LNN-Fly：时序失配下鲁棒避障的连续时间无人机导航
LNN-Fly: Continuous-Time UAV Navigation for Robust Obstacle Avoidance under Timing Mismatch
AI总结：
提出LNN-Fly，一种面向部署的连续时间导航策略，通过动态规划启发的结构化循环更新、显式控制间隔条件和自适应遗忘门，在时序失配下实现鲁棒避障，并在仿真和实物中验证了有效性。
摘要：
端到端无人机导航在仿真中可实现令人印象深刻的敏捷性，但其避障行为在部署后往往会退化，因为策略必须容忍仿真器失配、感知不规则和变速率控制。这些效应在杂乱环境中尤其危险，过时的观测或短暂的控制不规则可能直接导致碰撞。我们提出LNN-Fly，一种面向部署的连续时间导航策略，用于基于LiDAR的无人机避障。该策略结合了动态规划启发的结构化循环更新、对已过控制间隔Δt的显式条件化，以及一个输入驱动的自适应遗忘门，该门在危险附近刷新过时的潜在状态，同时在持续机动中保持一致性。它通过包含部署相关感知和时间扰动的可微轨迹进行训练。在仿真中，LNN-Fly在测试设置中提高了避障性能，并显示出对降低控制频率、稀疏观测和控制周期抖动的更好容忍度。它还能从简化的可微仿真器零样本迁移到物理四旋翼。在室内跨频率真实世界测试中，系统在20次飞行中实现了100%的成功率，而策略推理在桌面GPU上的中位延迟为0.514毫秒，在机载CPU上约为2.5毫秒，机载P95延迟低于30毫秒。
英文摘要：
End-to-end unmanned aerial vehicle (UAV) navigation can achieve impressive agility in simulation, yet its obstacle-avoidance behavior often degrades after deployment because the policy must tolerate simulator mismatch, sensing irregularity, and variable-rate control. These effects are especially dangerous in cluttered environments, where stale observations or short control irregularities can directly lead to collisions. We present LNN-Fly, a deployment-oriented continuous-time navigation policy for LiDAR-based UAV obstacle avoidance. The policy combines a dynamic-programming-inspired structured recurrent update, explicit conditioning on the elapsed control interval Δt, and an input-driven adaptive forgetting gate that refreshes stale latent state near hazards while preserving consistency during sustained maneuvers. It is trained with differentiable rollouts that incorporate deployment-relevant sensing and timing perturbations. In simulation, LNN-Fly improves obstacle-avoidance performance in the tested settings and shows better tolerance to reduced control frequency, sparse observations, and control-period jitter. It also transfers zero-shot from a simplified differentiable simulator to a physical quadrotor. In indoor cross-frequency real-world tests, the system achieves 100% success over 20 flights, while policy inference has a median latency of 0.514 ms on a desktop graphics processing unit (GPU) and about 2.5 ms on the onboard central processing unit (CPU), with onboard P95 latency below 30 ms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
