Optimization-based design is an effective and promising approach to realizing collective behaviours for robot swarms. Unfortunately, the domain literature often remains vague about the exact role played by the human designer, if any. It is our contention that two cases should be disentangled: semi-automatic design, in which a human designer operates and steers an optimization process (for example, by fine-tuning the parameters of the optimization algorithm); and (fully) automatic design, in which the optimization process does not involve, need or allow any human intervention. In this Perspective, we briefly review the relevant literature; illustrate the hypotheses, characteristics and core challenges of semi-automatic and automatic design; and sketch out the context in which they could be ideally applied.
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Dorigo, M., Birattari, M. & Brambilla, M. Swarm robotics. Scholarpedia 9, 1463 (2014).
Yang, G.-Z. et al. The grand challenges of Science Robotics. Sci. Robot. 3, eaar7650 (2018).
Rubenstein, M., Cornejo, A. & Nagpal, R. Programmable self-assembly in a thousand-robot swarm. Science 345, 795–799 (2014).
Werfel, J., Petersen, K. & Nagpal, R. Designing collective behavior in a termite-inspired robot construction team. Science 343, 754–758 (2014).
Garattoni, L. & Birattari, M. Autonomous task sequencing in a robot swarm. Sci. Robot. 3, eaat0430 (2018).
Slavkov, I. et al. Morphogenesis in robot swarms. Sci. Robot. 3, eaau9178 (2018).
Yu, J., Wang, B., Du, X., Wang, Q. & Zhang, L. Ultra-extensible ribbon-like magnetic microswarm. Nat. Commun. 9, 3260 (2018).
Li, S. et al. Particle robotics based on statistical mechanics of loosely coupled components. Nature 567, 361–365 (2019).
Xie, H. et al. Reconfigurable magnetic microrobot swarm: multimode transformation, locomotion, and manipulation. Sci. Robot. 4, eaav8006 (2019).
Brambilla, M., Ferrante, E., Birattari, M. & Dorigo, M. Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7, 1–41 (2013).
Brugali, D. (ed.) Software Engineering for Experimental Robotics (Springer, 2007).
Di Ruscio, D., Malavolta, I. & Pelliccione, P. A family of domain-specific languages for specifying civilian missions of multi-robot systems. In Proceedings of the 1st International Workshop on Model-Driven Robot Software Engineering Vol. 1319 (eds Aßmann, U. & Wagner, G.) 13–26 (CEUR-WS, 2014).
Bozhinoski, D., Di Ruscio, D., Malavolta, I., Pelliccione, P. & Tivoli, M. Flyaq: enabling non-expert users to specify and generate missions of autonomous multicopters. In Proceedings of the 2015 30th IEEE/ACM International Conference on Automated Software Engineering (eds Cohen, M., Grunske, L. & Whalen, M.) 801–806 (IEEE, 2015).
Schlegel, C. et al. Model-driven software systems engineering in robotics: covering the complete life-cycle of a robot. Inform. Technol. 57, 85–98 (2015).
Hamann, H. & Wörn, H. A framework of space–time continuous models for algorithm design in swarm robotics. Swarm Intell. 2, 209–239 (2008).
Kazadi, S. Model independence in swarm robotics. Int. J. Intell. Comput. Cybern. 2, 672–694 (2009).
Berman, S., Kumar, V. & Nagpal, R. Design of control policies for spatially inhomogeneous robot swarms with application to commercial pollination. In IEEE International Conference on Robotics and Automation 378–385 (IEEE, 2011).
Beal, J., Dulman, S., Usbeck, K., Viroli, M. & Correll, N. in Formal and Practical Aspects of Domain-Specific Languages: Recent Developments (ed. Mernik, M.) 436–501 (IGI Global, 2012).
Brambilla, M., Brutschy, A., Dorigo, M. & Birattari, M. Property-driven design for swarm robotics: a design method based on prescriptive modeling and model checking. ACM Trans. Auton. Adapt. Sys. 9, 17 (2014).
