用数据驱动方法统一无人机3D巡检的感知、规划与控制，实现长程精准轨迹生成。
Data-Driven Predictive Planning and Control for Aerial 3D Inspection with Back-face Elimination
- 基于输入输出数据构建预测控制框架，无需已知飞行模型
- 引入背面消除技术，实时生成可避免遮挡的巡检路径
- 适合无先验动力学模型的商用无人机快速部署
无人飞行器（UAS）自动化巡检具有变革性潜力，但需融合感知、规划与控制，现有方法常将其割裂。传统手段多为短视决策，难以实现长期精确规划。本文提出一种统一的3D巡检数据驱动预测控制框架，不依赖已知的UAS动力学模型，仅需输入输出数据即可适配现成黑箱无人机。通过将3D计算机图形中的背面消除技术直接嵌入控制回路，实现在线生成准确的长时程3D巡检轨迹，有效规避视角遮挡问题。
原文摘要 · Abstract (English)
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.
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