Formation flight has a vast potential for aerial robot swarms in various applications. However, existing methods lack the capability to achieve fully autonomous large-scale formation flight in dense environments. To bridge the gap, we present a complete formation flight system that effectively integrates real-world constraints into aerial formation navigation. This paper proposes a differentiable graph-based metric to quantify the overall similarity error between formations. This metric is invariant to rotation, translation, and scaling, providing more freedom for formation coordination. We design a distributed trajectory optimization framework that considers formation similarity, obstacle avoidance, and dynamic feasibility. The optimization is decoupled to make large-scale formation flights computationally feasible. To improve the elasticity of formation navigation in highly constrained scenes, we present a swarm reorganization method that adaptively adjusts the formation parameters and task assignments by generating local navigation goals. A novel swarm agreement strategy called global-remap-local-replan and a formation-level path planner is proposed in this work to coordinate the global planning and local trajectory optimizations. To validate the proposed method, we design comprehensive benchmarks and simulations with other cutting-edge works in terms of adaptability, predictability, elasticity, resilience, and efficiency. Finally, integrated with palm-sized swarm platforms with onboard computers and sensors, the proposed method demonstrates its efficiency and robustness by achieving the largest scale formation flight in dense outdoor environments.

翻译：编队飞行在各类应用中蕴含着巨大潜力，尤其适用于空中机器人集群。然而现有方法尚无法实现密集环境中完全自主的大规模编队飞行。为弥合这一差距，我们提出了一套完整的编队飞行系统，能够有效将现实世界约束融入空中编队导航。本文提出了一种可微分的基于图结构的度量方法，用于量化编队间的整体相似度误差。该度量对旋转、平移和缩放具有不变性，为编队协调提供了更大自由度。我们设计了一个分布式轨迹优化框架，综合考虑编队相似度、避障和动态可行性。通过解耦优化使得大规模编队飞行在计算上成为可能。为增强高度受限场景中编队导航的弹性，我们提出了一种集群重组方法，通过生成局部导航目标自适应调整编队参数与任务分配。本文提出了一种名为"全局重映射-局部重规划"的新型集群协调策略，并设计了编队级路径规划器，用以协调全局规划与局部轨迹优化。为验证所提方法，我们与其他前沿工作在设计适应性、可预测性、弹性、鲁棒性和效率等方面进行了综合基准测试与仿真。最终，集成搭载机载计算与传感器的手掌大小集群平台后，所提方法在密集户外环境中实现了最大规模的编队飞行，验证了其高效性与鲁棒性。