Decentralized traffic management of autonomous drones

IF 2.1 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm Intelligence Pub Date : 2024-07-11 DOI:10.1007/s11721-024-00241-y

Boldizsár Balázs, Tamás Vicsek, Gergő Somorjai, Tamás Nepusz, Gábor Vásárhelyi

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Abstract

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 show stable traffic simulations with up to 5000 agents, and experimentally demonstrate coordinated aerial traffic of 100 autonomous drones within a 250 m wide circular area.

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自主无人机的分散式交通管理

随着公共空域中无人驾驶飞行器数量的不断增加，协调本地和全球空中交通已成为法律和技术瓶颈。为了应对这一挑战，控制的自动化和分散化是不可避免的要求。在本文中，我们提出了一种解决方案，可使合作的自主代理自组织进入有效的交通流状态，在这种状态下，充满冲突的共同空中协调任务得以解决。通过实际模拟，我们证明了我们的算法是安全、高效的，并且在无人机数量和速度范围上具有可扩展性，同时它还能处理异构代理，甚至是它们之间的配对优先级。该算法适用于二维的任何稀疏或密集交通场景，并可通过三维的分层飞行空间结构提高效率。为了证明我们解决方案的可行性，我们展示了多达 5000 个代理的稳定交通模拟，并在 250 米宽的圆形区域内实验演示了 100 架自主无人机的协调空中交通。

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来源期刊

Swarm Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ROBOTICS

CiteScore

5.70

自引率

11.50%

发文量

审稿时长

>12 weeks

期刊介绍： Swarm Intelligence is the principal peer-reviewed publication dedicated to reporting on research and developments in the multidisciplinary field of swarm intelligence. The journal publishes original research articles and occasional review articles on theoretical, experimental and/or practical aspects of swarm intelligence. All articles are published both in print and in electronic form. There are no page charges for publication. Swarm Intelligence is published quarterly. The field of swarm intelligence deals with systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, it focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. It is a fast-growing field that encompasses the efforts of researchers in multiple disciplines, ranging from ethology and social science to operations research and computer engineering. Swarm Intelligence will report on advances in the understanding and utilization of swarm intelligence systems, that is, systems that are based on the principles of swarm intelligence. The following subjects are of particular interest to the journal: • modeling and analysis of collective biological systems such as social insect colonies, flocking vertebrates, and human crowds as well as any other swarm intelligence systems; • application of biological swarm intelligence models to real-world problems such as distributed computing, data clustering, graph partitioning, optimization and decision making; • theoretical and empirical research in ant colony optimization, particle swarm optimization, swarm robotics, and other swarm intelligence algorithms.