论文标题
输出反馈路径计划具有鲁棒性与状态依赖性错误
Output-Feedback Path Planning with Robustness to State-Dependent Errors
论文作者
论文摘要
我们考虑了受系统错误影响的测量结果基于样本的反馈运动计划的问题。我们以前的工作提出了输出反馈控制器，这些反馈控制器使用环境中地标的测量值，使用二元性，控制Lyapunov和屏障功能（CLF，CBF）和线性编程浏览可分配的环境。在本文中，我们以一种新颖的策略为基础，该策略允许在感知深度（与基于视觉的传感器可能生成的）中使用系统错误影响的测量值，而不是准确的位移测量。结果，我们的新方法的优点是使用不准确的传感器时提供更健壮的性能（具有定量保证）。我们测试了模拟中提出的算法，以评估理论推导预测的方法的性能限制。
We consider the problem of sample-based feedback motion planning from measurements affected by systematic errors. Our previous work presented output feedback controllers that use measurements from landmarks in the environment to navigate through a cell-decomposable environment using duality, Control Lyapunov and Barrier Functions (CLF, CBF), and Linear Programming. In this paper, we build on this previous work with a novel strategy that allows the use of measurements affected by systematic errors in perceived depth (similarly to what might be generated by vision-based sensors), as opposed to accurate displacement measurements. As a result, our new method has the advantage of providing more robust performance (with quantitative guarantees) when inaccurate sensors are used. We test the proposed algorithm in the simulation to evaluate the performance limits of our approach predicted by our theoretical derivations.
