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2018 Volume 33
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ORIGINAL RESEARCH   Open Access    

Towards adaptive multi-robot systems: self-organization and self-adaptation

  • DAI-Labor

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  • Abstract: 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.
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  • Ali S. M., Zimmer R. M., Elstob C. M. & Dubois D. M. 1998. The question concerning emergence: implications for artificiality. AIP Conference Proceedings 437(1), 138–156.

    Google Scholar

    Allgeuer P. & Behnke S. 2013. Hierarchical and state-based architectures for robot behavior planning and control. In Proceedings of 8th Workshop on Humanoid Soccer Robots, IEEE-RAS International Conference on Humanoid Robots.

    Google Scholar

    Ashley-Rollman M., Goldstein S., Lee P., Mowry T. & Pillai P. 2007. Meld: a declarative approach to programming ensembles. In IEEE/RSJ International Conference on Intelligent Robots and Systems,IROS, 2794–2800.

    Google Scholar

    Ay N., Der R. & Prokopenko M. 2012. Guided self-organization: perception-action loops of embodied systems. Theory in Biosciences 131(3), 125–127.

    Google Scholar

    Bachrach J., McLurkin J. & Grue A. 2008. Protoswarm: A language for programming multi-robot systems using the amorphous medium abstraction. In Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3, 1175–1178. AAMAS ’08. International Foundation for Autonomous Agents and Multiagent Systems.

    Google Scholar

    Balch T. & Arkin R. C. 1998. Behavior-based formation control for multirobot teams. IEEE Transactions on Robotics and Automation 14(6), 926–939.

    Google Scholar

    Bashyal S. & Venayagamoorthy G. 2008. Human swarm interaction for radiation source search and localization. In IEEE Swarm Intelligence Symposium, 2008, 1–8. SIS 2008.

    Google Scholar

    Beal J. & Bachrach J. 2006. Infrastructure for engineered emergence on sensor/actuator networks. IEEE Intelligent Systems 21(2), 10–19.

    Google Scholar

    Blum A. L. & Furst M. L. 1997. Fast planning through planning graph analysis. Artificial Intelligence 90(1–2), 281–300.

    Google Scholar

    Bohren J. & Cousins S. 2010. The SMACH high-level executive [ros news]. Robotics Automation Magazine, IEEE 17(4), 18–20.

    Google Scholar

    Bonabeau E., Dorigo M. & Theraulaz G. 1999. Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press Inc.

    Google Scholar

    Bonet B. & Geffner H. 2001. Heuristic search planner 2.0. AI Magazine 22(3), 77.

    Google Scholar

    Bonjean N., Mefteh W., Gleizes M. P., Maurel C. & Migeon F. 2014. Adelfe 2.0. In Handbook on Agent-Oriented Design Processes, Cossentino, M., Hilaire, V., Molesini, A. & Seidita, V. (eds). Springer, 19–63.

    Google Scholar

    Brambilla M., Ferrante E., Birattari M. & Dorigo M. 2013. Swarm robotics: a review from the swarm engineering perspective. Swarm Intelligence 7(1), 1–41.

    Google Scholar

    Brambilla M., Pinciroli C., Birattari M. & Dorigo M. 2012. Property-driven design for swarm robotics. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems-Volume 1, 139–146.

    Google Scholar

    Branke J., Mnif M., Muller-Schloer C. & Prothmann H. 2006. Organic computing – addressing complexity by controlled self-organization. In 2th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation ISoLA, 185–191. IEEE.

    Google Scholar

    Bresciani P., Perini A., Giorgini P., Giunchiglia F. & Mylopoulos J. 2004. Tropos: an agent-oriented software development methodology. Autonomous Agents and Multi-Agent Systems 8(3), 203–236.

    Google Scholar

    Breuer T., Giorgana Macedo G. R., Hartanto R., Hochgeschwender N., Holz D., Hegger F., Jin Z., Müller C., Paulus J., Reckhaus M., Álvarez Ruiz J. A., Plöger P. G. & Kraetzschmar G. K. 2012. Johnny: an autonomous service robot for domestic environments. Journal of Intelligent & Robotic Systems 66(1), 245–272.

    Google Scholar

    Brooks R. A. 1986. A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation 2(1), 14–23.

