Data from: Optimized flocking of autonomous drones in confined environments

We address a fundamental issue of collective motion of aerial robots: how to ensure that large flocks of autonomous drones seamlessly navigate in confined spaces. The numerous existing flocking models are rarely tested on actual hardware because they typically neglect some crucial aspects of multirobot systems. Constrained motion and communication capabilities, delays, perturbations, or the presence of barriers should be modeled and treated explicitly because they have large effects on collective behavior during the cooperation of real agents. Handling these issues properly results in additional model complexity and a natural increase in the number of tunable parameters, which calls for appropriate optimization methods to be coupled tightly to model development. In this paper, we propose such a flocking model for real drones incorporating an evolutionary optimization framework with carefully chosen order parameters and fitness functions. We numerically demonstrated that the induced swarm behavior remained stable under realistic conditions for large flock sizes and notably for large velocities. We showed that coherent and realistic collective motion patterns persisted even around perturbing obstacles. Furthermore, we validated our model on real hardware, carrying out field experiments with a self-organized swarm of 30 drones. This is the largest of such aerial outdoor systems without central control reported to date exhibiting flocking with collective collision and object avoidance. The results confirmed the adequacy of our approach. Successfully controlling dozens of quadcopters will enable substantially more efficient task management in various contexts involving drones.本研究针对空中机器人集群运动的核心问题展开探讨：如何保障大规模自主无人机集群在受限空间中实现无缝流畅的导航。现有诸多集群模型鲜少在实体硬件平台上开展测试，原因在于这类模型往往忽略了多机器人系统的若干关键属性。受限运动与通信能力、通信延迟、外部扰动以及障碍物的存在，均需被显式建模与处理——这些因素会对真实智能体协作过程中的集群行为产生显著影响。妥善处理上述问题会提升模型的复杂度，同时自然增加可调参数的数量，因此需要将适配的优化方法与模型开发进行紧密耦合。 本文提出一款面向真实无人机的集群模型，该模型集成了经精心选取序参数与适应度函数的进化优化框架。我们通过数值仿真验证：所诱导的集群行为在贴近实际的工况下，即使面对大规模集群与高飞行速度场景，仍能保持稳定。实验表明，即便在存在扰动性障碍物的环境中，集群仍能维持连贯且符合实际的集体运动模式。此外，我们在实体硬件平台上对该模型进行了验证，通过由30架无人机组成的自组织集群开展了户外实地实验。据现有公开报道，这是规模最大的无中心控制空中集群系统，可实现兼具集体碰撞规避与障碍物规避功能的集群飞行。实验结果证实了我们所提方法的有效性与适用性。成功实现数十架四旋翼无人机的协同管控，将能够在诸多涉及无人机的应用场景中大幅提升任务执行效率。