基于自主无人机Agent的三维覆盖规划的无气味最优控制

Savvas Papaioannou, P. Kolios, T. Theocharides, C. Panayiotou, M. Polycarpou
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引用次数: 0

摘要

我们提出了一种新的概率鲁棒控制器,用于无人机(UAV)的覆盖规划任务制导,该控制器可以同时优化无人机的运动和相机控制输入,以实现给定目标的3D覆盖。具体而言,本文将覆盖规划问题表述为具有逻辑约束的最优控制问题,使无人机智能体能够共同:a)选择一系列满足覆盖约束的离散相机视场状态,b)根据指定的任务目标优化其运动控制输入。通过将约束中的逻辑表达式转换为仅涉及连续变量的等式/不等式约束,我们展示了如何使用标准优化工具来解决这种混合最优控制问题。最后,通过将unscented变换集成到所提出的控制器中来实现概率鲁棒性,从而能够设计考虑无人机状态在规划视界内未来后验分布的鲁棒开环覆盖计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unscented Optimal Control for 3D Coverage Planning with an Autonomous UAV Agent
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.
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