发表机构
State Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院多光谱信息智能处理技术国家重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
RiskFly提出单阶段规划器，通过视锥对齐的时空风险场预测动态环境中的危险，实现高成功率敏捷飞行。
AI 中文摘要
在未知、杂乱且动态的环境中实现敏捷飞行，要求规划器不仅知道轨迹存在危险，还要知道危险将在何时何地出现。基于单阶段学习的规划器通过可微的特权成本进行训练，速度快且无需专家知识，但到达其编码器的唯一信号是标量轨迹成本，缺乏空间或时间结构，因此避障退化为延迟的反应性机动。我们提出了RiskFly，一种在与行动相同的空间中预测风险的单阶段规划器。双流观测将短深度序列与视锥对齐的倒置球形距离图序列配对，其角度单元与末端状态提议一一对应。辅助头回归视锥对齐的时空风险场，由特权最近接近点（CPA）目标监督。该自预测场在实例化的五次轨迹上以其自身到达时间进行可微查询，并进入训练目标，从而使表示监督和规划梯度在单一空间中汇合。部署时丢弃特权信号，规划器仅依靠机载深度和本体感觉实现无地图运行。在资源受限平台上的大量仿真和零样本真实世界飞行表明，在相当的端到端延迟下成功率更高。
英文摘要
Agile flight in unknown, cluttered, and dynamic environments requires a planner that knows where and when danger will appear, not only that a trajectory is dangerous. One-stage learning-based planners trained with differentiable privileged costs are fast and expert-free, but the only signal reaching their encoder is a scalar trajectory cost with no spatial or temporal structure, so avoidance degrades into late reactive maneuvers. We present RiskFly, a one-stage planner that predicts risk in the same space in which it acts. A dual-stream observation pairs a short depth sequence with a frustum-aligned inverted spherical range-map sequence, whose angular cells match the end-state proposals one to one. An auxiliary head regresses a frustum-aligned spatio-temporal risk field, supervised by a privileged closest-point-of-approach (CPA) target. This self-predicted field is queried differentiably along the instantiated quintic trajectory at its own arrival times, and also enters the training objective, so representation supervision and planning gradients meet in a single space. Privileged signals are discarded at deployment, and the planner runs map-free from onboard depth and proprioception. Extensive simulation and zero-shot real-world flights on resource-constrained platforms show higher success rates at comparable end-to-end latency.
