{"title":"Energy-Aware Task Allocation for Teams of Multi-Mode Robots","authors":"Takumi Ito;Riku Funada;Mitsuji Sampei;Gennaro Notomista","doi":"10.1109/LCSYS.2025.3590426","DOIUrl":null,"url":null,"abstract":"This letter proposes a multi-robot task allocation framework for robots that can switch between multiple modes, e.g., flying, driving, or walking. First, we present a method for encoding multi-mode properties as a graph, where the mode of each robot is represented by a node. Next, we formulate a constrained optimization problem to determine both the task to be allocated to each robot as well as the mode in which the latter should execute the task. The robot modes are optimized based on the state of the robot and environment as well as the energy required to execute the allocated task. Moreover, the proposed framework can encompass the kinematic and dynamic models of robots. We then provide sufficient conditions for the convergence of task execution and allocation in both robot models.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"1964-1969"},"PeriodicalIF":2.0000,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11084869","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Control Systems Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/11084869/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
This letter proposes a multi-robot task allocation framework for robots that can switch between multiple modes, e.g., flying, driving, or walking. First, we present a method for encoding multi-mode properties as a graph, where the mode of each robot is represented by a node. Next, we formulate a constrained optimization problem to determine both the task to be allocated to each robot as well as the mode in which the latter should execute the task. The robot modes are optimized based on the state of the robot and environment as well as the energy required to execute the allocated task. Moreover, the proposed framework can encompass the kinematic and dynamic models of robots. We then provide sufficient conditions for the convergence of task execution and allocation in both robot models.