发表机构
Differential Robotics; Zhejiang University（微分机器人; 浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
OmniRisk提出全向轨迹风险学习框架，通过离线学习风险场和双分支卷积网络预测，实现四旋翼在高速动态障碍物环境中的高效机载避障，无需在线累积风险。
AI 中文摘要
敏捷四旋翼躲避快速移动障碍物需要预测碰撞并在短反应窗口内选择可行机动。可靠的预测性避障仍然具有挑战性，因为稀疏的距离观测不能直接揭示障碍物运动，而在线轨迹优化器要么随障碍物数量增加而扩展性差，要么以牺牲可靠性为代价在密集、高速遭遇中保持效率。我们提出OmniRisk，一个全向规划框架，离线学习轨迹级风险以实现高效的机载规避。一个固定维度的张量结合LiDAR距离全景、动态掩码和笛卡尔表面速度来联合表示几何和运动。我们构建了一个与障碍物速度对齐的非对称风险场，强调接近的交互并衰减远离的交互。沿预测的相对轨迹累积该风险提供了密集的监督，并阻止障碍物通过后不必要的犹豫。一个双分支圆形卷积网络在单次前向传播中，在全向锚点格上预测候选原语的终端边界状态和动态风险，随后进行选择和所选候选原语的闭式重建。该公式消除了沿轨迹的在线风险累积，并使风险推断成本与障碍物数量无关。OmniRisk实现了高效的机载避障，真实世界飞行展示了在相对遭遇速度高达15米/秒的情况下无需微调即可连续执行规避机动。代码可在https://this https URL获取。
英文摘要
Agile quadrotor avoidance of fast-moving obstacles requires anticipating collisions and selecting feasible maneuvers within short reaction windows. Reliable predictive avoidance remains challenging because sparse range observations do not directly reveal obstacle motion, while online trajectory optimizers either scale poorly with obstacle count or remain efficient at the expense of reliability in dense, high-speed encounters. We present OmniRisk, an omnidirectional planning framework that learns trajectory-level risk offline for efficient onboard evasion. A fixed-dimensional tensor combines LiDAR range panoramas, dynamic masks, and Cartesian surface velocities to represent geometry and motion jointly. We formulate an asymmetric risk field aligned with obstacle velocity that emphasizes approaching interactions and attenuates receding ones. Accumulating this risk along predicted relative trajectories provides dense supervision and discourages unnecessary hesitation after obstacles pass. A dual-branch circular convolutional network predicts terminal boundary states and dynamic risks for candidate primitives over an omnidirectional anchor lattice in a single forward pass, followed by selection and closed-form reconstruction of the selected candidate primitive. This formulation removes online risk accumulation along trajectories and makes risk-inference cost independent of obstacle count. OmniRisk enables efficient onboard avoidance, with real-world flights demonstrating consecutive evasive maneuvers at relative encounter speeds up to 15 m/s without fine-tuning. Code is available at https://github.com/VANdexj/OmniRisk.
Comments8 pages, 5 figures, 6 tables
