标题： 基于数据驱动的空中三维检测背面消除预测规划与控制 显示英文标题

标题： Data-Driven Predictive Planning and Control for Aerial 3D Inspection with Back-face Elimination

摘要： 使用无人航空系统（UASs）的自动化检测是一种具有变革性的能力，有望彻底改变各种应用领域。 然而，这项任务本质上是复杂的，因为它需要感知、规划和控制的无缝集成，而现有方法通常将它们分别处理。 此外，它需要精确的长时程规划来预测动作序列，这与许多当前技术倾向于短视的做法形成对比。 为了克服这些限制，我们提出了一种3D检测方法，该方法在一个单一的数据驱动预测控制框架内统一了感知、规划和控制。 与依赖已知UAS动态模型的传统方法不同，我们的方法只需输入输出数据，使其易于应用于现成的黑盒UASs。 我们的方法将背面消除技术，一种来自3D计算机图形学的可见性确定技术，直接纳入控制循环，从而实现了准确的长时程3D检测轨迹的在线生成。 显示英文摘要

摘要： Automated inspection with Unmanned Aerial Systems (UASs) is a transformative capability set to revolutionize various application domains. However, this task is inherently complex, as it demands the seamless integration of perception, planning, and control which existing approaches often treat separately. Moreover, it requires accurate long-horizon planning to predict action sequences, in contrast to many current techniques, which tend to be myopic. To overcome these limitations, we propose a 3D inspection approach that unifies perception, planning, and control within a single data-driven predictive control framework. Unlike traditional methods that rely on known UAS dynamic models, our approach requires only input-output data, making it easily applicable to off-the-shelf black-box UASs. Our method incorporates back-face elimination, a visibility determination technique from 3D computer graphics, directly into the control loop, thereby enabling the online generation of accurate, long-horizon 3D inspection trajectories.