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.
Funding source: H2020 Future and Emerging Technologies
Award Identifier / Grant number: 640959
Funding statement: 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.
About the authors
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 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 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, 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.
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.
Author contributions: Mary Katherine Heinrich, Mohammad Divband Soorati, Tanja Katharina Kaiser and Mostafa Wahby contributed equally.
1. Carl Anderson, Guy Theraulaz, and Jean-Louis Deneubourg. Self-assemblages in insect societies. insectes sociaux. Insectes Sociaux, 49 (2): 99–110, 2002.10.1007/s00040-002-8286-ySearch in Google Scholar
2. Yaneer Bar-Yam. Unifying principles in complex systems. In M. C. Roco and W. S. Bainbridge, editors, Converging Technology (NBIC) for Improving Human Performance, Kluwer, 2003.Search in Google Scholar
3. Eric Bonabeau, Marco Dorigo, and Guy Theraulaz. Swarm Intelligence: From Natural to Artificial Systems, Oxford Univ. Press, New York, NY, 1999.10.1093/oso/9780195131581.001.0001Search in Google Scholar
4. Richard Borkowski and Heiko Hamann. Evolving robot swarm behaviors by minimizing surprise: Results of simulations in 2-d on a torus. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO ’17, pages 1679–1680, ACM, New York, NY, USA, 2017. ISBN 978-1-4503-4939-0. 10.1145/3067695.3082548.Search in Google Scholar
5. Manuele Brambilla, Eliseo Ferrante, Mauro Birattari, and Marco Dorigo. Swarm robotics: a review from the swarm engineering perspective. Swarm Intelligence, 7 (1): 1–41, 2013. ISSN 1935-3812. 10.1007/s11721-012-0075-2.Search in Google Scholar
6. N. Brooks. Delivering European megaprojects: a guide for policy makers and practitioners, University of Leeds, Leeds, 2015.Search in Google Scholar
7. Jim Cockrell, Richard Alena, David Mayer, Hugo Sanchez, Tom Luzod, Bruce Yost, and D. Klumpar. EDSN: A large swarm of advanced yet very affordable, COTS-based nanosats that enable multipoint physics and open source apps. In Proc. of 26th Annual AIAA/USU Conference on Small Satellites, 2012.Search in Google Scholar
8. Antoine Cully, Jeff Clune, Danesh Tarapore, and Jean-Baptiste Mouret. Robots that can adapt like animals. Nature, 521 (7553): 503, 2015.10.1038/nature14422Search in Google Scholar PubMed
9. Mohammad Divband Soorati, Javad Ghofrani, Payam Zahadat, and Heiko Hamann. Robust and adaptive robot self-assembly based on vascular morphogenesis. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4282–4287, Oct 2018. 10.1109/IROS.2018.8594093.Search in Google Scholar
10. Mohammad Divband Soorati, Mary Katherine Heinrich, Javad Ghofrani, Payam Zahadat, and Heiko Hamann. Photomorphogenesis for robot self-assembly: Adaptivity, collective decision-making, and self-repair. Bioinspiration & biomimetics, 14 (5): 056006, 2019.Search in Google Scholar
11. Mohammad Divband Soorati, Payam Zahadat, Javad Ghofrani, and Heiko Hamann. Adaptive path formation in self-assembling robot swarms by tree-like vascular morphogenesis. In Nikolaus Correll, Mac Schwager, and Michael Otte, editors, The 14th International Symposium on Distributed Autonomous Robotic Systems (DARS), Springer International Publishing, Cham, 2019.10.1007/978-3-030-05816-6_21Search in Google Scholar
12. Catriona Eschke, Mary Katherine Heinrich, et al. Self-organized adaptive paths in multi-robot manufacturing: reconfigurable and pattern-independent fibre deployment. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2019.10.1109/IROS40897.2019.8967951Search in Google Scholar
