自主无人机的去中心化交通管理
Decentralized traffic management of autonomous drones
AI总结：
针对公共空域无人机增长带来的交通协同瓶颈，提出去中心化自组织交通管理算法，具备安全高效可扩展等特性，可适配多场景并经百架无人机实验验证。
摘要：
随着公共空域中无人机数量持续增长，本地与全局空中交通的协同已成为法律和技术瓶颈。为应对这一挑战，控制的自动化与去中心化是必然要求。本文提出了一种解决方案，可让协作的自主智能体自组织形成高效的交通流状态，从而解决充满冲突的公共空中协同任务。通过真实仿真我们证明，该算法在无人机数量及其速度范围方面具备安全性、高效性与可扩展性，同时还能处理异构智能体，甚至智能体之间的成对优先级。该算法适用于二维的任意稀疏或密集交通场景，通过三维分层飞行空间结构可进一步提升效率。为验证方案的可行性，我们通过实验展示了100架自主无人机在半径125米的圆形区域内的协同空中交通运行情况。
英文摘要：
Coordination of local and global aerial traffic has become a legal and technological bottleneck as the number of unmanned vehicles in the common airspace continues to grow. To meet this challenge, automation and decentralization of control is an unavoidable requirement. In this paper, we present a solution that enables self-organization of cooperating autonomous agents into an effective traffic flow state in which the common aerial coordination task - filled with conflicts - is resolved. Using realistic simulations, we show that our algorithm is safe, efficient, and scalable regarding the number of drones and their speed range, while it can also handle heterogeneous agents and even pairwise priorities between them. The algorithm works in any sparse or dense traffic scenario in two dimensions and can be made increasingly efficient by a layered flight space structure in three dimensions. To support the feasibility of our solution, we experimentally demonstrate coordinated aerial traffic of 100 autonomous drones within a circular area with a radius of 125 meters.
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。
