用于间隔保障与多智能体导航的远程识别
Remote ID for separation provision and multi-agent navigation
AI总结：
本研究提出集成改进的远程识别消息与基于uNMAC的互惠速度障碍模型，在定位不确定下实现无碰撞导航，并将任务执行时间减半，提升空域容量与安全性。
摘要：
本文研究了无人机识别数据（远程识别）与碰撞避免机制的集成，以提高多无人机运行的安全性和效率。我们引入了一种改进的近空中碰撞（NMAC）定义，称为无人机NMAC（uNMAC），该定义考虑了由于自定位误差以及两次位置报告之间可能发生的位移而导致的无人机位置不确定性。我们提出的基于uNMAC的互惠速度障碍（RVO）模型将远程识别消息与RVO集成，以实现增强的无碰撞导航。我们建议对远程识别格式进行修改，以包含定位精度和无人机机体尺寸的数据，从而促进更高效的碰撞避免决策。通过大量仿真，我们证明了与基于保守标准远程识别的RVO相比，我们的方法将任务执行时间减半。重要的是，即使在定位不确定的情况下，它也能确保无碰撞运行。通过集成改进的远程识别消息和基于uNMAC的RVO，我们提供了一种在遵守严格安全标准的同时显著提高空域容量的解决方案。我们的研究强调了增强未来无人机运行安全性和效率的潜力，从而使依赖无人机技术的行业受益。
英文摘要：
In this paper, we investigate the integration of drone identification data (Remote ID) with collision avoidance mechanisms to improve the safety and efficiency of multi-drone operations. We introduce an improved Near Mid-Air Collision (NMAC) definition, termed as UAV NMAC (uNMAC), which accounts for uncertainties in the drone's location due to self-localization errors and possible displacements between two location reports. Our proposed uNMAC-based Reciprocal Velocity Obstacle (RVO) model integrates Remote ID messages with RVO to enable enhanced collision-free navigation. We propose modifications to the Remote ID format to include data on localization accuracy and drone airframe size, facilitating more efficient collision avoidance decisions. Through extensive simulations, we demonstrate that our approach halves mission execution times compared to a conservative standard Remote ID-based RVO. Importantly, it ensures collision-free operations even under localization uncertainties. By integrating the improved Remote ID messages and uNMAC-based RVO, we offer a solution to significantly increase airspace capacity while adhering to strict safety standards. Our study emphasizes the potential to augment the safety and efficiency of future drone operations, thereby benefiting industries reliant on drone technologies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
