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

arXiv 2511.17765 ↗First posted 21 Nov 2025cs.RO cs.LG cs.MAcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 36 of its 58 references that are in the map. First posted on arXiv 21 Nov 2025.

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36 of 58 references.

References · 36

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24 more
  1. 2020EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environmentscited by 15
  2. 2019Online Trajectory Generation with Distributed Model Predictive Control for Multi-Robot Motion Planningcited by 14
  3. 2021Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environmentscited by 12
  4. 2023Online Control Barrier Functions for Decentralized Multi-Agent Navigationcited by 11
  5. 2023Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learningcited by 8
  6. 2021Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safetycited by 7
  7. 2019Efficient Multi-Agent Trajectory Planning with Feasibility Guarantee using Relative Bernstein Polynomialcited by 6
  8. 2023AMSwarm: An Alternating Minimization Approach for Safe Motion Planning of Quadrotor Swarms in Cluttered Environmentscited by 5
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  10. 2023Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learningcited by 5
  11. 2022Robust MADER: Decentralized and Asynchronous Multiagent Trajectory Planner Robust to Communication Delaycited by 4
  12. 2021Learning Observation-Based Certifiable Safe Policy for Decentralized Multi-Robot Navigationcited by 3
  13. 2022Cooperative Multi-Agent Trajectory Generation with Modular Bayesian Optimizationcited by 3
  14. 2022Robust and Efficient Depth-based Obstacle Avoidance for Autonomous Miniaturized UAVscited by 3
  15. 2023DREAM: Decentralized Real-time Asynchronous Probabilistic Trajectory Planning for Collision-free Multi-Robot Navigation in Cluttered Environmentscited by 3
  16. 2024High-Speed Motion Planning for Aerial Swarms in Unknown and Cluttered Environmentscited by 3
  17. 2021Online Distributed Trajectory Planning for Quadrotor Swarm with Feasibility Guarantee using Linear Safe Corridorcited by 2
  18. 2022Enhanced Decentralized Autonomous Aerial Robot Teams with Group Planningcited by 2
  19. 2022Multi-Robot Trajectory Planning with Feasibility Guarantee and Deadlock Resolution: An Obstacle-Dense Environmentcited by 2
  20. 2023RLSS: Real-time, Decentralized, Cooperative, Networkless Multi-Robot Trajectory Planning using Linear Spatial Separationscited by 2
  21. 2024Decentralized Multi-Agent Trajectory Planning in Dynamic Environments with Spatiotemporal Occupancy Grid Mapscited by 2
  22. 2024Decentralized Nonlinear Model Predictive Control for Safe Collision Avoidance in Quadrotor Teams with Limited Detection Rangecited by 2
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  24. 2025Hierarchical Trajectory (Re)Planning for a Large Scale Swarmcited by 1

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