In this paper, we address the problem of online quadrotor whole-body motion planning (SE(3) planning) in unknown and unstructured environments. We propose a novel multi-resolution search method, which discovers narrow areas requiring full pose planning and normal areas requiring only position planning. As a consequence, a quadrotor planning problem is decomposed into several SE(3) (if necessary) and R^3 sub-problems. To fly through the discovered narrow areas, a carefully designed corridor generation strategy for narrow areas is proposed, which significantly increases the planning success rate. The overall problem decomposition and hierarchical planning framework substantially accelerate the planning process, making it possible to work online with fully onboard sensing and computation in unknown environments. Extensive simulation benchmark comparisons show that the proposed method is one to several orders of magnitude faster than the state-of-the-art methods in computation time while maintaining high planning success rate. The proposed method is finally integrated into a LiDAR-based autonomous quadrotor, and various real-world experiments in unknown and unstructured environments are conducted to demonstrate the outstanding performance of the proposed method.

翻译：本文针对未知非结构化环境中四旋翼飞行器的在线全身运动规划（SE(3)规划）问题展开研究。我们提出了一种新颖的多分辨率搜索方法，该方法能够识别需要进行完整位姿规划的狭窄区域与仅需位置规划的正常区域。由此，四旋翼飞行器的规划问题被分解为若干SE(3)子问题（必要时）和R³子问题。为了穿越所识别的狭窄区域，我们提出了一种针对狭窄区域精心设计的走廊生成策略，显著提升了规划成功率。整体问题分解与分层规划框架极大加速了规划进程，使其能够在未知环境中依托完全机载感知与计算实现在线运行。广泛的仿真基准比较表明，所提方法在保持高规划成功率的同时，计算速度比现有最先进方法提升一至数个数量级。最终将该方法集成于基于LiDAR的自主四旋翼平台，并在未知非结构化环境中进行了多种真实世界实验，充分验证了所提方法的卓越性能。