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 Radio Frequency (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 enables the swarm to online transition between behaviors featuring different trade-offs of applications depending on 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 investigates the importance of individual sensor or controller inputs. This is done through ablation, where individual inputs are disabled and their impact on the performance of the swarm controllers is assessed and analyzed.
Highlights
Typical applications for swarms are tasks that are either too big or complex for single agents to do well
The core of the framework is the combination of evolutionary methods with a directly encoded controller based on a variant of Physicomimetics, or artificial physics (Spears et al, 2004)
Previous works examined the two applications of exploration and network creation using a repertoire of 10 exploration bins × 100 network bins (Engebråten et al, 2018b)
Summary
Typical applications for swarms are tasks that are either too big or complex for single agents to do well. It might be possible to imagine a single complex and large agent that is able to solve these tasks, this is often undesirable due to system complexity or cost. An operator that requires capacity and performance in both tasks has limited options. One way of tackling this challenge could be to launch two swarms, giving each swarm a task and operating them independently. This adds complexity to the operation and doubles the system cost. Another option is to develop a concept for a multi-function swarm
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