Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, 16636 Prague 6, Czech Republic.
Department of Generation Technologies and Materials, Ricerca sul Sistema Energetico (RSE) S.p.A., 20134 Milan, Italy.
Bioinspir Biomim. 2022 Nov 3;17(6). doi: 10.1088/1748-3190/ac98e6.
This article proposes Persistence Administered Collective Navigation (PACNav) as an approach for achieving the decentralized collective navigation of unmanned aerial vehicle (UAV) swarms. The technique is based on the flocking and collective navigation behavior observed in natural swarms, such as cattle herds, bird flocks, and even large groups of humans. As global and concurrent information of all swarm members is not available in natural swarms, these systems use local observations to achieve the desired behavior. Similarly, PACNav relies only on local observations of the relative positions of UAVs, making it suitable for large swarms deprived of communication capabilities and external localization systems. We introduce the novel concepts ofandthat allow each swarm member to analyze the motion of other members in order to determine its own future motion. PACNav is based on two main principles: (a) UAVs with little variation in motion direction have high, and are considered by other UAVs to be reliable leaders; (b) groups of UAVs that move in a similar direction have high, and such groups are assumed to contain a reliable leader. The proposed approach also embeds a reactive collision avoidance mechanism to avoid collisions with swarm members and environmental obstacles. This collision avoidance ensures safety while reducing deviations from the assigned path. Along with several simulated experiments, we present a real-world experiment in a natural forest, showcasing the validity and effectiveness of the proposed collective navigation approach in challenging environments. The source code is released as open-source, making it possible to replicate the obtained results and facilitate the continuation of research by the community.
本文提出了持久性管理集体导航 (PACNav) 方法，用于实现无人机 (UAV) 群的分散集体导航。该技术基于自然群体中观察到的集群和集体导航行为，如牛群、鸟群，甚至是大量人类。由于自然群体中没有所有群体成员的全局和并发信息，这些系统仅使用局部观察来实现所需的行为。同样，PACNav 仅依赖于 UAV 相对位置的局部观察，因此适用于缺乏通信能力和外部定位系统的大型群体。我们引入了和的新概念，使每个群体成员都可以分析其他成员的运动，从而确定自己未来的运动。PACNav 基于两个主要原则：(a) 运动方向变化较小的无人机具有较高的，被其他无人机视为可靠的领导者；(b) 运动方向相似的无人机群具有较高的，这些群体被认为包含可靠的领导者。所提出的方法还嵌入了一种反应式避碰机制，以避免与群体成员和环境障碍物发生碰撞。这种避碰机制在确保安全的同时，减少了对分配路径的偏离。除了几个模拟实验外，我们还在自然森林中进行了真实世界的实验，展示了所提出的集体导航方法在具有挑战性的环境中的有效性和有效性。该源代码以开源形式发布，这使得复制获得的结果和促进社区的研究继续成为可能。