    Google Scholar

    Brun Y., Serugendo G. D. M., Gacek C., Giese H., Kienle H., Litoiu M., Müller H, Pezzè M. & Shaw M. 2009. Engineering self-adaptive systems through feedback loops. In Software Engineering for Self-Adaptive Systems, 48–70. Springer.

    Google Scholar

    Camazine S., Deneubourg J.-L., Franks N. R., Sneyd J., Theraulaz G. & Bonabeau E. 2003. Self-Organization in Biological Systems. Princeton University Press.

    Google Scholar

    CogniTeam Ltd 2015. Cognitao (think as one). http://www.cogniteam.com/cognitao.html.

    Google Scholar

    Cui Y., Voyles R., Lane J. & Mahoor M. 2014. ReFrESH: a self-adaptation framework to support fault tolerance in field mobile robots. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1576–1582.

    Google Scholar

    Das R., Crutchfield J. P., Mitchell M. & Hanson James E. 1995. Evolving globally synchronized cellular automata. In Proceedings of the 6th International Conference on Genetic Algorithms, 336–343. Morgan Kaufmann Publishers Inc.

    Google Scholar

    De Nicola R., Ferrari G., Loreti M. & Pugliese R. 2013. A language-based approach to autonomic computing. In Formal Methods for Components and Objects, Beckert, B., Damiani, F., de Boer, F. & Bonsangue, M., (eds), Lecture Notes in Computer Science, 7542. Springer, 25–48.

    Google Scholar

    De Rosa M., Goldstein S., Lee P., Pillai P. & Campbell J. 2008. Programming modular robots with locally distributed predicates. In IEEE International Conference on Robotics and Automation (ICRA), 3156–3162.

    Google Scholar

    De Wolf T. & Holvoet T. 2005. Emergence versus self-organisation: different concepts but promising when combined. In Engineering Self-Organising Systems, Brueckner, S. A., Marzo Serugendo, G., Karageorgos, A. & Nagpal, R. (eds). Springer-Verlag, 1–15.

    Google Scholar

    De Wolf T. & Holvoet T. 2007. Design patterns for decentralised coordination in self-organising emergent systems. In Engineering Self-Organising Systems, 28–49. Springer.

    Google Scholar

    De Wolf T., Samaey G. & Holvoet T. 2005. Engineering self-organising emergent systems with simulation-based scientific analysis. In 4th International Workshop on Engineering Self-Organising Applications, 146–160.

    Google Scholar

    Deneubourg J. L., Goss S., Franks N., Sendova-Franks A., Detrain C. & Chrétien L. 1990. The dynamics of collective sorting robot-like ants and ant-like robots. In Proceedings of the First International Conference on Simulation of Adaptive Behavior on From Animals to Animats, 356–363. MIT Press.

    Google Scholar

    Desai J. P., Ostrowski J. P. & Kumar V. 2001. Modeling and control of formations of nonholonomic mobile robots. IEEE Transactions on Robotics and Automation 17(6), 905–908.

    Google Scholar

    Dorigo M., Floreano D., Gambardella L., Mondada F., Nolfi S., Baaboura T., Birattari M., Bonani M., Brambilla M., Brutschy A., Burnier D., Campo A., Christensen A., Decugniere A., Di Caro G., Ducatelle F., Ferrante E., Forster A., Martinez Gonzales J., Guzzi J., Longchamp V., Magnenat S., Mathews N., Montes de Oca M, O’Grady R., Pinciroli C., Pini G., Retornaz P., Roberts J., Sperati V., Stirling T., Stranieri A., Stutzle T., Trianni V., Tuci E., Turgut A. & Vaussard F. 2013. Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Robotics Automation Magazine 20(4), 60–71.

    Google Scholar

    Dorigo M., Trianni V., Şahin E., Groβ R., Labella T. H, Baldassarre G., Nolfi S., Deneubourg J.-L., Mondada F., Floreano D. & Gambardella L. M. 2004. Evolving self-organizing behaviors for a swarm-bot. Autonomous Robots 17(2–3), 223–245.

    Google Scholar

    Ducatelle F., Di Caro G. A. & Gambardella L. M. 2010. Cooperative self-organization in a heterogeneous swarm robotic system. In 12th Annual Conference on Genetic and Evolutionary Computation, GECCO’10, 87–94. ACM.

