一句话：提出进化算法模糊测试框架 DPFuzzer，以环境风险因子引导生成多样关键场景，自动发现无人机路径规划器安全漏洞。
无人机路径规划器需在无 GPS、未知且杂乱的环境中自主避障，但作者发现规划器在特定场景下会判断失误并引发碰撞。现有无人机安全测试研究主要针对飞控程序，难以生成多样化的障碍场景来测试路径规划器。本文提出 DPFuzzer，一个基于进化算法（EA）的自动化测试框架，通过持续生成可触发漏洞的多样化关键场景来发现路径规划器中的安全隐患。为更好地引导关键场景生成，作者提出环境风险因子（Environmental Risk Factor, ERF）这一度量，用于抽象场景中潜在的安全威胁。作者在最先进的无人机路径规划器上评估 DPFuzzer，实验表明其能有效发现多种漏洞，并进一步证明这些漏洞在真实世界中可被利用。
State-of-the-art drone path planners enable drones to autonomously travel through obstacles in GPS-denied, uncharted, cluttered environments. However, our investigation shows that path planners fail to maneuver drones correctly in specific scenarios, leading to incidents such as collisions. To minimize such risks, drone path planners should be tested thoroughly against diverse scenarios before deployment. Existing research for testing drones to uncover safety-critical vulnerabilities is only focused on flight control programs and is limited in the capability to generate diverse obstacle scenarios for testing drone path planners. In this work, we propose DPFuzzer, an automated framework for testing drone path planners. DPFuzzer is an evolutionary algorithm (EA) based testing framework. It aims to uncover vulnerabilities in drone path planners by generating diverse critical scenarios that can trigger vulnerabilities. To better guide the critical scenario generation, we introduce Environmental Risk Factor (ERF), a metric we propose, to abstract potential safety threats of scenarios. We evaluate DPFuzzer on state-of-the-art drone path planners and the experimental result shows that DPFuzzer can effectively find diverse vulnerabilities. Additionally, we demonstrate that these vulnerabilities are exploitable in the real world.
DOI 原文 ·
@inproceedings{paperbot8842,
title = {DPFuzzer: Discovering Safety Critical Vulnerabilities for Drone Path Planners},
author = {Yue Wang and Chao Yang and Xiaodong Zhang and Yuwanqi Deng and JianFeng Ma},
booktitle = {ICSE},
year = {2025},
doi = {10.1109/icse55347.2025.00051}
}