让越野车在空中精准操控，高速飞越障碍不落地
Dom, cars don't fly! -- Or do they? In-Air Vehicle Maneuver for High-Speed Off-Road Navigation
- 结合物理与机器学习构建混合动力模型，预测空中轨迹
- 通过油门和转向控制，实现短时飞行中精准着陆
- 适合追求极限速度的无人越野系统开发者
在高速非结构化地形导航中，车辆难免会短暂离地。在时间敏感任务中，直接飞越障碍比绕行或缓慢通过更高效。然而，多数自主系统假设车辆始终接地，限制了行驶速度。本文提出一种高动态越野飞行中的车辆操控新方法：基于融合物理与机器学习的混合前向动力学模型，采用固定时域采样规划器，仅通过现有油门与转向指令，在短时空中确保车辆着陆姿态及其导数的精确性。我们在室内外多场景开展飞行实验，对比基于误差驱动的控制方法，验证了利用现有地面车辆控制器实现精准及时空中操控的可行性。
原文摘要 · Abstract (English)
When pushing the speed limit for aggressive off-road navigation on uneven terrain, it is inevitable that vehicles may become airborne from time to time. During time-sensitive tasks, being able to fly over challenging terrain can also save time, instead of cautiously circumventing or slowly negotiating through. However, most off-road autonomy systems operate under the assumption that the vehicles are always on the ground and therefore limit operational speed. In this paper, we present a novel approach for in-air vehicle maneuver during high-speed off-road navigation. Based on a hybrid forward kinodynamic model using both physics principles and machine learning, our fixed-horizon, sampling-based motion planner ensures accurate vehicle landing poses and their derivatives within a short airborne time window using vehicle throttle and steering commands. We test our approach in extensive in-air experiments both indoors and outdoors, compare it against an error-driven control method, and demonstrate that precise and timely in-air vehicle maneuver is possible through existing ground vehicle controls.
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