13. Jobst Fiedler and Alexander Wendler. Berlin Brandenburg Airport. In Large Infrastructure Projects in Germany, pages 87–145. Springer, 2016.10.1007/978-3-319-29233-5_4Search in Google Scholar
14. Karl Friston. The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11 (2): 127–138, 2010.10.1038/nrn2787Search in Google Scholar PubMed
15. Victor Gerling and Sebastian Von Mammen. Robotics for self-organised construction. In IEEE International Workshops on Foundations and Applications of Self* Systems, pages 162–167, IEEE, 2016.10.1109/FAS-W.2016.45Search in Google Scholar
16. Dani Goldberg and Maja J. Matarić. Interference as a tool for designing and evaluating multi-robot controllers. In Benjamin J. Kuipers and Bonnie Webber, editors, Proc. of the Fourteenth National Conference on Artificial Intelligence (AAAI’97), pages 637–642, MIT Press, Cambridge, MA, 1997.Search in Google Scholar
17. Heiko Hamann. Towards swarm calculus: Urn models of collective decisions and universal properties of swarm performance. Swarm Intelligence, 7 (2–3): 145–172, 2013. URL http://dx.doi.org/10.1007/s11721-013-0080-0.10.1007/s11721-013-0080-0Search in Google Scholar
18. Heiko Hamann. Evolution of collective behaviors by minimizing surprise. In Hiroki Sayama, John Rieffel, Sebastian Risi, René Doursat, and Hod Lipson, editors, 14th Int. Conf. on the Synthesis and Simulation of Living Systems (ALIFE 2014), pages 344–351, MIT Press, 2014. URL http://dx.doi.org/10.7551/978-0-262-32621-6-ch055.10.7551/978-0-262-32621-6-ch055Search in Google Scholar
19. Heiko Hamann. Superlinear scalability in parallel computing and multi-robot systems: Shared resources, collaboration, and network topology. In Mladen Berekovic, Rainer Buchty, Heiko Hamann, Dirk Koch, and Thilo Pionteck, editors, Architecture of Computing Systems – ARCS 2018, pages 31–42, Springer International Publishing, Cham, 2018. ISBN 978-3-319-77610-1.10.1007/978-3-319-77610-1_3Search in Google Scholar
20. Heiko Hamann. Swarm Robotics: A Formal Approach, Springer, 2018.10.1007/978-3-319-74528-2Search in Google Scholar
21. Andrew A. Head and Victor M. Ivers. Rapidly configurable braiding machine, December 29 2015. US Patent 9,222,205.Search in Google Scholar
22. Mary Katherine Heinrich and Phil Ayres. Using the phase space to design complexity: Design methodology for distributed control of architectural robotic elements. In Complexity & Simplicity, Proc. of the 34th eCAADe Conference, volume 1, pages 413–422, eCAADe, 2016.10.52842/conf.ecaade.2016.1.413Search in Google Scholar
23. Mary Katherine Heinrich, Mostafa Wahby, Mohammad Divband Soorati, Daniel Nicolas Hofstadler, Payam Zahadat, Phil Ayres, Kasper Støy, and Heiko Hamann. Self-organized construction with continuous building material: higher flexibility based on braided structures. In Foundations and Applications of Self* Systems, IEEE International Workshops on, pages 154–159, IEEE, 2016.10.1109/FAS-W.2016.43Search in Google Scholar
24. Mary Katherine Heinrich, Payam Zahadat, John Harding, et al. Using interactive evolution to design behaviors for non-deterministic self-organized construction. In Proceedings of the Symposium on Simulation for Architecture and Urban Design, page 21, ACM, and Society for Computer Simulation (SCS) International, 2018.Search in Google Scholar
