论文标题
在不确定性下进行机器人计划中可拖动反馈的实验：在广泛的噪声方面的见解（扩展报告）
Experiments with Tractable Feedback in Robotic Planning under Uncertainty: Insights over a wide range of noise regimes (Extended Report)
论文作者
论文摘要
我们考虑了在不确定性下机器人计划的问题。该问题可能被视为一个随机最佳控制问题，由于臭名昭著的维度诅咒，因此从根本上棘手的解决方案。我们报告了一项广泛的仿真研究的结果，其中我们比较了两种方法，两种方法旨在通过使用替代性（尽管是不精确）来治疗反馈的方法来挽救障碍性。第一个是一种最近提出的方法，基于可访问反馈设计的近乎最佳的“分离原理”，其中解决了标称的开环问题，然后解决了围绕开环的线性反馈设计。第二个是模型预测控制（MPC），这是一种广泛雇用的方法，它在执行过程中使用名义开环问题的重复重计来纠正噪声，尽管当将其解释为反馈时，这只能说这是一种隐式形式。我们研究了比以前报道的噪声水平更大的噪声水平，经验证据表明，脱钩方法允许在多种不确定性条件的情况下进行易处理的计划，而不会过分牺牲性能。
We consider the problem of robotic planning under uncertainty. This problem may be posed as a stochastic optimal control problem, complete solution to which is fundamentally intractable owing to the infamous curse of dimensionality. We report the results of an extensive simulation study in which we have compared two methods, both of which aim to salvage tractability by using alternative, albeit inexact, means for treating feedback. The first is a recently proposed method based on a near-optimal "decoupling principle" for tractable feedback design, wherein a nominal open-loop problem is solved, followed by a linear feedback design around the open-loop. The second is Model Predictive Control (MPC), a widely-employed method that uses repeated re-computation of the nominal open-loop problem during execution to correct for noise, though when interpreted as feedback, this can only said to be an implicit form. We examine a much wider range of noise levels than have been previously reported and empirical evidence suggests that the decoupling method allows for tractable planning over a wide range of uncertainty conditions without unduly sacrificing performance.