    Google Scholar

    Edmonds B. 2005. Using the experimental method to produce reliable self-organised systems. In Brueckner, S. A, Di Marzo Serugendo, G., Karageorgos, A. & Nagpal, R. (eds) Engineering Self-Organising Systems: Methodologies and Applications. Springer, 84–99.

    Google Scholar

    Edmonds B. & Bryson J. J. 2004. The insufficiency of formal design methods “ The Necessity of an Experimental Approach - for the Understanding and Control of Complex MAS”. In 3th International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2, AAMAS ’04, 938–945. IEEE Computer Society.

    Google Scholar

    Fernandez-Marquez J. L., Serugendo G. D. M. & Montagna S. 2011. Bio-core: bio-inspired self-organising mechanisms core. In International Conference on Bio-Inspired Models of Network, Information, and Computing Systems, 59–72. Springer.

    Google Scholar

    Fernandez-Marquez J. L., Serugendo G. D. M., Montagna S., Viroli M. & Arcos J. L. 2012. Description and composition of bio-inspired design patterns: a complete overview. Natural Computing 12(1), 43–67.

    Google Scholar

    Fikes R. E. & Nilsson N. J. 1972. STRIPS: a new approach to the application of theorem proving to problem solving. Artificial Intelligence 2(3), 189–208.

    Google Scholar

    Francesca G., Brambilla M., Brutschy A., Garattoni L., Miletitch R., Podevijn G., Reina A., Soleymani T., Salvaro M., Pinciroli C., Mascia F., Trianni V. & Birattari M. 2015. AutoMoDe-Chocolate: automatic design of control software for robot swarms. Swarm Intelligence 9(2–3), 125–152.

    Google Scholar

    Garlan D., Cheng S.-W., Huang A.-C., Schmerl B. & Steenkiste P. 2004. Rainbow: architecture-based self-adaptation with reusable infrastructure. Computer 37(10), 46–54.

    Google Scholar

    Gershenson C. 2007. Design and control of self-organizing systems. PhD thesis, Vrije Universiteit Brussel.

    Google Scholar

    Goebel P. 2013. ROS By Example. Lulu.

    Google Scholar

    Goldstein J. 1999. Emergence as a construct: history and issues. Emergence 1(1), 49–72.

    Google Scholar

    Goodrich M. A., Pendleton B., Sujit P. B. & Pinto J. 2011. Toward human interaction with bio-inspired robot teams. In 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2859–2864. IEEE.

    Google Scholar

    Graff D., Richling J. & Werner M. 2013. Programming and managing the swarm - an operating system for an emerging system of mobile devices. In IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks, 0, 9–16. IEEE Computer Society.

    Google Scholar

    Graff D., Richling J. & Werner M. 2014. jSwarm: distributed coordination in robot swarms. In Proceedings of the International Workshop on Robotic Sensor Networks (RSN).

    Google Scholar

    Groß R., Bonani M., Mondada F. & Dorigo M. 2006. Autonomous self-assembly in swarm-bots. IEEE Transactions on Robotics 22(6), 1115–1130.

    Google Scholar

    Haken H. 1984. The Science of Structure: Synergetics. Van Nostrand Reinhold.

    Google Scholar

    Hernandez-Sosa D., Dominguez-Brito A. C., Guerra-Artal C. & Cabrera-Gámez J. 2005. Runtime self-adaptation in a component-based robotic framework. In IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), 2700–2705. IEEE.

    Google Scholar

    Hertzberg J., Jaeger H., Zimmer U. & Morignot P 1998. A framework for plan execution in behavior-based robots. In Intelligent Control (ISIC), 1998. Held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), Intelligent Systems and Semiotics (ISAS), Proceedings, 8–13. IEEE.

    Google Scholar

    Hoffmann J. 2001. FF: the fast-forward planning system. AI Magazine 22(3), 57.

    Google Scholar

    Hrabia C.-E., Masuch N. & Albayrak S. 2015. A metrics framework for quantifying autonomy in complex systems. In Müller, P. J., Ketter, W., Kaminka, G., Wagner, G., and Bulling, N. (eds), Multiagent System Technologies: 13th German Conference, MATES 2015, Cottbus, Germany, September 28–30, 2015, Revised Selected Papers. Springer International Publishing, 22–41.

