We study planning problems for dynamical systems with uncertainty caused by measurement and process noise. Measurement noise causes limited observability of system states, and process noise causes uncertainty in the outcome of a given control. The problem is to find a controller that guarantees that the system reaches a desired goal state in finite time while avoiding obstacles, with at least some required probability. Due to the noise, this problem does not admit exact algorithmic or closed-form solutions in general. Our key contribution is a novel planning scheme that employs Kalman filtering as a state estimator to obtain a finite-state abstraction of the dynamical system, which we formalize as a Markov decision process (MDP). By extending this MDP with intervals of probabilities, we enhance the robustness of the model against numerical imprecision in approximating the transition probabilities. For this so-called interval MDP (iMDP), we employ state-of-the-art verification techniques to efficiently compute plans that maximize the probability of reaching goal states. We show the correctness of the abstraction and provide several optimizations that aim to balance the quality of the plan and the scalability of the approach. We demonstrate that our method is able to handle systems with a 6-dimensional state that result in iMDPs with tens of thousands of states and millions of transitions.
翻译:测量噪音导致系统状态的可观察性有限,而过程噪音则造成特定控制结果的不确定性。问题在于找到一个控制器,确保系统在有限的时间内达到理想的目标状态,同时避免障碍,至少需要一定的概率。由于噪音,这一问题并不普遍接受精确的算法或封闭式解决办法。我们的关键贡献是一个新的规划方案,它利用卡尔曼过滤器作为国家估计器来获取动态系统的有限状态抽象化,我们将其正式定为马尔科夫决策程序(MDP)。通过将这一MDP扩展为概率间隔,我们加强了模型的稳健性,防止在接近过渡概率时出现数字不准确性。对于这种所谓的间隙MDP(iMDP),我们使用最先进的核查技术来有效地配置能够最大限度地达到目标状态的计划。我们展示了抽象的正确性,并提供了若干优化,目的是在计划质量与概率间断的概率间隙中,我们用上百万个系统的方法来平衡了我们计划的质量,并且能够以上百万的高度的方法来显示我们处理的系统。