Threat-Aware UAV Dodging of Human-Thrown Projectiles with an RGB-D Camera

TL;DR

This paper presents a real-time UAV dodging system using RGB-D cameras that predicts projectile trajectories based on human pose estimation and depth data, enabling agile and reliable dodging of human-thrown projectiles.

Contribution

It introduces a novel perception and dodging framework combining human pose estimation with depth data and an uncertainty-aware strategy for fast, accurate projectile avoidance.

Findings

High prediction accuracy surpassing baselines

Effective dodging at low latency

Robust performance across diverse scenarios

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 movements 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.