Abstract
Indoor micro-aerial vehicles (MAVs) are increasingly used for tasks that require close proximity to people, yet practitioners lack practical methods to tune motion limits based on measured impact risk. We present an end-to-end, open toolchain that converts benchtop impact tests into deployable safety governors for drones. First, we describe a compact and replicable impact rig and protocol for capturing force-time profiles across drone classes and contact surfaces. Second, we provide data-driven models that map pre-impact speed to impulse and contact duration, enabling direct computation of speed bounds for a target force limit. Third, we release scripts and a ROS2 node that enforce these bounds online and log compliance, with support for facility-specific policies. We validate the workflow on multiple commercial off-the-shelf quadrotors and representative indoor assets, demonstrating that the derived governors preserve task throughput while meeting force constraints specified by safety stakeholders. Our contribution is a practical bridge from measured impacts to runtime limits, with shareable datasets, code, and a repeatable process that teams can adopt to certify indoor MAV operations near humans.
Abstract (translated)
室内微型航空车辆（MAVs）在需要与人近距离接触的任务中使用得越来越频繁，然而操作者缺乏基于实际冲击风险测量来调整运动限制的实用方法。我们提出了一种端到端、开放式的工具链，能够将实验台上的碰撞测试数据转化为无人机可部署的安全控制器。 首先，我们描述了一个紧凑且易于复制的碰撞测试装置和协议，用于捕捉不同类型的无人机及接触面在碰撞过程中的力-时间曲线。 其次，我们提供了一些基于数据驱动模型的方法，这些方法可以将预碰撞速度映射到冲量和接触持续时间上，从而直接计算出达到特定力限制所需的最高速度阈值。 最后，我们发布了一系列脚本以及一个ROS2节点，用于在线执行这些速度边界并记录合规情况，并支持不同设施的具体政策。 我们在多个商用现成的四旋翼无人机及代表性的室内设备上验证了这一工作流程的有效性。结果表明，由此衍生出的安全控制器能够保证任务吞吐量的同时满足安全利益相关方所指定的力量限制条件。我们的贡献在于提供了一座从实际碰撞测量到运行时限制之间实用桥梁，通过共享的数据集、代码和可重复的过程，团队可以采用这些方法来认证室内MAVs在人类周围的操作安全性。
URL
https://arxiv.org/abs/2602.05922