Reina, A., Valentini, G., Fernández-Oto, C., Dorigo, M. & Trianni, V. A design pattern for decentralised decision making. PLoS ONE 10, e0140950 (2015).
Lopes, Y. K., Trenkwalder, S. M., Leal, A. B., Dodd, T. J. & Groβ, R. Supervisory control theory applied to swarm robotics. Swarm Intell. 10, 65–97 (2016).
Pinciroli, C. & Beltrame, G. Buzz: a programming language for robot swarms. IEEE Softw. 33, 97–100 (2016).
Hamann, H. Swarm Robotics: A Formal Approach (Springer, 2018).
Trianni, V. Evolutionary Swarm Robotics (Springer, 2008).
Hecker, J. P., Letendre, K., Stolleis, K., Washington, D. & Moses, M. E. Formica ex machina: ant swarm foraging from physical to virtual and back again. In International Conference on Swarm Intelligence Vol. 7461 (eds Dorigo, M. et al.) 252–259 (Springer, 2012).
Francesca, G. & Birattari, M. Automatic design of robot swarms: achievements and challenges. Front. Robot. AI 3, 29 (2016).
Bredeche, N., Haasdijk, E. & Prieto, A. Embodied evolution in collective robotics: a review. Front. Robot. AI 5, 12 (2018).
Brooks, R. A. Artificial life and real robots. In Towards a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life (eds Varela, F. J. & Bourgine, P.) 3–10 (MIT Press, 1992).
Jakobi, N., Husbands, P. & Harvey, I. Noise and the reality gap: the use of simulation in evolutionary robotics. In Advances in Artificial Life: Third European Conference on Artificial Life Vol. 929 (eds Moraán, F. et al.) 704–720 (Springer, 1995).
Nolfi, S. & Floreano, D. Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines (MIT Press, 2000).
Floreano, D., Husbands, P. & Nolfi, S. in Springer Handbook of Robotics (eds Siciliano, B. & Khatib, O.) 1423–1451 (Springer, 2008).
Christensen, A. L. & Dorigo, M. Evolving an integrated phototaxis and hole-avoidance behavior for a swarm-bot. In Artificial Life X: Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems (eds Rocha, L. M. et al.) 248–254 (MIT Press, 2006).
Divband Soorati, M. & Hamann, H. The effect of fitness function design on performance in evolutionary robotics: the influence of a priori knowledge. In GECCO 2015: Proceedings of the Genetic and Evolutionary Computation Conference (ed. Silva, S.) 153–160 (ACM, 2015).
Floreano, D. & Urzelai, J. Evolutionary robots with on-line self-organization and behavioral fitness. Neural Netw. 13, 431–443 (2000).
Doncieux, S. & Mouret, J.-B. Beyond black-box optimization: a review of selective pressures for evolutionary robotics. Evol. Intell. 7, 71–93 (2014).
Silva, F., Duarte, M., Correia, L., Oliveira, S. M. & Christensen, A. L. Open issues in evolutionary robotics. Evol. Comput. 24, 205–236 (2016).
Quinn, M., Smith, L., Mayley, G. & Husbands, P. Evolving controllers for a homogeneous system of physical robots: structured cooperation with minimal sensors. Philos. Trans. R. Soc. A 361, 2321–2343 (2003).
Dorigo, M. et al. Evolving self-organizing behaviors for a swarm-bot. Auton. Robots 17, 223–245 (2003).
Ampatzis, C., Tuci, E., Trianni, V. & Dorigo, M. Evolving communicating agents that integrate information over time: a real robot experiment. In Artificial Evolution: Seventh International Conference, Evolution Artificielle Vol. 3871 (eds Talbi, E. G. et al.) 248–254 (Springer, 2006).
Trianni, V. & Dorigo, M. Self-organisation and communication in groups of simulated and physical robots. Biol. Cybern. 95, 213–231 (2006).
Ampatzis, C., Tuci, E., Trianni, V., Christensen, A. L. & Dorigo, M. Evolving self-assembly in autonomous homogeneous robots: experiments with two physical robots. Artif. Life 15, 465–484 (2009).
