LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigation

• URL: http://arxiv.org/abs/2511.17765v1
• Date: Fri, 21 Nov 2025 20:29:03 GMT
• Title: LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigation
• Abstract summary: LEARN is a lightweight, two-stage safety-guided reinforcement learning framework for multi-UAV navigation.<n>Our system combines low-resolution Time-of-Flight (ToF) sensors and a simple motion planner with a compact, attention-based RL policy.<n>We demonstrate LEARN's viability on six Crazyflie quadrotors, achieving fully onboard flight in diverse indoor and outdoor environments.
• Score: 15.483749658372124
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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