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 RLaGA, a reinforcement learning (RL) augmented search-based testing framework, which constructs diverse and real marker-based landing cases that involve safety violations. Specifically, RLaGA 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：针对无人机（如多旋翼飞行器）的自动着陆系统，尤其在标记辅助场景下，需依赖包含控制算法、避障与机器视觉的复杂软件。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，因此在实际部署前通过仿真全面测试自动着陆系统对保障安全至关重要。本文提出RLaGA——一种基于强化学习增强的搜索测试框架，可构建涉及安全违规的多样且真实的标记辅助着陆案例。具体而言，RLaGA引入遗传算法进行保守离线搜索，以生成多样化的静态环境配置；同时通过强化学习在线主动操控动态物体的轨迹，以探测目标部署环境中的潜在脆弱性。定量结果表明，与基线方法相比，本方法能生成最多22.19%的违规案例，且生成的违规案例多样性近乎翻倍。定性分析显示，本方法可发现现有最先进算法遗漏的边缘案例。我们通过现场无人机实际测试验证了特定类型边缘案例的可复现性。