密集環境における編隊飛行のための堅牢で効率的な軌道計画
Robust and Efficient Trajectory Planning for Formation Flight in Dense Environments
密集環境での大規模自律編隊飛行を実現するため、回転・並進・スケール不変な編隊類似度指標と分散軌道最適化、群再編成手法を統合したシステムを提案し、ベンチマークとシミュレーションで有効性を示した。
著者: Lun Quan, Longji Yin, Tingrui Zhang, Mingyang Wang, Ruilin Wang, Sheng Zhong, Zhou Xin, Yanjun Cao, Chao Xu, Fei Gao
分類: cs.RO
原文アブストラクト
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.