    Google Scholar

    Joshi R. K., Carbone P., Wang F. C., Kravets V. G., Su Y., Grigorieva I. V., Wu H. A., Geim A. K. & Nair R. R. 2014. Precise and ultrafast molecular sieving through graphene oxide membranes. Science 343(6172), 752–754.

    Google Scholar

    Jung D. 1998. An architecture for cooperation among autonomous agents. PhD thesis, University of South Australia.

    Google Scholar

    Kaminka G. A. 2012. Autonomous agents research in robotics: a report from the trenches. In AAAI Spring Symposium: Designing Intelligent Robots.

    Google Scholar

    Kauffman S. A. 1995. At Home in the Universe: The Search for Laws of Self-Organization and Complexity. Oxford University Press.

    Google Scholar

    Kazadi S. T. 2000. Swarm engineering. Phd, California Institute of Technology.

    Google Scholar

    Kephart J. O. & Chess D. M. 2003. The vision of autonomic computing. Computer 36(1), 41–50.

    Google Scholar

    Kernbach S., Meister E., Schlachter F., Jebens K., Szymanski M., Liedke J., Laneri D., Winkler L., Schmickl T., Thenius R., Corradi P. & Ricotti L. 2008. Symbiotic Robot Organisms: REPLICATOR and SYMBRION Projects. In Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems, PerMIS ’08, 62–69. ACM.

    Google Scholar

    Klavins E. 2004. A language for modeling and programming cooperative control systems. In 2004 IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA ’04., 4, 3403–3410. IEEE.

    Google Scholar

    Kloetzer M. & Belta C. 2006. Hierarchical abstractions for robotic swarms. In Proceedings of the 2006 IEEE International Conference on Robotics and Automation. ICRA 2006, 952–957.

    Google Scholar

    Krupke D., Ernestus M., Hemmer M. & Fekete S. 2015. Distributed cohesive control for robot swarms: maintaining good connectivity in the presence of exterior forces. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 413–420.

    Google Scholar

    Lee Y.-S. & Cho S.-B. 2014. A hybrid system of hierarchical planning of behaviour selection networks for mobile robot control. International Journal of Advanced Robotic Systems 11(4), 1–13.

    Google Scholar

    de Lemos R., Giese H., Müller H. A., Shaw M., Andersson J., Litoiu M., Schmerl B., Tamura G., Villegas N. M., Vogel T., Weyns D., Baresi L., Becker B., Bencomo N., Brun Y., Cukic B., Desmarais R., Dustdar S., Engels G., Geihs K., Göschka K. M., Gorla A., Grassi V., Inverardi P., Karsai G., Kramer J., Lopes A., Magee J., Malek S., Mankovskii S., Mirandola R., Mylopoulos J., Nierstrasz O., Pezzè M., Prehofer C., Schäfer W., Schlichting R., Smith D. B., Sousa J. P., Tahvildari L., Wong K. & Wuttke J. 2013. Software engineering for self-adaptive systems: a second research roadmap. In Software Engineering for Self-Adaptive Systems II: International Seminar, Dagstuhl Castle, Germany, October 24-29, 2010 Revised Selected and Invited Papers, de Lemos, R., Giese, H., Müller, H. & Shaw, M. (eds). Springer Verlag, 1–32.

    Google Scholar

    Maes P. 1989. How to do the right thing. Connection Science 1(3), 291–323.

    Google Scholar

    Mamei M., Vasirani M. & Zambonelli F. 2005. Self-organizing spatial shapes in mobile particles: The TOTA approach. In Engineering Self-Organising Systems, 138–153. Springer.

    Google Scholar

    Masar M. 2013. A biologically inspired swarm robot coordination algorithm for exploration and surveillance. In IEEE 17th International Conference on Intelligent Engineering Systems (INES), 271–275.

    Google Scholar

    Matarić M. J. 1995. Issues and approaches in the design of collective autonomous agents. Robotics and Autonomous Systems 16(2–4), 321–331.

    Google Scholar

    Montagna S., Viroli M., Fernandez-Marquez J. L., Di Marzo Serugendo G. & Zambonelli F. 2013. Injecting self-organisation into pervasive service ecosystems. Mobile Networks and Applications 18(3), 398–412.

