一个多功能群体行为自动生成框架
A Framework for Automatic Behavior Generation in Multi-Function Swarms
摘要：
多功能群是一次解决多个任务的群。例如，四轴飞行器群可以负责探索感兴趣的区域，同时充当临时中继。这种类型的多功能带来了同时处理潜在冲突需求的挑战。利用质量多样性算法MAP-精英结合一个合适的控制器结构，提出了一个框架的多功能群体的自动行为生成。该框架测试的情况下，同时进行三项任务：勘探，通信网络的创建和地理定位的RF发射器。一个剧目是演变，包括广泛的控制器，或行为原语，在不同的任务具有不同的特点和权衡。这套系统将使群体能够根据情境要求在线进行行为权衡。此外，噪声对MAP精英的行为特征的影响进行了研究。一个中等数量的重新评估被发现，以增加鲁棒性，同时保持相对较低的计算要求。几个选定的控制器进行检查，并探讨这些控制器之间的过渡的动态。最后，研究开发了一种方法来分析所得到的控制器的化妆。这是通过一个参数变化的研究，其中的重要性，个别输入的群体控制器进行评估和分析。
英文摘要：
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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