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.

中文翻译：

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使用行为树作为控制体系结构的机器人群的自动模块化设计

我们研究了在群体机器人中集体行为的自动模块化设计的背景下，由于使用行为树而引起的可能性，挑战和局限性。为此，我们引入了Maple，这是一种自动设计方法，它将预定义的模块（低级行为和条件）组合到行为树中，该行为树对群中每个机器人的单独行为进行编码。我们基于两个任务提出了三个实证研究：聚集和觅食。为了探索采用行为树作为控制体系结构的优缺点，我们将Maple与Chocolate进行了比较，后者是一种以前提出的使用概率有限状态机的自动设计方法。在第一个研究中，我们评估了Maple生产令人满意地超越现实差距的控制软件的能力。在第二项研究中 我们将Maple的性能作为设计预算的函数进行调查，也就是说，允许设计过程执行的最大模拟运行次数。在第三项研究中，我们探索了Maple的许多可能变体，这些变体在对生成的行为树的结构施加的约束方面有所不同。这三项研究的结果表明，在群体机器人技术的背景下，行为树可能很有吸引力，但在许多情况下，它们不能提供比有限状态机更好的解决方案。