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.

翻译：近年来，空中机器人领域取得了显著进展，在灾后搜救等多样化场景中展现出应用潜力。尽管取得了这些进步，部署实体多无人机系统所伴随的高昂购置成本仍构成挑战，阻碍了其在研究工作中的广泛使用。为克服这些挑战，我们提出了STAR（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。本框架引入了一种基于Crazyflie的集群架构——Crazyflie是一种低成本、开源、手掌尺寸的空中平台，非常适合用于实验性集群算法。为提升成本效益并缓解在实验中使用低成本机器人的局限性，我们提出了一种基于路标的地标定位模块，该模块利用基准标记实现定位。此模块同时作为目标检测模块，增强了框架的适应性与多功能性。此外，通过速度障碍法实现了碰撞与避障功能。本研究致力于弥合理论进展与实际应用之间的鸿沟，从而推动该领域的发展。