25. Tanja Katharina Kaiser and Heiko Hamann. Self-assembly in patterns with minimal surprise: Engineered self-organization and adaptation to the environment. In Nikolaus Correll, Mac Schwager, and Michael Otte, editors, The 14th International Symposium Distributed Autonomous Robotic Systems (DARS), Springer International Publishing, 2019.Search in Google Scholar
26. Serge Kernbach, Ronald Thenius, Olga Kernbach, and Thomas Schmickl. Re-embodiment of honeybee aggregation behavior in an artificial micro-robotic system. Adaptive Behavior, 17 (3): 237–259, 2009.10.1177/1059712309104966Search in Google Scholar
27. Serge Kernbach, Dagmar Häbe, Olga Kernbach, Ronald Thenius, Gerald Radspieler, Toshifumi Kimura, and Thomas Schmickl. Adaptive collective decision-making in limited robot swarms without communication. The International Journal of Robotics Research, 32 (1): 35–55, 2013.10.1177/0278364912468636Search in Google Scholar
28. Mark Klein, Hiroki Sayama, Peyman Faratin, and Yaneer Bar-Yam. The dynamics of collaborative design: Insights from complex systems and negotiation research. In Complex Engineered Systems, pages 158–174. Springer, 2006.10.1007/3-540-32834-3_8Search in Google Scholar
29. Riccardo La Magna, Frederic Waimer, and Jan Knippers. Coreless winding—a novel fabrication approach for frp based components in building construction. In Proceedings of the international conference on FRP composites in civil engineering, Vancouver, Canada, 2014.Search in Google Scholar
30. Kristina Lerman and Aram Galstyan. Mathematical model of foraging in a group of robots: Effect of interference. Autonomous Robots, 13 (2): 127–141, 2002.10.1023/A:1019633424543Search in Google Scholar
31. Ronald F. McConnell and Peter Popper. Complex shaped braided structures, January 19 1988. US Patent 4,719,837.Search in Google Scholar
32. Sandra Mišić and Mladen Radujković. Critical drivers of megaprojects success and failure. Procedia Engineering, 122: 71–80, 2015.10.1016/j.proeng.2015.10.009Search in Google Scholar
33. Francesco Mondada, Michael Bonani, Fanny Riedo, Manon Briod, Léa Pereyre, Philippe Rétornaz, and Stéphane Magnenat. The Thymio open-source hardware robot. IEEE Robotics & Automation Magazine, 1070 (9932/17): 2, 2017.Search in Google Scholar
34. Christian Müller-Schloer and Sven Tomforde. Organic Computing–Technical Systems for Survival in the Real World, Springer, 2017.10.1007/978-3-319-68477-2Search in Google Scholar
35. Rehan O’Grady, Anders Lyhne Christensen, Carlo Pinciroli, and Marco Dorigo. Robots autonomously self-assemble into dedicated morphologies to solve different tasks. In 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, Canada, May 10–14, 2010, Volume 1–3, pages 1517–1518, 2010. 10.1145/1838206.1838459.Search in Google Scholar
36. Charles W. Rogers and Steven R. Crist. Braided preform for composite bodies, April 15 1997. US Patent 5,619,903.Search in Google Scholar
37. Michael Rubenstein, Alejandro Cornejo, and Radhika Nagpal. Programmable self-assembly in a thousand-robot swarm. Science, 345 (6198): 795–799, 2014. 10.1126/science.1254295.Search in Google Scholar PubMed
38. Shaina Saporta, Frances Yang, and Matthew Clark. Design and delivery of structural material innovations. In Structures Congress 2015, pages 1253–1265, 2015.10.1061/9780784479117.107Search in Google Scholar
39. Thomas Schmickl and Heiko Hamann. BEECLUST: A swarm algorithm derived from honeybees. In Yang Xiao, editor, Bio-inspired Computing and Communication Networks, pages 95–137, CRC Press, Boca Raton, FL, USA, March 2011.Search in Google Scholar
40. Thomas Schmickl, Christoph Möslinger, and Karl Crailsheim. Collective perception in a robot swarm. In Erol Şahin, William M. Spears, and Alan F. T. Winfield, editors, Swarm Robotics – Second SAB 2006 International Workshop, volume 4433 of LNCS, Springer, Heidelberg/Berlin, Germany, 2007.Search in Google Scholar
