Automated landing for Unmanned Aerial Vehicles (UAVs), like multirotor drones, requires intricate software encompassing control algorithms, obstacle avoidance, and machine vision, especially when landing markers assist. Failed landings can lead to significant costs from damaged drones or payloads and the time spent seeking alternative landing solutions. Therefore, it's important to fully test auto-landing systems through simulations before deploying them in the real-world to ensure safety. This paper proposes \tool, a reinforcement learning (RL) augmented search-based testing framework, which constructs diverse and real marker-based landing cases that involve safety violations. Specifically, \tool \ introduces a genetic algorithm (GA) to conservatively search for diverse static environment configurations offline and RL to aggressively manipulate dynamic objects' trajectories online to find potential vulnerabilities in the target deployment environment. Quantitative results reveal that our method generates up to 22.19\% more violation cases and nearly doubles the diversity of generated violation cases compared to baseline methods. Qualitatively, our method can discover those corner cases which would be missed by state-of-the-art algorithms. We demonstrate that select types of these corner cases can be confirmed via real-world testing with drones in the field.

翻译：无人飞行器（如多旋翼无人机）的自动着陆需要复杂的软件系统，涵盖控制算法、障碍物规避和机器视觉，尤其在着陆标记辅助时更为关键。着陆失败可能导致无人机或载荷损坏、以及寻找替代着陆方案的时间成本造成重大损失。因此，在真实环境部署前通过仿真充分测试自动着陆系统对保障安全性至关重要。本文提出一种基于强化学习的搜索测试框架\tool，该框架构建包含安全违规的多样化真实场景的基于标记的着陆案例。具体而言，\tool\ 引入遗传算法保守搜索离线的多样化静态环境配置，并利用强化学习在线主动操控动态物体轨迹，以发现目标部署环境中的潜在漏洞。定量结果表明，与基线方法相比，本方法最多可生成22.19%的额外违规案例，且生成的违规案例多样性近乎翻倍。定性分析显示，本方法能发现被最先进算法遗漏的极端案例。我们证明其中特定类型的极端案例可通过野外真实环境中的无人机测试得到验证。