在复杂环境中，根据用户偏好智能调整任务完成度，实现高效路径规划。
A*-based Temporal Logic Path Planning with User Preferences on Relaxed Task Satisfaction
- 基于A*算法与时序逻辑结合，融入用户对任务松弛的偏好。
- 可在极短时间内完成大规模环境下的近优轨迹规划。
- 适合需要灵活响应任务约束的机器人系统部署。
本文研究大型机器人环境中的时序逻辑任务规划问题。当完全满足任务要求不可行时，通过将用户对任务松弛的偏好纳入规划过程，力求实现最优的任务满足度。利用时序逻辑目标与用户偏好的自动机表示，提出一种基于A*的规划框架，能够有效处理大规模问题并生成近似最优的高层轨迹。为此，设计了一种简单高效的启发式方法，在显著减少搜索时间和内存消耗的前提下，实现对大环境的有效规划。通过大量案例研究，验证了该方法的可扩展性、运行时性能以及启发式策略子最优性的经验边界。
原文摘要 · Abstract (English)
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.
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