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

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 introduceMaple, 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:aggregationandForaging. To explore the strengths and weaknesses of adopting behavior trees as a control architecture, we compareMaplewithChocolate, a previously proposed automatic design method that uses probabilistic finite state machines instead. In the first study, we assessMaple’s ability to produce control software that crosses the reality gap satisfactorily. In the second study, we investigateMaple’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 ofMaplethat 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.