Automatic modular design of robot swarms using behavior trees as a control architecture

Antoine Ligot; Jonas Kuckling; Darko Bozhinoski; Mauro Birattari

doi:10.7717/peerj-cs.314

Automatic modular design of robot swarms using behavior trees as a control architecture

PeerJ Comput Sci. 2020 Nov 9:6:e314. doi: 10.7717/peerj-cs.314. eCollection 2020.

Authors

Antoine Ligot^#¹, Jonas Kuckling^#¹, Darko Bozhinoski^{1

2}, Mauro Birattari¹

Affiliations

¹ IRIDIA, Université Libre de Bruxelles, Brussels, Belgium.
² Cognitive Robotics, Delft University of Technology, Delft, Netherlands.

^# Contributed equally.

Abstract

We investigate the possibilities, challenges, and limitations that arise from the use of behavior trees in the context of the automatic modular design of collective behaviors in swarm robotics. To do so, we introduce Maple, an automatic design method that combines predefined modules-low-level behaviors and conditions-into a behavior tree that encodes the individual behavior of each robot of the swarm. We present three empirical studies based on two missions: aggregation and Foraging. To explore the strengths and weaknesses of adopting behavior trees as a control architecture, we compare Maple with Chocolate, a previously proposed automatic design method that uses probabilistic finite state machines instead. In the first study, we assess Maple's ability to produce control software that crosses the reality gap satisfactorily. In the second study, we investigate Maple's performance as a function of the design budget, that is, the maximum number of simulation runs that the design process is allowed to perform. In the third study, we explore a number of possible variants of Maple that differ in the constraints imposed on the structure of the behavior trees generated. The results of the three studies indicate that, in the context of swarm robotics, behavior trees might be appealing but in many settings do not produce better solutions than finite state machines.

Keywords: AutoMoDe; Automatic design; Behavior trees; Evolutionary robotics; Finite state machines; Optimisation-based design; Swarm robotics.

Grants and funding

The project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 681872). Mauro Birattari and Jonas Kuckling received support from the Belgian Fonds de la Recherche Scientifique—FNRS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.