具有变化能力的机器人高水平协同任务的在线再合成

IF 4.6 2区 计算机科学 Q2 ROBOTICS
Amy Fang;Tenny Yin;Hadas Kress-Gazit
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引用次数: 0

摘要

给定一个协作的高级任务和一个具有行为来满足该任务的异构机器人团队,本工作重点关注在运行时自动调整单个机器人行为以满足任务的挑战。我们专门解决了机器人遇到能力变化的场景——要么是失败,要么是它们可以执行的额外动作。当机器人的能力发生变化时,我们的目标是最小化全局团队重新分配(以及因此产生的局部重新合成)。任务用LTL$^\psi$编码,LTL$是我们在前面的工作中介绍的LTL的扩展。我们通过在用户可以指定的整体团队分配中包含额外类型的约束来增加LTL$^\psi$的表达性,例如每个分配所需的机器人的最小数量。我们在模拟的仓库场景中演示该框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online Resynthesis of High-Level Collaborative Tasks for Robots With Changing Capabilities
Given a collaborative high-level task and a team of heterogeneous robots with behaviors to satisfy it, this work focuses on the challenge of automatically adjusting the individual robot behaviors at runtime such that the task is still satisfied. We specifically address scenarios when robots encounter changes to their abilities–either failures or additional actions they can perform. We aim to minimize global teaming reassignments (and as a result, local resynthesis) when robots' capabilities change. The tasks are encoded in LTL$^\psi$, an extension of LTL introduced in our prior work. We increase the expressivity of LTL$^\psi$ by 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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来源期刊
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters Computer Science-Computer Science Applications
CiteScore
9.60
自引率
15.40%
发文量
1428
期刊介绍: The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.
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