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Behavior trees as a control architecture in the automatic modular design of robot swarms

par Kuckling, Jonas ;Ligot, Antoine ;Bozhinoski, Darko ;Birattari, Mauro
Editeur scientifique Birattari, Mauro ;Birattari, Mauro ;Birattari, Mauro ;Birattari, Mauro ;Birattari, Mauro ;Birattari, Mauro
Référence ANTS 2018 Eleventh International Conference on Swarm Intelligence(11: October 29-31, 2018: Rome, Italy), Swarm Intelligence, 11th International Conference, ANTS 2018, Rome, Italy, October 29–31, 2018, Proceedings, Springer, Cham, Switzerland, page (30-43)
Publication Publié, 2018-10-29

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Résumé :

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.