    Google Scholar

    Morandini M., Migeon F., Gleizes M.-P., Maurel C., Penserini L. & Perini A. 2009. A goal-oriented approach for modelling self-organising MAS. In Engineering Societies in the Agents World X, Lecture Notes in Computer Science. Springer, 33–48.

    Google Scholar

    Naghsh A. M., Gancet J., Tanoto A. & Roast C. 2008. Analysis and design of human-robot swarm interaction in firefighting. In The 17th IEEE International Symposium on Robot and Human Interactive Communication, 2008. RO-MAN 2008, 255–260. IEEE.

    Google Scholar

    Nagpal R. 2002. Programmable self-assembly using biologically-inspired multiagent control. In Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems: Part 1, 418–425. ACM.

    Google Scholar

    Newman D. V. 1996. Emergence and strange attractors. Philosophy of Science 63(2), 245–261.

    Google Scholar

    Nicola R. D., Latella D., Lafuente A. L., Loreti M., Margheri A., Massink M., Morichetta A., Pugliese R., Tiezzi F. & Vandin A. 2015. The SCEL language: design, implementation, verification. In Software Engineering for Collective Autonomic Systems, Wirsing, M., Holzl, M., Koch, N. & Mayer, P. (eds), Lecture Notes in Computer Science 8998. Springer International Publishing, 3–71.

    Google Scholar

    Nicolis G. 1989. Physics of far-from-equilibrium systems and self-organisation. The New Physics 11, 316–347.

    Google Scholar

    Noël V. & Zambonelli F. 2015. Methodological guidelines for engineering self-organization and emergence. Software Engineering for Collective Autonomic Systems, In Wirsing, M., Huolzl, M., Koch, N. & Mayer, P. (eds), Lecture Notes in Computer Science 8998. Springer International Publishing, 355–378.

    Google Scholar

    Nolfi S. & Floreano D. 2000. Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press Cambridge.

    Google Scholar

    Oreizy P., Gorlick M. M., Taylor R. N., Heimbigner D., Johnson G., Medvidovic N., Quilici A., Rosenblum D. S. & Wolf A. L. 1999. An architecture-based approach to self-adaptive software. IEEE Intelligent Systems 14(3), 54–62.

    Google Scholar

    Penders J., Alboul L., Witkowski U., Naghsh A., Saez-Pons J., Herbrechtsmeier S. & El-Habbal M. 2011. A Robot Swarm Assisting a Human Fire-Fighter. Advanced Robotics 25(1–2), 93–117.

    Google Scholar

    Picard G. & Gleizes M.-P. 2004. The ADELFE methodology. In Methodologies and Software Engineering for Agent Systems, 157–175. Springer.

    Google Scholar

    Pinciroli C. & Beltrame G. 2016. Buzz: An extensible programming language for heterogeneous swarm robotics. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3794–3800. IEEE.

    Google Scholar

    Preisler T., Vilenica A. & Renz W. 2013. Decentralized coordination in self-organizing systems based on peer-to-peer coordination spaces. Electronic Communications of the EASST 56, 1–13.

    Google Scholar

    Prokopenko M. 2009. Guided self-organization. Human Frontiers Science Program (HFSP) Journal 3(5), 287–289.

    Google Scholar

    Quigley M., Conley K., Gerkey B., Faust J., Foote T., Leibs J., Wheeler R. & Ng A. Y. 2009a. Ros: an open-source robot operating system. ICRA Workshop on Open Source Software 3 (3.2): 5.

    Google Scholar

    Quigley M., Conley K., Gerkey B., Faust J., Foote T., Leibs J., Wheeler R. & Ng A. Y. 2009a. ROS: an open-source Robot Operating System. In ICRA Workshop on Open Source Software 3, 5.

    Google Scholar

    Reina A., Dorigo M. & Trianni V. 2014. Towards a cognitive design pattern for collective decision-making. In Swarm Intelligence, 194–205. Springer International Publishing.

    Google Scholar

    Ren W. & Beard A. W. 2004. A decentralized scheme for spacecraft formation flying via the virtual structure approach. AIAA Journal of Guidance, Control, and Dynamics 27, 73–82.

