在语义未知环境中，用概率知识规划满足时空逻辑任务的路径。
Motion Planning Under Temporal Logic Specifications In Semantically Unknown Environments
- 构建带不确定性的产品自动机，融合语义标签的概率信念
- 通过价值迭代在线重规划，实现对动态环境的适应性路径生成
- 适用于自动驾驶、机器人导航等需推理复杂逻辑的任务
本文研究在不确定性环境中的运动规划问题，目标是完成由语法安全线性时序逻辑（scLTL）表达的时空逻辑任务。环境中的不确定性以语义标签的概率知识建模，例如区域1和区域2的具体位置未知，但存在概率分布。为此，提出一种新颖的自动机理论方法：构建特殊的产品自动机以捕获语义标签的不确定性，并为每条边设计奖励函数。所提算法采用价值迭代进行在线重规划。理论分析与仿真结果验证了该方法的有效性。
原文摘要 · Abstract (English)
This paper addresses a motion planning problem to achieve spatio-temporal-logical tasks, expressed by syntactically co-safe linear temporal logic specifications (scLTL\next), in uncertain environments. Here, the uncertainty is modeled as some probabilistic knowledge on the semantic labels of the environment. For example, the task is "first go to region 1, then go to region 2"; however, the exact locations of regions 1 and 2 are not known a priori, instead a probabilistic belief is available. We propose a novel automata-theoretic approach, where a special product automaton is constructed to capture the uncertainty related to semantic labels, and a reward function is designed for each edge of this product automaton. The proposed algorithm utilizes value iteration for online replanning. We show some theoretical results and present some simulations/experiments to demonstrate the efficacy of the proposed approach.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。