We propose a novel probabilistically robust controller for the guidance of an unmanned aerial vehicle (UAV) in coverage planning missions, which can simultaneously optimize both the UAV's motion, and camera control inputs for the 3D coverage of a given object of interest. Specifically, the coverage planning problem is formulated in this work as an optimal control problem with logical constraints to enable the UAV agent to jointly: a) select a series of discrete camera field-of-view states which satisfy a set of coverage constraints, and b) optimize its motion control inputs according to a specified mission objective. We show how this hybrid optimal control problem can be solved with standard optimization tools by converting the logical expressions in the constraints into equality/inequality constraints involving only continuous variables. Finally, probabilistic robustness is achieved by integrating the unscented transformation to the proposed controller, thus enabling the design of robust open-loop coverage plans which take into account the future posterior distribution of the UAV's state inside the planning horizon.

翻译：本文提出一种新颖的概率鲁棒控制器，用于引导无人航空器（UAV）执行覆盖规划任务，该控制器可同时优化无人机运动与相机控制输入，以实现对特定目标物体的三维覆盖。具体而言，本工作将覆盖规划问题形式化为包含逻辑约束的最优控制问题，使无人机Agent能够协同完成：a) 选择一系列满足覆盖约束的离散相机视场状态，b) 根据指定任务目标优化其运动控制输入。我们展示了如何通过将约束中的逻辑表达式转化为仅涉及连续变量的等式/不等式约束，利用标准优化工具求解此混合最优控制问题。最后，通过将无迹变换集成至所提控制器实现概率鲁棒性，从而能够设计考虑规划时域内无人机状态未来后验分布的鲁棒开环覆盖方案。