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Methods and Applications of Informatics and Information Technology

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Volume 61, Issue 4

Issues

Swarm robotics: Robustness, scalability, and self-X features in industrial applications

Mary Katherine Heinrich / Mohammad Divband Soorati / Tanja Katharina Kaiser / Mostafa Wahby / Heiko HamannORCID iD: https://orcid.org/0000-0002-2458-8289
Published Online: 2019-10-30 | DOI: https://doi.org/10.1515/itit-2019-0003

Abstract

Applying principles of swarm intelligence to the control of autonomous systems in industry can advance our ability to manage complexity in prominent and high-cost sectors—such as transportation, logistics, and construction. In swarm robotics, the exclusive use of decentralized control relying on local communication and information provides the key advantage first of scalability, and second of robustness against failure points. These are directly useful in certain applied tasks that can be studied in laboratory environments, such as self-assembly and self-organized construction. In this article, we give a brief introduction to swarm robotics for a broad audience, with the intention of targeting future industrial applications. We then present a summary of four examples of our recently published research results with simple models. First, we present our approach to self-reconfiguration, which uses collective adjustment of swarm density in a dynamic setting. Second, we describe our robot experiments for self-organized material deployment in structured and semi-structured environments, applicable to braided composites. Third, we present our machine learning approach for self-assembly, motivated as a simple model developing foundational methods, which generates self-organizing robot behaviors to form emergent patterns. Fourth, we describe our experiments implementing a bioinspired model in a robot swarm, where we show self-healing of damage as the robots collectively locate a resource. Overall, the four examples we present concern robustness, scalability, and self-X features, which we propose as potentially relevant to future research in swarm robotics applied to industry sectors. We summarize these approaches as an introduction to our recent research, targeting the broad audience of this journal.

Keywords: swarm robotics; swarm intelligence; robustness; scalability; adaptivity; self-X

ACM CCS: Computer systems organizationRoboticsComputer systems organizationEvolutionary roboticsComputer systems organizationSelf-organizing autonomic computing

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About the article

Mary Katherine Heinrich

Mary Katherine Heinrich, at the time of writing, was a scientific research associate at the Institute of Computer Engineering, University of Lübeck, Germany. Currently, Heinrich is an F.R.S.-FNRS Postdoctoral Researcher on swarm intelligence and swarm robotics, under Prof. Marco Dorigo, at IRIDIA, the artificial intelligence lab of the Université Libre de Bruxelles, Belgium. Heinrich holds a Ph.D. on the topic of self-organization and self-adaptation in information technology and architecture, from the Centre for IT and Architecture (CITA), in Copenhagen, Denmark, and is an external affiliate of the New England Complex Systems Institute (NECSI), in Cambridge, MA, USA.

Mohammad Divband Soorati

Mohammad Divband Soorati is a PhD candidate at University of Lübeck, Germany. He is also a senior research assistant at University of Southampton, UK. He holds a MSc degree in Computer Science from Paderborn University, Germany and a BSc degree in Software Engineering from Iran University of Science and Technology. His research is mainly focused on adaptive intelligent systems including swarm, evolutionary, and bio-inspired robotics.

Tanja Katharina Kaiser

Tanja Katharina Kaiser is a doctoral student at the University of Lübeck, Germany. She received a B.Sc. degree in computer science from the Baden-Wuerttemberg Cooperative State University Stuttgart, Germany in 2014 and a M.Sc. degree in computer science from the Technical University of Berlin, Germany in 2017. Her research interests are swarm robotics and evolutionary robotics.

Mostafa Wahby

Mostafa Wahby, at the time of writing, was a scientific research associate at the Institute of Computer Engineering, University of Lübeck, Germany. Currently, he is a postdoctoral researcher at IRIDIA, Universite’ Libre de Bruxelles, where he investigates applying temporary self-organized central control in robot swarms to improve their performance. He has received his Ph.D. in computer science from the University of Lübeck during his work on shaping natural plants using robot swarms. Wahby is mainly interested in machine learning and swarm intelligence research topics.

Heiko Hamann

Since 2017 Heiko Hamann is professor for service robotics at the University of Lübeck, Germany. He coordinated the EU-funded project flora robotica that develops and investigates closely linked symbiotic relationships between robots and natural plants to explore the potentials of a plant-robot society able to produce architectural artifacts and living spaces. His main research interests are swarm intelligence, swarm robotics, evolutionary robotics, applications of evolutionary computation in software engineering, and modeling of complex systems. He was assistant professor of swarm robotics at the University of Paderborn, Germany from 2013 until 2017 and did his postdoctoral training in swarm robotics, modular robotics, and evolutionary robotics at the Zoology department of the University of Graz, Austria. He received his doctorate in engineering from the University of Karlsruhe, Germany in 2008.


Received: 2019-01-18

Revised: 2019-09-26

Accepted: 2019-10-10

Published Online: 2019-10-30

Published in Print: 2019-08-27


Funding Source: H2020 Future and Emerging Technologies

Award identifier / Grant number: 640959

The authors acknowledge partial funding from the European Union’s Horizon 2020 research and innovation program under the FET grant agreement ‘flora robotica’, no. 640959.


Author contributions: Mary Katherine Heinrich, Mohammad Divband Soorati, Tanja Katharina Kaiser and Mostafa Wahby contributed equally.


Citation Information: it - Information Technology, Volume 61, Issue 4, Pages 159–167, ISSN (Online) 2196-7032, ISSN (Print) 1611-2776, DOI: https://doi.org/10.1515/itit-2019-0003.

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