FAPP：动态杂乱环境中无人机的快速自适应感知与规划
FAPP: Fast and Adaptive Perception and Planning for UAVs in Dynamic Cluttered Environments
AI总结：
针对动态杂乱环境下无人机避障的感知与规划难题，提出FAPP框架，通过高效点云分割、协方差自适应运动估计、高效轨迹优化及自适应重规划，经仿真与实实验证了其在高动态杂乱环境中的有效性。
摘要：
无人机（UAV）在杂乱环境中的避障是一项极具挑战性的任务。现有的无人机避障方法要么专注于完全静态的环境，要么仅针对包含少量动态物体的静态环境。本文主动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，这类环境对感知和规划都提出了严峻挑战。多个动态物体具有各异的运动模式，很难用单一运动模型来估计和预测它们的运动；规划模块必须具备极高的效率，才能避开杂乱分布的动态物体。\n本文提出了适用于在复杂动态杂乱环境中飞行的无人机的快速自适应感知与规划框架（FAPP）。我们提出了一种新颖高效的点云分割策略，用于区分静态和动态物体。为应对具有不同运动模式的多个动态物体，本文提出了一种带协方差自适应的自适应估计方法，可快速准确地预测它们的运动。我们提出的轨迹优化算法效率极高，能够避开高速运动的物体。此外，本文还提出了一种自适应重规划方法，用于处理轨迹优化无法找到可行解的情况——这种情况在动态杂乱环境中十分常见。仿真和真实世界实验中的大量验证结果表明，我们提出的系统在高动态、高杂乱度的环境中具备有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
