This paper presents a new multi-layered algorithm for motion planning under motion and sensing uncertainties for Linear Temporal Logic specifications. We propose a technique to guide a sampling-based search tree in the combined task and belief space using trajectories from a simplified model of the system, to make the problem computationally tractable. Our method eliminates the need to construct fine and accurate finite abstractions. We prove correctness and probabilistic completeness of our algorithm, and illustrate the benefits of our approach on several case studies. Our results show that guidance with a simplified belief space model allows for significant speed-up in planning for complex specifications.

翻译：本文提出了一种新的多层运动规划算法，用于线性时态逻辑规范下的不确定运动和感知下的运动规划。我们提出了一种利用系统简化模型中的轨迹来指导在任务和置信度空间中进行基于采样的搜索树的技术，以使问题在计算上变得可行。我们的方法消除了构建精细和准确的有限抽象的需求。我们证明了算法的正确性和概率完备性，并在几个案例研究中说明了我们方法的好处。我们的结果表明，使用简化置信度空间模型进行指导可以显着加速复杂规范的规划。