A*-based Temporal Logic Path Planning with User Preferences on Relaxed Task Satisfaction

TL;DR

This paper introduces an A*-based planning method for temporal logic tasks that incorporates user preferences for relaxation, enabling efficient and near-optimal path planning in large robot environments when full task satisfaction is not possible.

Contribution

The paper presents a novel A*-based framework that integrates user preferences into temporal logic planning, with an efficient heuristic for large-scale environments and empirical bounds on suboptimality.

Findings

Effective planning in large environments with high scalability.

The heuristic significantly reduces planning time and memory usage.

Empirical bounds demonstrate near-optimal solutions with relaxed task satisfaction.

Abstract

In this work, we consider the problem of planning for temporal logic tasks in large robot environments. When full task compliance is unattainable, we aim to achieve the best possible task satisfaction by integrating user preferences for relaxation into the planning process. Utilizing the automata-based representations for temporal logic goals and user preferences, we propose an A*-based planning framework. This approach effectively tackles large-scale problems while generating near-optimal high-level trajectories. To facilitate this, we propose a simple, efficient heuristic that allows for planning over large robot environments in a fraction of time and search memory as compared to uninformed search algorithms. We present extensive case studies to demonstrate the scalability, runtime analysis as well as empirical bounds on the suboptimality of the proposed heuristic.