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：本文提出了一种面向线性时序逻辑规范的全新多层运动规划算法，该算法同时考虑运动与感知不确定性。我们提出一种技术，利用系统简化模型生成的轨迹来引导基于采样的搜索树在联合任务与信念空间中的扩展，从而提升问题的计算可解性。该方法无需构建精细准确的有限抽象模型。我们证明了算法的正确性与概率完备性，并通过多个案例研究验证了其优势。实验结果表明，采用简化信念空间模型进行引导可显著加速复杂规范下的规划进程。