发表机构
University of Klagenfurt（克拉根福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对小型四旋翼无人机，提出首个毫米波RADAR感知与控制系统，结合交互多模型跟踪器与控制障碍函数控制器，实现快速动态避障，在多环境实验中精度达标，端到端延迟约14ms。
AI 中文摘要
无人机上的快速动态避障（DOA）不仅需要低延迟的控制与作动，还需要具备足够感知范围的可靠感知，以实现精确的障碍物检测与速度估计。本文首次提出基于毫米波RADAR的感知与控制系统，用于快速机载DOA。我们推导并分析了与感知范围、相对速度和控制延迟相关的延迟与空间边界，得出成功避障的充分条件。该系统采用基于交互多模型的轻量型跟踪器，以及基于控制障碍函数的控制器，可直接输出避障加速度。在300次不同物体尺寸、不同可见度（明暗环境）的实验中，系统在x、y、z方向的位置误差分别小于0.15m、0.93m和0.87m；在90次烟雾环境实验中，误差分布类似。基于Raspberry Pi 4B的机载实现验证了实时可行性，端到端的感知到指令延迟约为14ms。代码及包含390次抛投的完整数据集可获取（此https URL）。
英文摘要
Fast dynamic obstacle avoidance (DOA) on uncrewed aerial vehicles (UAVs) demands not only low-latency control and actuation but also reliable perception with sufficient sensing range for accurate obstacle detection and speed estimation. This letter presents, to the best of our knowledge, the first mmWave RADAR-based perception-and-control system for fast onboard DOA. We derive and analyze latency and spatial bounds that relate sensing range, relative speed, and control delay, yielding sufficient conditions for successful avoidance. Our system adopts a lightweight tracker based on interacting multiple models and a controller based on control-barrier functions that directly outputs evasive accelerations. It achieves position errors of less than 0.15 m, 0.93 m, and 0.87 m in x, y, and z directions for 300 experiments with three different object sizes and varying visibility (light and dark), and a similar spread for 90 experiments in smoke. An onboard implementation on a Raspberry Pi 4B demonstrates real-time feasibility with an end-to-end sensing-to-command latency of approximately 14 ms. Code and the full dataset of 390 throws are available (https://tinyurl.com/radardoagit).
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