    Google Scholar

    Şahin E 2005. Swarm robotics: from sources of inspiration to domains of application. In Swarm Robotics, Şahin, E. & Spears, W. M. (eds), Lecture Notes in Computer Science 3342. Springer, 10–20.

    Google Scholar

    Schmeck H. 2005. Organic computing – a new vision for distributed embedded systems. In 8th IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC), 201–203.

    Google Scholar

    Serugendo G. D. M., Gleizes M.-P. & Karageorgos A. 2005. Self-organization in multi-agent systems. The Knowledge Engineering Review 20(2), 165–189.

    Google Scholar

    Serugendo G. D. M., Gleizes M. P. & Karageorgos A. 2006. Self-organisation and emergence in MAS: an overview. Informatica (Slovenia) 30(1), 45–54.

    Google Scholar

    Steghöfer J.-P., Seebach H., Eberhardinger B. & Reif W. 2014. PosoMAS: an extensible, modular SE process for open self-organising systems. In PRIMA 2014: Principles and Practice of Multi-Agent Systems, Dam, H. K., Pitt, J., Xu, Y., Governatori, G. & Ito, T. (eds), Lecture Notes in Computer Science 8861. Springer International Publishing, 1–17.

    Google Scholar

    Sudeikat J., Braubach L., Pokahr A., Renz W. & Lamersdorf W. 2009. Systematically engineering self-organizing systems: the SodekoVS approach. Electronic Communications of the EASST 17, 1–12.

    Google Scholar

    Sudeikat J. & Renz W. 2009. MASDynamics: toward systemic modeling of decentralized agent coordination. In Kommunikation in Verteilten Systemen (KiVS), Informatik aktuell, David, K. & Geihs, K. (eds). Springer, 79–90.

    Google Scholar

    Viroli M., Casadei M. & Omicini A. 2009. A framework for modelling and implementing self-organising coordination. In Proceedings of the 2009 ACM Symposium on Applied Computing, 1353–1360. ACM.

    Google Scholar

    Viscido S. V., Parrish J. K. & Grünbaum D. 2004. Individual behavior and emergent properties of fish schools: a comparison of observation and theory. Marine Ecology Progress Series 273, 239–250.

    Google Scholar

    Wegner P. 1997. Why interaction is more powerful than algorithms. Communications of the ACM 40(5), 80–91.

    Google Scholar

    Winfield A. F. T., Harper C. J. & Nembrini J. 2004. Towards dependable swarms and a new discipline of swarm engineering. In Swarm Robotics, 126–142. Springer.

    Google Scholar

    Ye D., Zhang M. & Vasilakos A. V. 2017. A survey of self-organization mechanisms in multiagent systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems 47(3), 441–461.

    Google Scholar

    Yu C.-H. & Nagpal R. 2009. Self-adapting modular robotics: A generalized distributed consensus framework. In IEEE International Conference on Robotics and Automation(ICRA), 1881–1888. IEEE.

    Google Scholar

    Yu C.-H. & Nagpal R. 2011. A self-adaptive framework for modular robots in a dynamic environment: theory and applications. The International Journal of Robotics Research 30(8), 1015–1036.

    Google Scholar

  • Cite this article

    Christopher-Eyk Hrabia, Marco Lützenberger, Sahin Albayrak. 2018. Towards adaptive multi-robot systems: self-organization and self-adaptation. The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000176
    Christopher-Eyk Hrabia, Marco Lützenberger, Sahin Albayrak. 2018. Towards adaptive multi-robot systems: self-organization and self-adaptation. The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000176
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ORIGINAL RESEARCH   Open Access    

Towards adaptive multi-robot systems: self-organization and self-adaptation

  • DAI-Labor

  • Published online: 04 October 2018
The Knowledge Engineering Review  33,  2018, 33(1)  |  Cite this article

Abstract: Abstract: 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.

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    Christopher-Eyk Hrabia, Marco Lützenberger, Sahin Albayrak. 2018. Towards adaptive multi-robot systems: self-organization and self-adaptation. The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000176
    Christopher-Eyk Hrabia, Marco Lützenberger, Sahin Albayrak. 2018. Towards adaptive multi-robot systems: self-organization and self-adaptation. The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000176
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