Duarte, M. et al. Evolution of collective behaviors for a real swarm of aquatic surface robots. PLoS ONE 11, e0151834 (2016).
Jones, S., Studley, M., Hauert, S. & Winfield, A. Evolving behaviour trees for swarm robotics. In Distributed Autonomous Robotic Systems Vol. 6 (eds Groß, R. et al.) 487–501 (Springer, 2016).
Watson, R. A., Ficici, S. G. & Pollack, J. B. Embodied evolution: distributing an evolutionary algorithm in a population of robots. Robot. Auton. Syst. 39, 1–18 (2002).
Bredeche, N., Montanier, J.-M., Liu, W. & Winfield, A. Environment-driven distributed evolutionary adaptation in a population of autonomous robotic agents. Math. Comput. Model. Dyn. Syst. 18, 101–129 (2012).
Jones, S., Winfield, A., Hauert, S. & Studley, M. Onboard evolution of understandable swarm behaviors. Adv. Intell. Syst. 1, 1900031 (2019).
Birattari, M. et al. Automatic off-line design of robot swarms: a manifesto. Front. Robot. AI 6, 59 (2019).
Waibel, M., Keller, L. & Floreano, D. Genetic team composition and level of selection in the evolution of multi-agent systems. IEEE Trans. Evol. Comput. 13, 648–660 (2009).
Francesca, G., Brambilla, M., Brutschy, A., Trianni, V. & Birattari, M. AutoMoDe: a novel approach to the automatic design of control software for robot swarms. Swarm Intell. 8, 89–112 (2014).
Francesca, G. et al. An experiment in automatic design of robot swarms: AutoMoDe-Vanilla, EvoStick, and human experts. In Swarm Intelligence: 9th International Conference (eds Dorigo, M. et al.) 25–37 (Springer, 2014).
Francesca, G. et al. AutoMoDe-Chocolate: automatic design of control software for robot swarms. Swarm Intell. 9, 125–152 (2015).
Hasselmann, K., Robert, F. & Birattari, M. Automatic design of communication-based behaviors for robot swarms. In Swarm Intelligence – ANTS (eds Dorigo, M. et al.) 11172 (Springer, Cham, Switzerland, 2018), 16–29
Kuckling, J., Ligot, A., Bozhinoski, D. & Birattari, M. Behavior trees as a control architecture in the automatic modular design of robot swarms. In Swarm Intelligence: 11th International Conference (eds Dorigo, M. et al.) 30–43 (Springer, 2018).
Baldassarre, G. et al. Self-organized coordinated motion in groups of physically connected robots. IEEE Trans. Syst. Man Cybern. B 37, 224–239 (2007).
Trianni, V. & Nolfi, S. Self-organizing sync in a robotic swarm: a dynamical system view. IEEE Trans. Evol. Comput. 13, 722–741 (2009).
Gauci, M., Chen, J., Li, W., Dodd, T. J. & Groß, R. Clustering objects with robots that do not compute. In Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems 421–428 (International Foundation for Autonomous Agents and Multiagent Systems, 2014).
Gauci, M., Chen, J., Li, W., Dodd, T. J. & Groß, R. Self-organized aggregation without computation. Int. J. Robot. Res. 33, 1145–1161 (2014).
Usui, Y. & Arita, T. Situated and embodied evolution in collective evolutionary robotics. In Proceedings of the 8th International Symposium on Artificial Life and Robotics 212–215 (AROB, 2003).
The project has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 681872). M.B. acknowledges support from the Belgian Fonds de la Recherche Scientifique—FNRS, of which he is a research director.
The authors declare no competing interests.
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Birattari, M., Ligot, A. & Hasselmann, K. Disentangling automatic and semi-automatic approaches to the optimization-based design of control software for robot swarms. Nat Mach Intell 2, 494–499 (2020). https://doi.org/10.1038/s42256-020-0215-0