Temporal-Logic-Aware Frontier-Based Exploration

2026-02-21

AI Summary

This work addresses the challenge of enabling robots to satisfy syntactically co-safe linear temporal logic (scLTL) specifications in unknown environments where the locations of target labels are not a priori known. To this end, the authors propose a novel algorithm that integrates frontier-based exploration with scLTL-constrained planning. The approach introduces, for the first time, the concept of “commitment states” to formally capture the impact of irreversible actions on task progression. By leveraging automaton theory and commitment state modeling, the method ensures that all feasible successful trajectories remain unpruned while guiding the robot toward efficient completion of complex temporal tasks. Simulation experiments demonstrate that the proposed framework effectively balances task correctness with exploration completeness in unknown environments.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Temporal Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• temporal logic
• autonomous robot
• unknown environment
• scLTL
• motion planning

Innovation

• commit states
• frontier-based exploration
• linear temporal logic
• autonomous robot
• unknown environment