Automatic Design of Robot Swarms: Achievements and Challenges

Self Cite

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.JK and AL 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.The final authenticated version is available online at https://doi.

Section: Introductionmentioning

confidence: 99%

Behavior Trees as a Control Architecture in the Automatic Modular Design of Robot Swarms

Kuckling

Ligot

Bozhinoski

et al. 2018

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“…Notwithstanding the advancements achieved in the last decade [24,29,4,51,7,43,34], the design of robot swarms is still at dawn and no generally applicable methodology has been proposed so far [8,11,21]. Automatic design methods are a promising way of approaching the issue [15,6]. In automatic methods, the design problem is cast into an optimization problem: a space of solutions is searched via an optimization algorithm, with the goal of maximizing a performance measure.…”

Section: Introductionmentioning

confidence: 99%

Automatic Design of Communication-Based Behaviors for Robot Swarms

Hasselmann

Robert

Birattari

2018

Self Cite

We introduce Gianduja, an automatic design method that generates communication-based behaviors for robot swarms. Gianduja extends Chocolate, a previously published design method. It does so by providing the robots with the capability to communicate using one message. The semantics of the message is not a priori fixed. It is the automatic design process that implicitly defines it, on a per-mission basis, by prescribing the conditions under which the message is sent by a robot and how the receiving peers react to it. We empirically study Gianduja on three missions and we compare it with the aforementioned Chocolate and with EvoCom, a rather standard evolutionary robotics method that generates communication-based behaviors. We evaluate the behaviors produced by the three automatic design methods on a swarm of 20 e-puck robots. The results show that Gianduja uses communication meaningfully and effectively in all the three missions considered. The aggregate results indicate that, on the three missions considered, Gianduja performs significantly better than the two other methods under analysis.

“…Evolutionary algorithms can search through vast solution spaces and discover solutions to complex problems, and are thus a popular approach to dealing with the intricacies of swarm robotics and extracting valid local behaviors [6,14]. They have been used for numerous architectures, including: neural networks [3,11], state machines [7], behavior trees [12], and grammar rules [5].…”

Section: Related Work and Research Contextmentioning

confidence: 99%

Optimization of Swarm Behavior Assisted by an Automatic Local Proof for a Pattern Formation Task

Coppola

Croon

2018

In this work, we optimize the behavior of swarm agents in a pattern formation task. We start with a local behavior, expressed as a local state-action map, that has been formally proven to lead the swarm to always eventually form the desired pattern. We seek to optimize this for performance while keeping the formal proof. First, the state-action map is pruned to remove unnecessary state-action pairs, reducing the solution space. Then, the probabilities of executing the remaining actions are tuned with a genetic algorithm. The final controllers allow the swarm to form the patterns up to orders of magnitude faster than with the original behavior. The optimization is found to suffer from scalability issues. These may be tackled in future work by automatically minimizing the size of the local state-action map with a further direct focus on performance.