learn to cooperate towards the achievement of complex tasks. While their flexibility makes them an increasingly popular way to improve a system’s reliability, scalability, robustness and autonomy, their effective realisation demands integrated control and software solutions for the specification, integration and management of their highly heterogeneous and computational constrained components. In this extended abstract we briefly illustrate the characteristic requirements dictated by robotic ecologies, discuss our experience in developing adaptive robotic ecologies, and provide an overview of the specific solutions developed as part of the EU FP7 RUBICON Project
AMon: A domain-specific language and framework for adaptive monitoring of Cyber–Physical Systems
Cyber–Physical Systems (CPS) are increasingly used in safety–critical scenarios where ensuring their correct behavior at runtime becomes a crucial task. Therefore, the behavior of the CPS needs to be monitored at runtime so that violations of requirements can be detected. With the inception of edge devices that facilitate runtime analysis at the edge and the increasingly diverse environments that CPS operate in, flexible monitoring approaches are needed that consider the data that needs to be monitored and the analyses performed on that data. In this paper, we propose AMon, a flexible adaptive monitoring framework that supports the specification and validation of monitoring adaptation rules, using a domain-specific language. Based on these rules, AMon automatically generates code for direct deployment onto devices. We evaluated AMon by applying it to TurtleBot Robots and a fleet of Unmanned Aerial Vehicles. Furthermore, we conducted a user study assessing the understandability and ease of use of our language. Results show that creating multiple adaptation rules with our DSL is feasible with minimal effort, and that adaptive monitoring can reduce the amount of runtime data transmitted from the edge device according to the current state of the system and its monitoring needs
RoboPlanner: Towards an Autonomous Robotic Action Planning Framework for Industry 4.0
Autonomous robots are being increasingly integrated into manufacturing, supply chain and retail industries due to the twin advantages of improved throughput and adaptivity. In order to handle complex Industry 4.0 tasks, the autonomous robots require robust action plans, that can self-adapt to runtime changes. A further requirement is efficient implementation of knowledge bases, that may be queried during planning and execution. In this paper, we propose RoboPlanner, a framework to generate action plans in autonomous robots. In RoboPlanner, we model the knowledge of world models, robotic capabilities and task templates using knowledge property graphs and graph databases. Design time queries and robotic perception are used to enable intelligent action planning. At runtime, integrity constraints on world model observations are used to update knowledge bases. We demonstrate these solutions on autonomous picker robots deployed in Industry 4.0 warehouses
Comparison of approaches for self-improvement in self-adaptive systems (extended version)
Various trends such as mobility of devices, Cloud Computing, or Cyber-Physical Systems lead to a higher degree of distribution. These systems-of-systems need to be integrated.
The integration of various subsystems still remains a challenge. Self-improvement within self-adaptive systems can help to shift
integration tasks from the static design time to the runtime, which fits the dynamic needs of these systems. Thus, it can enable the
integration of system parts at runtime.
In this paper, we define self-improvement as an adaptation of an Autonomic Computing system’s adaptation logic. We present an overview of approaches for self-improvement in the domains
of Autonomic Computing and self-adaptive systems. Based on a taxonomy for self-adaptation, we compare the approaches and categorize them. The categorization shows that the approaches
focus either on structural or parameter adaptation but seldomly combine both. Based on the categorization, we elaborate challenges, that need to be addressed by future approaches for
offering self-improving system integration at runtime
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