用毫米波雷达实现小型无人机实时避障，精度高且延迟低。
RADAR Perception for Dynamic Obstacle Avoidance onboard small-scale Quadrotor UAVs
- 基于雷达与多模型跟踪的轻量级感知系统
- 实测位置误差小于0.15米，烟雾中表现稳定
- 适合对延迟敏感的微型无人机避障场景
小型无人飞行器（UAV）的快速动态障碍物避障不仅需要低延迟控制和执行，还需具备足够探测范围的可靠感知以准确检测障碍物并估计速度。本文首次提出基于毫米波雷达的机载动态避障感知-控制一体化系统。我们推导并分析了感知范围、相对速度与控制延迟之间的时序与空间约束，给出了成功避障的充分条件。系统采用基于交互多模型的轻量级跟踪器，以及基于控制屏障函数的控制器，直接输出规避加速度。在300次实验中，三个不同尺寸物体在明暗环境下位置误差分别小于0.15米、0.93米和0.87米；90次烟雾环境实验也表现出相似的误差分布。在树莓派4B上的机载实现表明，系统端到端感知至指令延迟约为14毫秒，具备实时可行性。代码与全部390次实验数据已公开。
原文摘要 · Abstract (English)
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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