CaMeRL：面向多尺度障碍环境的无人机导航碰撞感知与记忆增强强化学习
CMRL: Collision-Aware and Memory-Enhanced Reinforcement Learning for UAV Navigation in Multi-Scale Obstacle Environments
AI总结：
针对无人机多尺度障碍导航中障碍尺度变化被忽视的问题，提出CaMeRL框架，通过碰撞感知和记忆增强提升对小障碍物的识别与复杂环境的导航能力。
摘要：
在无人机避障导航中，障碍尺度的变化比障碍数量或密度受到更少关注。现有方法通常从单帧深度观测中提取纯几何特征，此类表示容易忽视小障碍物并在大障碍遮挡下丢失空间上下文，导致多尺度障碍环境性能下降。为此，我们提出CaMeRL，一种面向无人机导航的碰撞感知与记忆增强强化学习框架。碰撞感知的潜在表示编码风险敏感的深度线索以保留细粒度障碍结构，从而提升对小障碍物的敏感性。时间记忆模块整合跨帧观测，缓解大障碍遮挡导致的部分可观测性问题。我们使用多尺度障碍进行评估，包括超小和超大障碍设置。结果表明，CaMeRL在所有尺度上均优于现有最佳基线，超小和超大设置的成功率分别提高了0.48和0.28。更重要的是，CaMeRL在杂乱的户外环境中实现了可靠的导航。
英文摘要：
In obstacle avoidance navigation of unmanned aerial vehicles (UAVs), variations in obstacle scale have received less attention than obstacle number or density. Existing methods typically extract purely geometric features from single-frame depth observations. Such representations tend to neglect small obstacles and lose spatial context under occlusions caused by large obstacles, leading to noticeable degradation in environments with multi-scale obstacles. To address this issue, we propose CMRL, a Collision-aware and Memory-enhanced Reinforcement Learning framework for UAV navigation. The collision-aware latent representation encodes risk-sensitive depth cues to preserve fine-grained obstacle structures, thereby improving sensitivity to small obstacles. The temporal memory module integrates observations across frames, mitigating partial observability caused by large-obstacle occlusions. We evaluate CMRL with multi-scale obstacles, including ultra-small and extra-large obstacle settings. Results show that CMRL outperforms state-of-the-art baselines across all scales, with success rate gains of 0.47 and 0.29 in the ultra-small and extra-large settings, respectively. More importantly, CMRL achieves reliable navigation in cluttered outdoor environments. The code is available at https://honghongdev.github.io/camerl/
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。
