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

Aziz Mohamed Mili, Louis Catar, Paul Gérard, Ilyass Tabiai, David St-Onge

TL;DR

This paper tackles the problem of certifying indoor drone operations near humans by turning bench-top impact data into runtime safety constraints. It introduces a compact, open-toolchain that includes a dedicated impact bench, a diverse set of drone specimens, data-driven models (mapping speed to impulse and contact duration), and a ROS 2 safety governor that enforces speed limits to meet target force bounds. Key contributions include a rebound-aware indoor impact dataset across multiple platforms, regression-based impact maps, and an end-to-end governor that preserves task throughput while satisfying safety requirements. The approach provides a practical, certifiable pathway for indoor MAV operations near people, with shared datasets, code, and a repeatable process that facilities can adopt to meet policy-specific safety standards.

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.

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

TL;DR

This paper tackles the problem of certifying indoor drone operations near humans by turning bench-top impact data into runtime safety constraints. It introduces a compact, open-toolchain that includes a dedicated impact bench, a diverse set of drone specimens, data-driven models (mapping speed to impulse and contact duration), and a ROS 2 safety governor that enforces speed limits to meet target force bounds. Key contributions include a rebound-aware indoor impact dataset across multiple platforms, regression-based impact maps, and an end-to-end governor that preserves task throughput while satisfying safety requirements. The approach provides a practical, certifiable pathway for indoor MAV operations near people, with shared datasets, code, and a repeatable process that facilities can adopt to meet policy-specific safety standards.

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.
Paper Structure (21 sections, 6 equations, 7 figures, 3 tables)

This paper contains 21 sections, 6 equations, 7 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. METHODS AND MATERIAL
  4. Custom Impact bench
  5. Test Specimens
  6. Material Characterization
  7. Impact Conditions and calibration
  8. Statistical Analysis and Curve Fitting
  9. Safety Governor force estimation
  10. RESULTS
  11. Baseline Frontal Impact Performance (0° Configuration)
  12. Peak Impact Force Distribution and Safety Thresholds
  13. Contact Duration Independence from Velocity
  14. Critical Trade-off: Peak Force versus Rebound Energy
  15. Angular Impact Effects and Real-World Relevance
  16. ...and 6 more sections

Figures (7)

  • Figure 1: UAVs considered for this study, from left to right: Bamboo Cognifly, Carbon Cognifly, DJI Avata, and Flywoo Flylens.
  • Figure 2: Overview of the custom impact test bench. The main rail (green) is fixed and used to propel the mobile rail (red). The sample trolley (yellow) holds the drone. Just before impact, it is released by the solenoid (gray) and translates freely along the red rail.
  • Figure 3: Force data sample from a carbon Cognifly impact showing at least 3 stages impact with restitution of the force plate in-between.
  • Figure 4: Safety governor block diagram: /odometry and /range yield speed $v$; bench-derived regressors define the impact map $F(v)$; a root-solver computes $v_{\max}$ for a target force; the governor publishes /cmd_vel_limited.
  • Figure 5: Peak impact forces at 0° orientation
  • ...and 2 more figures