{"title":"具有变化能力的机器人高水平协同任务的在线再合成","authors":"Amy Fang;Tenny Yin;Hadas Kress-Gazit","doi":"10.1109/LRA.2025.3527337","DOIUrl":null,"url":null,"abstract":"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<inline-formula><tex-math>$^\\psi$</tex-math></inline-formula>, an extension of LTL introduced in our prior work. We increase the expressivity of LTL<inline-formula><tex-math>$^\\psi$</tex-math></inline-formula> 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.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"2032-2039"},"PeriodicalIF":4.6000,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Online Resynthesis of High-Level Collaborative Tasks for Robots With Changing Capabilities\",\"authors\":\"Amy Fang;Tenny Yin;Hadas Kress-Gazit\",\"doi\":\"10.1109/LRA.2025.3527337\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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<inline-formula><tex-math>$^\\\\psi$</tex-math></inline-formula>, an extension of LTL introduced in our prior work. We increase the expressivity of LTL<inline-formula><tex-math>$^\\\\psi$</tex-math></inline-formula> 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.\",\"PeriodicalId\":13241,\"journal\":{\"name\":\"IEEE Robotics and Automation Letters\",\"volume\":\"10 2\",\"pages\":\"2032-2039\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2025-01-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Robotics and Automation Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10833845/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Robotics and Automation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10833845/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
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.
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.