提升无人机在大小障碍物混合环境中的避障能力
CaMeRL: Collision-Aware and Memory-Enhanced Reinforcement Learning for UAV Navigation in Multi-Scale Obstacle Environments
- 融合碰撞感知与时序记忆的强化学习框架
- 在超小与特大障碍物场景中成功率提升0.48和0.28
- 适合复杂室外动态环境下的无人机自主导航
在无人飞行器（UAV）避障导航中，障碍物尺度变化的重要性远低于障碍物数量或密度，但现有方法通常仅从单帧深度图提取几何特征，忽略小障碍物且在大障碍物遮挡下丢失空间上下文，导致多尺度障碍物环境中性能显著下降。为此，我们提出CaMeRL——一种碰撞感知与记忆增强的强化学习框架。其碰撞感知隐式表示编码风险敏感的深度线索，保留细粒度障碍结构，提升对小障碍物的敏感性；时序记忆模块跨帧整合观测，缓解大障碍物遮挡带来的部分可观测性问题。我们在包含超小与特大障碍物设置的多尺度障碍物环境中评估了CaMeRL，结果表明其在所有尺度上均优于现有先进基线，在超小与特大障碍物设置中成功率分别提升0.48和0.28。更重要的是，该方法在杂乱室外环境中实现了可靠导航。
原文摘要 · Abstract (English)
In obstacle avoidance navigation of unmanned aerial vehicles (UAVs), variations in obstacle scale have received strangely 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 CaMeRL, 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 CaMeRL with multi-scale obstacles, including ultra-small and extra-large obstacle settings. Results show that CaMeRL outperforms state-of-the-art baselines across all scales, with success rate gains of 0.48 and 0.28 in the ultra-small and extra-large settings, respectively. More importantly, CaMeRL achieves reliable navigation in cluttered outdoor environments.
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