HEPP：用于无人机高速避障的超高效感知与规划
HEPP: Hyper-efficient Perception and Planning for High-speed Obstacle Avoidance of UAVs
AI总结：
本文提出HEPP系统，通过增量建图、拓扑路径搜索和自适应轨迹生成，实现无人机在杂乱环境中高速避障，耗时显著降低。
摘要：
在杂乱环境中实现无人驾驶飞行器（UAV）的高速避障是一项重大挑战。现有的无人机规划与避障系统只能在中等速度下飞行，或只能在空旷或稀疏区域高速飞行。本文提出了一种用于无人机高速避障的超高效感知与规划系统。该系统主要由三个模块组成：1）一种新颖的增量式机器人中心建图方法，包含距离和梯度信息，与现有方法相比耗时减少89.5%；2）一种新颖的障碍物感知拓扑路径搜索方法，可生成多条不同路径；3）一种基于自适应梯度的快速轨迹生成方法，并配有新颖的时间预分配算法。凭借这些创新，该系统具有出色的实时性能，每次迭代仅需毫秒级延迟，在高速（杂乱环境中15米/秒）下比现有方法耗时减少79.24%，使无人机能够在杂乱环境中快速飞行并避开障碍物。无人机规划的轨迹在时间和空间域上都接近全局最优。最后，在仿真和真实世界实验中的广泛验证证明了我们提出的系统在杂乱环境中进行高速导航的有效性。
英文摘要：
High-speed obstacle avoidance of uncrewed aerial vehicles (UAVs) in cluttered environments is a significant challenge. Existing UAV planning and obstacle avoidance systems can only fly at moderate speeds or at high speeds over empty or sparse fields. In this article, we propose a hyper-efficient perception and planning system for the high-speed obstacle avoidance of UAVs. The system mainly consists of three modules: 1) A novel incremental robocentric mapping method with distance and gradient information, which takes 89.5% less time compared to existing methods. 2) A novel obstacle-aware topological path search method that generates multiple distinct paths. 3) An adaptive gradient-based high-speed trajectory generation method with a novel time pre-allocation algorithm. With these innovations, the system has an excellent real-time performance with only milliseconds latency in each iteration, taking 79.24% less time than existing methods at high speeds (15 m/s in cluttered environments), allowing UAVs to fly swiftly and avoid obstacles in cluttered environments. The planned trajectory of the UAV is close to the global optimum in both temporal and spatial domains. Finally, extensive validations in both simulation and real-world experiments demonstrate the effectiveness of our proposed system for high-speed navigation in 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。
