ORCA – Towards an Organic Robotic Control Architecture

Mösch, Florian; Litza, Marek; Auf, Adam El Sayed; Maehle, Erik; Grosspietsch, K.E.; Brockmann, Werner

doi:10.1007/11822035_24

Cited by 14 publications

(4 citation statements)

References 3 publications

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“…This afterwards dramatically speeds up the exploration process, because it can be done in simulating the derived model and does not have to be done in reality. With ORCA [18] Mösch et al show how a spider-like robot is able to reorganize its legs movements decentralized after one leg has been amputated. Although these working examples show the feasibility of this approach for usage in real-world scenarios, these attempts up to now ignore the society a robot belongs to.…”

Section: Related Workmentioning

confidence: 99%

Adaptivity at every layer

Richert

Kleinjohann

2008

Proceedings of the 2008 International Workshop on Software Engineering for Adaptive and Self-Managing Systems

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We describe a developmental architecture that enables individual robots to fulfill tasks assigned to the robot society in a robust, decentralized manner. The architecture is meant to show emergent properties according to Organic Computing principles that are positive for the society's robustness and performance. This requires the architecture to feature those adaptation and learning processes that are not only selfishly useful for the individual robot, but also incorporate the robot society's actual needs at all layers.

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Section: Related Workmentioning

confidence: 99%

Adaptivity at every layer

Richert

Kleinjohann

2008

Proceedings of the 2008 International Workshop on Software Engineering for Adaptive and Self-Managing Systems

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“…Taking the heterogeneity of resources into account, Component-Based [5][6][7][8] robotic system (CBRS) approach provides a solution that shifts the emphasis from hardware-specific software programming to composing application systems from a mixture of components. Nowadays, middleware-based component platforms [8,9], such as ORCA [10], Miro [11], RT-Middleware [12], OPRoS [13], etc., have been introduced in a wide literature.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-enabled decision making for robotic system utilizing ambient service components

Qian

Duan

et al. 2014

Journal of Ambient Intelligence and Smart Environments

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Robotic and ambient intelligent environments are much more demanding regarding the knowledge a robot needs to have. A skilled robot performs human-scale manipulation tasks, interacts with a variety of objects, understands instructions given by humans and most importantly, requires the capability of interpreting ubiquitous resources and assembling them into a complex plan. In this paper, a novel Knowledge-enable Decision Making Framework (KDMF) is proposed for Component-Based Robotic System (CBRS). We exploit that the use of domain knowledge is pivotal to endow robots with higher degrees of autonomy and intelligence in CBRS. Ontology knowledge about classes, properties, relations is organized in OWL based conceptual map, which allows automated inference to derive new pieces of information. Knowledge about tasks is specified in a tree data structure, and knowledge about components' functions is formulated by a specific type of service specification profile. Using the knowledge representation, a task-oriented decision making method is proposed that integrates knowledge inference and service components utilization. In practical applications of ambient and robotic assisted living, robot's plans generated by the decision making software are based on the knowledge of components, rather than particular device instances, which improves the reusability and flexibility of the system. Experimental results validate the effectiveness of the proposed method.

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“…With respect to robotics, OC research initially focused on failure tolerant and robust hardware concepts, mainly applied to multi-legged walking machines. The most prominent example is the Organic Robot Control Architecture (ORCA), see Brockmann et al (2005) and Mösch et al (2006). In ORCA, two kinds of behavioral modules are discerned.…”

mentioning

confidence: 99%

An Organic Computing Approach to Self-Organizing Robot Ensembles

2016

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Similar to the Autonomous Computing initiative, which has mainly been advancing techniques for self-optimization focusing on computing systems and infrastructures, Organic Computing (OC) has been driving the development of system design concepts and algorithms for self-adaptive systems at large. Examples of application domains include, for instance, traffic management and control, cloud services, communication protocols, and robotic systems. Such an OC system typically consists of a potentially large set of autonomous and self-managed entities, where each entity acts with a local decision horizon. By means of cooperation of the individual entities, the behavior of the entire ensemble system is derived. In this article, we present our work on how autonomous, adaptive robot ensembles can benefit from OC technology. Our elaborations are aligned with the different layers of an observer/controller framework, which provides the foundation for the individuals' adaptivity at system design-level. Relying on an extended Learning Classifier System (XCS) in combination with adequate simulation techniques, this basic system design empowers robot individuals to improve their individual and collaborative performances, e.g., by means of adapting to changing goals and conditions. Not only for the sake of generalizability but also because of its enormous transformative potential, we stage our research in the domain of robot ensembles that are typically comprised of several quad-rotors and that organize themselves to fulfill spatial tasks such as maintenance of building facades or the collaborative search for mobile targets. Our elaborations detail the architectural concept, provide examples of individual self-optimization as well as of the optimization of collaborative efforts, and we show how the user can control the ensembles at multiple levels of abstraction. We conclude with a summary of our approach and an outlook on possible future steps.

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ORCA – Towards an Organic Robotic Control Architecture

Cited by 14 publications

References 3 publications

Adaptivity at every layer

Adaptivity at every layer

Knowledge-enabled decision making for robotic system utilizing ambient service components

An Organic Computing Approach to Self-Organizing Robot Ensembles

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