用轻量强化学习让微型无人机在复杂环境中自主飞行
LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigation
- 分两阶段设计，结合低分辨率传感器与轻量注意力策略
- 仿真中比顶尖方法快10%，仅需极少算力资源
- 实测六架微型无人机全机载飞行，最高速2.0米/秒
纳米级无人机集群具备高机动性，但受限于机载感知、通信和计算能力，传统方法难以应用。现有方案依赖高分辨率视觉或高耗能规划器，不适用于此类平台。我们提出LEARN，一种轻量级、两阶段的安全引导强化学习框架，用于复杂空间中的多无人机导航。系统融合低分辨率飞行时间（ToF）传感器与简单运动规划器，搭配紧凑的注意力型强化学习策略。仿真结果显示，LEARN相比两种先进规划器性能提升10%，资源消耗显著降低。我们在六架Crazyflie四旋翼上验证了可行性，在多种室内外环境中实现全机载飞行，最高时速达2.0米/秒，可穿越0.2米宽缝隙。
原文摘要 · Abstract (English)
Nano-UAV teams offer great agility yet face severe navigation challenges due to constrained onboard sensing, communication, and computation. Existing approaches rely on high-resolution vision or compute-intensive planners, rendering them infeasible for these platforms. We introduce LEARN, a lightweight, two-stage safety-guided reinforcement learning (RL) framework for multi-UAV navigation in cluttered spaces. Our system combines low-resolution Time-of-Flight (ToF) sensors and a simple motion planner with a compact, attention-based RL policy. In simulation, LEARN outperforms two state-of-the-art planners by $10\%$ while using substantially fewer resources. We demonstrate LEARN's viability on six Crazyflie quadrotors, achieving fully onboard flight in diverse indoor and outdoor environments at speeds up to $2.0 m/s$ and traversing $0.2 m$ gaps.
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