用自然语言+逻辑约束实现机器人分层路径规划，兼顾任务顺序与安全。
Hierarchical Sampling-based Planner with LTL Constraints and Text Prompting
- 分层规划：先生成区域图再转为状态机，高阶规划指导低阶导航
- 文本指令转为LTL公式，通过BFS生成满足时序要求的路径
- 适合复杂多步骤任务，支持安全约束下的智能移动规划
本项目提出一种融合线性时序逻辑（LTL）约束与自然语言提示的分层规划框架。该框架将地图划分为区域并生成有向图，进而转化为转移系统用于高层规划。文本指令被转换为LTL公式，并转化为确定性有限自动机（DFA），以实现符合时序要求的多目标任务执行，同时满足安全约束。通过广度优先搜索（BFS）生成高层计划，指导如快速扩展随机树（RRT）和概率路线图（PRM）等低层规划器完成避障导航。方法在多种任务复杂度下表现出良好适应性，但存在图构建开销大、路径非最优等问题。未来工作将拓展至考虑地形条件和高阶动力学模型。
原文摘要 · Abstract (English)
This project introduces a hierarchical planner integrating Linear Temporal Logic (LTL) constraints with natural language prompting for robot motion planning. The framework decomposes maps into regions, generates directed graphs, and converts them into transition systems for high-level planning. Text instructions are translated into LTL formulas and converted to Deterministic Finite Automata (DFA) for sequential goal-reaching tasks while adhering to safety constraints. High-level plans, derived via Breadth-First Search (BFS), guide low-level planners like Exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM) for obstacle-avoidant navigation along with LTL tasks. The approach demonstrates adaptability to various task complexities, though challenges such as graph construction overhead and suboptimal path generation remain. Future directions include extending to considering terrain conditions and incorporating higher-order dynamics.
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