Obstacle avoidance for Unmanned Aerial Vehicles (UAVs) in cluttered environments is significantly challenging. Existing obstacle avoidance for UAVs either focuses on fully static environments or static environments with only a few dynamic objects. In this paper, we take the initiative to consider the obstacle avoidance of UAVs in dynamic cluttered environments in which dynamic objects are the dominant objects. This type of environment poses significant challenges to both perception and planning. Multiple dynamic objects possess various motions, making it extremely difficult to estimate and predict their motions using one motion model. The planning must be highly efficient to avoid cluttered dynamic objects. This paper proposes Fast and Adaptive Perception and Planning (FAPP) for UAVs flying in complex dynamic cluttered environments. A novel and efficient point cloud segmentation strategy is proposed to distinguish static and dynamic objects. To address multiple dynamic objects with different motions, an adaptive estimation method with covariance adaptation is proposed to quickly and accurately predict their motions. Our proposed trajectory optimization algorithm is highly efficient, enabling it to avoid fast objects. Furthermore, an adaptive re-planning method is proposed to address the case when the trajectory optimization cannot find a feasible solution, which is common for dynamic cluttered environments. Extensive validations in both simulation and real-world experiments demonstrate the effectiveness of our proposed system for highly dynamic and cluttered environments.

翻译：无人机在杂乱环境中的避障是一项极具挑战性的任务。现有无人机避障方法要么专注于完全静态环境，要么仅适用于存在少量动态物体的静态环境。本文首次系统性地研究了以动态物体为主体的动态杂乱环境下的无人机避障问题。此类环境对感知与规划均构成重大挑战：多个动态物体具有各异运动模式，导致难以使用单一运动模型对其运动进行估计与预测；规划算法需具备极高效率以规避杂乱动态障碍物。本文提出面向复杂动态杂乱环境无人机飞行的快速自适应感知与规划（FAPP）方法。设计了一种新型高效点云分割策略以区分静态与动态物体；针对多运动模式的动态物体，提出了具有协方差自适应调整的估计方法以实现快速精准运动预测；所提出的轨迹优化算法具有极高效率，可规避快速移动物体；此外，针对动态杂乱环境中轨迹优化常无法找到可行解的情形，提出了自适应重规划方法。仿真与真实世界实验的广泛验证结果表明，本系统在高度动态与杂乱场景中具有显著有效性。