Threat-Aware UAV Dodging of Human-Thrown Projectiles with an RGB-D Camera
Yuying Zhang, Na Fan, Haowen Zheng, Junning Liang, Zongliang Pan, Qifeng Chen, Ximin Lyu
AI summary
Problem
UAVs face critical safety risks from sudden human-initiated projectile attacks, but existing avoidance methods suffer from high perception latency, unpredictable threat timing, and heavy computational demands that hinder real-time onboard deployment.
Approach
The framework uses an RGB-D camera to estimate human joint poses and predict projectile trajectories before release, then applies an uncertainty-aware optimization strategy to generate safe evasion paths while accounting for prediction errors.
Key results
- 6-meter effective detection range and 26.4 ms latency on CPU-only hardware
- Ivory-shaped uncertainty model for timing and trajectory prediction errors
- Real-time trajectory optimization penalizing proximity to surviving projectile paths
- Robust real-world dodging across varying speeds, lighting, occlusions, and multiple threats
Why it matters
Provides a lightweight, generalizable solution for UAV safety against intentional human threats, enabling safer deployment in public events, delivery, and aerial operations.
Abstract
Uncrewed aerial vehicles (UAVs) performing tasks such as transportation and aerial photography are vulnerable to intentional projectile attacks from humans. Dodging such a sudden and fast projectile poses a significant challenge for UAVs, requiring ultra-low latency responses and agile maneuvers. Drawing inspiration from baseball, in which pitchers’ body move- ments are analyzed to predict the ball’s trajectory, we propose a novel real-time dodging system that leverages an RGB-D camera. Our approach integrates human pose estimation with depth information to predict the attacker’s motion trajectory and the subsequent projectile trajectory. Additionally, we introduce an uncertainty-aware dodging strategy to enable the UAV to dodge incoming projectiles efficiently. Our perception system achieves high prediction accuracy and outperforms the baseline in effective distance and latency. The dodging strategy addresses temporal and spatial uncertainties to ensure UAV safety. Extensive real- world experiments demonstrate the framework’s reliable dodging capabilities against sudden attacks and its outstanding robustness across diverse scenarios.