A Framework for Automatic Behavior Generation in Multi-Function Swarms

• URL: http://arxiv.org/abs/2007.08656v1
• Date: Sat, 11 Jul 2020 20:50:52 GMT
• Title: A Framework for Automatic Behavior Generation in Multi-Function Swarms
• Authors: Sondre A. Engebraaten, Jonas Moen, Oleg A. Yakimenko, Kyrre Glette
• Abstract summary: A framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks. The effect of noise on the behavior characteristics in MAP-elites is investigated.
• Score: 1.290382979353427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-function swarms are swarms that solve multiple tasks at once. For example, a quadcopter swarm could be tasked with exploring an area of interest while simultaneously functioning as ad-hoc relays. With this type of multi-function comes the challenge of handling potentially conflicting requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites in combination with a suitable controller structure, a framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks: exploration, communication network creation and geolocation of RF emitters. A repertoire is evolved, consisting of a wide range of controllers, or behavior primitives, with different characteristics and trade-offs in the different tasks. This repertoire would enable the swarm to transition between behavior trade-offs online, according to the situational requirements. Furthermore, the effect of noise on the behavior characteristics in MAP-elites is investigated. A moderate number of re-evaluations is found to increase the robustness while keeping the computational requirements relatively low. A few selected controllers are examined, and the dynamics of transitioning between these controllers are explored. Finally, the study develops a methodology for analyzing the makeup of the resulting controllers. This is done through a parameter variation study where the importance of individual inputs to the swarm controllers is assessed and analyzed.

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