让机器人在未知环境中按逻辑要求探索，确保任务可完成且不走错路。
Temporal-Logic-Aware Frontier-Based Exploration
- 引入'承诺状态'追踪不可逆动作，保留任务完成路径
- 算法能完全满足指定的时序逻辑任务要求
- 适合需要可靠探索的自主机器人系统
本文研究自主机器人在未知环境中满足时序逻辑运动规划的问题。目标是在无法预先知晓目标标签位置的情况下，使机器人能够满足语法共安全线性时序逻辑（scLTL）规范。我们提出一种新型自动机状态——承诺状态，用于捕捉因不可逆动作导致的任务进展。一旦进入此类状态，某些未来的任务达成路径将不再可行。基于承诺状态，我们设计了一种完备且可靠的基于前沿的探索算法，可有效引导机器人推进任务，同时保留所有可能的满足路径。仿真结果验证了该方法的有效性。
原文摘要 · Abstract (English)
This paper addresses the problem of temporal logic motion planning for an autonomous robot operating in an unknown environment. The objective is to enable the robot to satisfy a syntactically co-safe Linear Temporal Logic (scLTL) specification when the exact locations of the desired labels are not known a priori. We introduce a new type of automaton state, referred to as commit states. These states capture intermediate task progress resulting from actions whose consequences are irreversible. In other words, certain future paths to satisfaction become not feasible after taking those actions that lead to the commit states. By leveraging commit states, we propose a sound and complete frontier-based exploration algorithm that strategically guides the robot to make progress toward the task while preserving all possible ways of satisfying it. The efficacy of the proposed method is validated through simulations.
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。