STAR：面向空中机器人研究的集群技术
STAR: Swarm Technology for Aerial Robotics Research
AI总结：
针对多无人机实体系统成本高阻碍空中集群研究的问题，本文提出基于低成本开源Crazyflie平台的STAR框架，搭配路标定位模块与速度障碍避障方案，降低空中集群实验门槛，推动理论落地。
摘要：
近年来，空中机器人领域取得了显著进展，已在包括灾后搜救行动在内的多个领域得到应用。尽管已有这些进步，但部署实体多无人机系统的购置成本过高，带来了诸多挑战，阻碍了其在研究工作中的广泛应用。为克服这些挑战，我们提出了STAR（Swarm Technology for Aerial Robotics Research，面向空中机器人研究的集群技术），这是一个专为提升空中集群研究实验可及性而开发的框架。我们的框架引入了一种基于Crazyflie的集群架构，Crazyflie是一款低成本、开源的手掌大小空中平台，非常适合用于集群算法实验。为提升成本效益并缓解实验中使用低成本机器人的局限性，我们提出了一种利用基准标记的基于路标的定位模块。该模块同时可作为目标检测模块，增强了框架的适应性和多功能性。此外，我们通过速度障碍法实现了碰撞与障碍物规避。本研究致力于弥合理论进展与实际实现之间的差距，从而推动该领域的发展。
英文摘要：
In recent years, the field of aerial robotics has witnessed significant progress, finding applications in diverse domains, including post-disaster search and rescue operations. Despite these strides, the prohibitive acquisition costs associated with deploying physical multi-UAV systems have posed challenges, impeding their widespread utilization in research endeavors. To overcome these challenges, we present STAR (Swarm Technology for Aerial Robotics Research), a framework developed explicitly to improve the accessibility of aerial swarm research experiments. Our framework introduces a swarm architecture based on the Crazyflie, a low-cost, open-source, palm-sized aerial platform, well suited for experimental swarm algorithms. To augment cost-effectiveness and mitigate the limitations of employing low-cost robots in experiments, we propose a landmark-based localization module leveraging fiducial markers. This module, also serving as a target detection module, enhances the adaptability and versatility of the framework. Additionally, collision and obstacle avoidance are implemented through velocity obstacles. The presented work strives to bridge the gap between theoretical advances and tangible implementations, thus fostering progress in the field.
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