机器人能力变化时，自动调整行为以确保协作任务完成
Online Resynthesis of High-Level Collaborative Tasks for Robots with Changing Capabilities
- 基于LTL^ψ动态重调度机器人行为，最小化团队重组
- 支持用户指定每项任务至少需多少机器人参与
- 适用于机器人故障或新增能力的实时协作场景
针对由异构机器人和行为组成的协作高阶任务，在机器人能力发生改变（如故障或新增动作）时，本文研究如何在运行时自动调整各机器人的行为，以确保任务仍可满足。任务采用LTL^ψ编码，目标是最小化全局团队重组（及局部重合成）。同时，通过引入用户可指定的额外约束（如每项任务所需最少机器人数量），提升了LTL^ψ的表达能力。实验在模拟仓库场景中验证了该框架的有效性。
原文摘要 · Abstract (English)
Given a collaborative high-level task and a team of heterogeneous robots and behaviors to satisfy it, this work focuses on the challenge of automatically, at runtime, adjusting the individual robot behaviors such that the task is still satisfied, when robots encounter changes to their abilities--either failures or additional actions they can perform. We consider tasks encoded in LTL^ψand minimize global teaming reassignments (and as a result, local resynthesis) when robots' capabilities change. We also increase the expressivity of LTL^ψby including additional types of constraints on the overall teaming assignment that the user can specify, such as the minimum number of robots required for each assignment. We demonstrate the framework in a simulated warehouse scenario.
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