论文标题
COP：控制与观察性知觉计划
COP: Control & Observability-aware Planning
论文作者
论文摘要
在这项研究中，我们旨在回答以下问题：如何将闭环状态和基于输入灵敏度与意识性轨迹计划相结合？这些可能相反的优化目标可用于改善轨迹控制跟踪，同时估算性能。我们提出的新颖的控制和可观察性知觉计划（COP）框架是第一个基于增强加权Tchebycheff方法在单一目标优化问题（SOOP）中使用这些可能相反的目标的框架，以执行其平衡和基于Bézier曲线的产生。 3D四型无人机（UAV）案例研究的统计相关模拟产生了支持我们的主张并显示两个目标之间的负相关性的结果。我们能够减少位置平均积分误差规范，以及具有相同轨迹的估计不确定性，以与单个目标优化的可比轨迹相同。
In this research, we aim to answer the question: How to combine Closed-Loop State and Input Sensitivity-based with Observability-aware trajectory planning? These possibly opposite optimization objectives can be used to improve trajectory control tracking and, at the same time, estimation performance. Our proposed novel Control & Observability-aware Planning (COP) framework is the first that uses these possibly opposing objectives in a Single-Objective Optimization Problem (SOOP) based on the Augmented Weighted Tchebycheff method to perform the balancing of them and generation of Bézier curve-based trajectories. Statistically relevant simulations for a 3D quadrotor unmanned aerial vehicle (UAV) case study produce results that support our claims and show the negative correlation between both objectives. We were able to reduce the positional mean integral error norm as well as the estimation uncertainty with the same trajectory to comparable levels of the trajectories optimized with individual objectives.