41. Thomas Schmickl, Ronald Thenius, Christoph Möslinger, Gerald Radspieler, Serge Kernbach, and Karl Crailsheim. Get in touch: Cooperative decision making based on robot-to-robot collisions. Autonomous Agents and Multi-Agent Systems, 18 (1): 133–155, February 2008.10.1007/s10458-008-9058-5Search in Google Scholar
42. Cyril Duncan Sculthorpe. Biology of aquatic vascular plants, St. Martin’s Press, 1967.Search in Google Scholar
43. Michael F. Smith. Apparatus and method for automated braiding of square rope and rope product produced thereby, February 14 1989. US Patent 4,803,909.Search in Google Scholar
44. Mohammad Divband Soorati and Heiko Hamann. The effect of fitness function design on performance in evolutionary robotics: The influence of a priori knowledge. In Genetic and evolutionary computation conference (GECCO 2015), pages 153–160. ACM, 2015.10.1145/2739480.2754676Search in Google Scholar
45. Vito Trianni. Evolutionary Swarm Robotics – Evolving Self-Organising Behaviours in Groups of Autonomous Robots, volume 108 of Studies in Computational Intelligence, Springer, Berlin, Germany, 2008.Search in Google Scholar
46. Elio Tuci, Roderich Groß, Vito Trianni, Francesco Mondada, Michael Bonani, and Marco Dorigo. Cooperation through self-assembly in multi-robot systems. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 1 (2): 115–150, 2006.10.1145/1186778.1186779Search in Google Scholar
47. Gabriele Valentini, Davide Brambilla, Heiko Hamann, and Marco Dorigo. Collective perception of environmental features in a robot swarm. In International Conference on Swarm Intelligence, pages 65–76, Springer, 2016.10.1007/978-3-319-44427-7_6Search in Google Scholar
48. Mostsafa Wahby, Julian Petzold, Catriona Eschke, Thomas Schmickl, and Heiko Hamann. Collective Change Detection: Adaptivity to Dynamic Swarm Densities and Light Conditions in Robot Swarms. In The 2019 Conference on Artificial Life, IEEE, 2019. 10.1162/isal_a_00233.Search in Google Scholar
49. Barbara Webb and Thomas Consilvio, editors. Biorobotics. MIT Press, Cambridge, MA, USA, 2001. ISBN 026273141X.Search in Google Scholar
50. Justin Werfel, Yaneer Bar-Yam, Daniela Rus, and Radhika Nagpal. Distributed construction by mobile robots with enhanced building blocks. In Proceedings IEEE International Conference on Robotics and Automation (ICRA), pages 2787–2794, IEEE, 2006.Search in Google Scholar
51. Justin Werfel, Kirstin Petersen, and Radhika Nagpal. Designing collective behavior in a termite-inspired robot construction team. Science, 343 (6172): 754–758, 2014. 10.1126/science.1245842.Search in Google Scholar PubMed
52. Peter R. Wurman, Raffaello D’Andrea, and Mick Mountz. Coordinating hundreds of cooperative, autonomous vehicles in warehouses. AI magazine, 29 (1): 9, 2008.Search in Google Scholar
53. Payam Zahadat, Heiko Hamann, and Thomas Schmickl. Evolving diverse collective behaviors independent of swarm density. In Workshop Evolving Collective Behaviors in Robotics (GECCO 2015), pages 1245–1246, ACM, 2015.10.1145/2739482.2768492Search in Google Scholar
54. Payam Zahadat, Daniel Nicolas Hofstadler, and Thomas Schmickl. Vascular morphogenesis controller: A generative model for developing morphology of artificial structures. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pages 163–170, ACM, New York, NY, USA, 2017. ISBN 978-1-4503-4920-8. 10.1145/3071178.3071247.Search in Google Scholar
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