From Bench to Flight: Translating Drone Impact Tests into Operational Safety Limits

2026-02-05

AI Summary

This work addresses the lack of dynamic safety speed-limiting methods for indoor micro aerial vehicles (MAVs) grounded in empirically measured impact risk in human-robot coexistence scenarios. We present the first end-to-end open-source toolchain that enables reproducible impact testing via a compact experimental platform, constructs a data-driven velocity-to-impact impulse mapping model, and integrates a real-time speed-limiting node with a compliance logging system within ROS 2 to close the loop from experimentation to deployment of safety policies. The framework provides standardized procedures, a public dataset, and open-source code to support autonomous certification. Validation across multiple commercial quadrotors and representative indoor environments demonstrates that the generated policies strictly adhere to prescribed impact force constraints while effectively preserving mission execution efficiency.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Application Domains: Mobility, Driving & Flight
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• indoor micro-aerial vehicles
• impact risk
• safety limits
• human proximity
• operational safety

Innovation

• impact testing
• safety governors
• micro-aerial vehicles
• data-driven modeling
• ROS2