RAVEN：基于开集语义记忆与行为自适应的弹性空中导航
RAVEN: Resilient Aerial Navigation via Open-Set Semantic Memory and Behavior Adaptation
AI总结：
提出 RAVEN 行为树框架，利用三维语义体素-射线记忆、长短程混合搜索和视觉-语言模型线索，实现非结构化户外环境中的鲁棒开集空中语义导航。
摘要：
户外空中语义导航要求机器人探索广阔、非结构化环境以定位目标物体。语义导航的最新进展已在室内场景中展示了开集物体目标导航能力，但这些方法仍受限于有限的空间范围和结构化布局，不适合长距离户外搜索。尽管已有户外语义导航方法，它们要么依赖基于当前观测的反应式策略，容易产生短视行为；要么离线预计算场景图用于导航，限制了对在线部署的适应性。我们提出 RAVEN，这是一个面向非结构化户外环境中空中语义导航、基于三维记忆的行为树框架。它（1）使用空间一致的语义体素-射线地图作为持久记忆，支持长时程规划并避免纯反应式行为；（2）结合短程体素搜索与长程射线搜索以扩展到大型环境；（3）利用大型视觉-语言模型提供辅助线索，缓解户外目标稀疏的问题。这些组件由行为树协调，可自适应切换行为以实现鲁棒运行。我们在 10 个照片级真实感户外仿真环境中评估 RAVEN，涵盖 100 项语义任务，包括单物体搜索、多类别、多实例导航以及顺序任务变化。结果表明，RAVEN 在仿真中比基线性能高出 85.25%，并通过在户外实地测试中部署到空中机器人上证明了其现实适用性。
英文摘要：
Aerial outdoor semantic navigation requires robots to explore large, unstructured environments to locate target objects. Recent advances in semantic navigation have demonstrated open-set object-goal navigation in indoor settings, but these methods remain limited by constrained spatial ranges and structured layouts, making them unsuitable for long-range outdoor search. While outdoor semantic navigation approaches exist, they either rely on reactive policies based on current observations, which tend to produce short-sighted behaviors, or precompute scene graphs offline for navigation, limiting adaptability to online deployment. We present RAVEN, a 3D memory-based, behavior tree framework for aerial semantic navigation in unstructured outdoor environments. It (1) uses a spatially consistent semantic voxel-ray map as persistent memory, enabling long-horizon planning and avoiding purely reactive behaviors, (2) combines short-range voxel search and long-range ray search to scale to large environments, (3) leverages a large vision-language model to suggest auxiliary cues, mitigating sparsity of outdoor targets. These components are coordinated by a behavior tree, which adaptively switches behaviors for robust operation. We evaluate RAVEN in 10 photorealistic outdoor simulation environments over 100 semantic tasks, encompassing single-object search, multi-class, multi-instance navigation and sequential task changes. Results show RAVEN outperforms baselines by 85.25% in simulation and demonstrate its real-world applicability through deployment on an aerial robot in outdoor field tests.
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。
