一句话：提出 GARL 框架，用遗传算法离线探索环境配置、强化学习在线搜索测试，在有限预算内高效发现无人机自主着陆系统的多样化违规场景。
无人机自主着陆需要完成目标检测、障碍感知、无碰撞路径规划与飞行控制，一旦失效将造成重大损失，因此需要严格的仿真测试。传统离线测试局限于静态环境和预定义轨迹，容易漏掉由人或动物等动态物体引发的违规；在线测试方法虽能应对动态场景，却需要大量训练时间，在预算受限时不切实际。为此，作者提出 GARL，将遗传算法与强化学习结合：遗传算法在离线阶段探索多样的环境配置，从而降低强化学习在线模拟挑战性着陆场景时的复杂度，二者协同生成多样且真实的失效案例。实验表明，GARL 在违规率上最多优于现有方法 18.35%，在多样性指标上提升 58%，且多数发现的违规类型经真实无人机测试得到验证。该工作首次将离线与在线测试策略结合用于自主系统，为在线测试开辟了新的研究方向。
Automated Uncrewed Aerial Vehicle (UAV) landing is crucial for autonomous UAV services such as monitoring, surveying, and package delivery. It involves detecting landing targets, perceiving obstacles, planning collision-free paths, and controlling UAV movements for safe landing. Failures can lead to significant losses, necessitating rigorous simulation-based testing for safety. Traditional offline testing methods, limited to static environments and predefined trajectories, may miss violation cases caused by dynamic objects like people and animals. Conversely, online testing methods require extensive training time, which is impractical with limited budgets. To address these issues, we introduce GARL, a framework combining a genetic algorithm (GA) and reinforcement learning (RL) for efficient generation of diverse and real landing system failures within a practical budget. GARL employs GA for exploring various environment setups offline, reducing the complexity of RL's online testing in simulating challenging landing scenarios. Our approach outperforms existing methods by up to 18.35% in violation rate and 58% in diversity metric. We validate most discovered violation types with real-world UAV tests, pioneering the integration of offline and online testing strategies for autonomous systems. This method opens new research directions for online testing, with our code and supplementary material available at https://github.com/lfeng0722/drone_testig/.
DOI 原文 ·
@inproceedings{paperbot8929,
title = {GARL: Genetic Algorithm-Augmented Reinforcement Learning to Detect Violations in Marker-Based Autonomous Landing Systems},
author = {Linfeng Liang and Yao Deng and Kye Morton and Valtteri Kallinen and Alice James and Avishkar Seth and Endrowednes Kuantama and Subhas Mukhopadhyay and Richard Han and Xi Zheng},
booktitle = {ICSE},
year = {2025},
doi = {10.1109/icse55347.2025.00076}
}