面向大规模无人机集群的仿生视觉相对定位
Bio-inspired visual relative localization for large swarms of UAVs
AI总结：
针对大规模无人机集群相对定位，本文提出仿生邻居密度回归方法及配套集群控制算法，提升了距离估计精度、可扩展性与姿态鲁棒性。
摘要：
我们提出了一种用于大规模无人机（UAV）集群中智能体相对定位的视觉感知新方法。该方法受到沙丁鱼群、蜂群以及其他能够以去中心化但协调一致方式运动的大型动物群体所利用的生物感知机制启发，并不依赖每个智能体检测单个邻居并估计其相对位置，而是建议回归随距离变化的邻居密度。这使得距离估计更加准确，并且在邻居数量方面具有更好的可扩展性。此外，我们提出了一种新颖的集群控制算法，使其与这种新的相对定位方法兼容。我们对所提出的方法进行了全面评估，并证明这种用于距离估计的回归方法对目标不断变化的相对姿态更加鲁棒，而且适合作为集群稳定的主要相对定位来源。
英文摘要：
We propose a new approach to visual perception for relative localization of agents within large-scale swarms of UAVs. Inspired by biological perception utilized by schools of sardines, swarms of bees, and other large groups of animals capable of moving in a decentralized yet coherent manner, our method does not rely on detecting individual neighbors by each agent and estimating their relative position, but rather we propose to regress a neighbor density over distance. This allows for a more accurate distance estimation as well as better scalability with respect to the number of neighbors. Additionally, a novel swarm control algorithm is proposed to make it compatible with the new relative localization method. We provide a thorough evaluation of the presented methods and demonstrate that the regressing approach to distance estimation is more robust to varying relative pose of the targets and that it is suitable to be used as the main source of relative localization for swarm stabilization.
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