Abstract
Previous research has shown that automatically combining low-level behaviors into a probabilistic finite state machine produces control software that crosses the reality gap satisfactorily. In this paper, we explore the possibility of adopting behavior trees as an architecture for the control software of robot swarms. We introduce Maple: an automatic design method that combines preexisting modules into behavior trees. To highlight the potential of this control architecture, we present robot experiments in which we compare Maple with Chocolate and EvoStick on two missions: foraging and aggregation. Chocolate and EvoStick are two previously published automatic design methods. Chocolate is a modular method that generates probabilistic finite state machines and EvoStick is a traditional evolutionary robotics method. The results of the experiments indicate that behavior trees are a viable and promising architecture to automatically generate control software for robot swarms.
J. Kuckling and A. Ligot contributed equally to the research and should be considered co–first authors. Behavior trees were originally brought to the attention of the authors by DB. The proposed method was conceived by the four authors. It was implemented and tested by JK and AL. The initial draft of the manuscript was written by JK and AL and then revised by DB and MB. The research was directed by MB.
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Notes
- 1.
In biology this behavior is known as negative phototaxis [28].
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Acknowledgements
The project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 681872). Mauro Birattari acknowledges support from the Belgian Fonds de la Recherche Scientifique – FNRS.
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Kuckling, J., Ligot, A., Bozhinoski, D., Birattari, M. (2018). Behavior Trees as a Control Architecture in the Automatic Modular Design of Robot Swarms. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A., Reina, A., Trianni, V. (eds) Swarm Intelligence. ANTS 2018. Lecture Notes in Computer Science(), vol 11172. Springer, Cham. https://doi.org/10.1007/978-3-030-00533-7_3
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