Autonomous Aerial Swarming in GNSS-denied Environments with High Obstacle Density
Autonomous Aerial Swarming in GNSS-denied Environments with High Obstacle Density
复制标题
高障碍物密度、GNSS 拒绝环境中的自主空中集群
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
M. Saska
中科院分区：
文献类型：
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作者：
Afzal Ahmad;V. Walter;Pavel Petráček;Matěj Petrlík;T. Báča;David Žaitlík;M. Saska
本文讨论了高障碍物密度区域中相对局部的无人机（UAV）的紧凑集群。所提出的工作解决了现实场景，其中环境地图是先验未知的，并且由于存在障碍而很难使用全球定位系统和通信基础设施。为了在这种受限的环境中实现集群，我们提出了一种完全分散的、仿生控制法，该控制法仅使用机载传感器数据来在环境中安全集群，而无需与其他代理进行任何通信。在所提出的方法中，每个无人机代理使用机载传感器进行自定位并估计其他代理在其本地参考系中的相对位置。使用现实机器人模拟器和天然森林中的各种实验验证和评估了所提出方法的可用性和性能。所提出的实验还验证了在缺乏全球定位信息和通信的情况下机载相对定位对于自主多无人机应用的实用性。
The compact flocking of relatively localized Un-manned Aerial Vehicles (UAVs) in high obstacle density areas is discussed in this paper. The presented work tackles realistic scenarios in which the environment map is not known apriori and the use of a global localization system and communication infrastructure is difficult due to the presence of obstacles. To achieve flocking in such a constrained environment, we propose a fully decentralized, bio-inspired control law that uses only onboard sensor data for safe flocking through the environment without any communication with other agents. In the proposed approach, each UAV agent uses onboard sensors to self-localize and estimate the relative position of other agents in its local reference frame. The usability and performance of the proposed approach were verified and evaluated using various experiments in a realistic robotic simulator and a natural forest. The presented experiments also validate the utility of onboard relative localization for autonomous multi-UAV applications in the absence of global localization information and communication.