{"title":"控制移动机器人机械和计算资源的协调方法","authors":"Sajad Shahsavari;Hashem Haghbayan;Antonio Miele;Eero Immonen;Juha Plosila","doi":"10.1109/TRO.2024.3492345","DOIUrl":null,"url":null,"abstract":"Energy management of mechanical and cyber parts in mobile robots consists of two processes operating concurrently at runtime. Both the two processes can significantly improve the robots' battery lifetime and further extend mission time. In each process, information on energy consumption of one of the two parts is captured and analyzed to manipulate various mechanical/computational actuators in a robot, such as motor speed and CPU voltage/frequency. In this article, we show that considering management of mechanical and computational segments separately does not necessarily result in an energy-optimal solution due to their co-dependence; as a consequence, a runtime co-management scheme is required. We propose a proactive energy optimization methodology in which dynamically trained internal models are utilized to predict the future energy consumption for the mechanical and computational parts of a mobile robot, and based on that, the optimal mechanical speed and CPU voltage/frequency are determined at runtime. The experimental results on a ground wheeled robot show up to 36.34% reduction in the overall energy consumption compared to the state-of-the-art methods.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"347-363"},"PeriodicalIF":9.4000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10746315","citationCount":"0","resultStr":"{\"title\":\"A Coordinated Approach to Control Mechanical and Computing Resources in Mobile Robots\",\"authors\":\"Sajad Shahsavari;Hashem Haghbayan;Antonio Miele;Eero Immonen;Juha Plosila\",\"doi\":\"10.1109/TRO.2024.3492345\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Energy management of mechanical and cyber parts in mobile robots consists of two processes operating concurrently at runtime. Both the two processes can significantly improve the robots' battery lifetime and further extend mission time. In each process, information on energy consumption of one of the two parts is captured and analyzed to manipulate various mechanical/computational actuators in a robot, such as motor speed and CPU voltage/frequency. In this article, we show that considering management of mechanical and computational segments separately does not necessarily result in an energy-optimal solution due to their co-dependence; as a consequence, a runtime co-management scheme is required. We propose a proactive energy optimization methodology in which dynamically trained internal models are utilized to predict the future energy consumption for the mechanical and computational parts of a mobile robot, and based on that, the optimal mechanical speed and CPU voltage/frequency are determined at runtime. The experimental results on a ground wheeled robot show up to 36.34% reduction in the overall energy consumption compared to the state-of-the-art methods.\",\"PeriodicalId\":50388,\"journal\":{\"name\":\"IEEE Transactions on Robotics\",\"volume\":\"41 \",\"pages\":\"347-363\"},\"PeriodicalIF\":9.4000,\"publicationDate\":\"2024-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10746315\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Robotics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10746315/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Robotics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10746315/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ROBOTICS","Score":null,"Total":0}
A Coordinated Approach to Control Mechanical and Computing Resources in Mobile Robots
Energy management of mechanical and cyber parts in mobile robots consists of two processes operating concurrently at runtime. Both the two processes can significantly improve the robots' battery lifetime and further extend mission time. In each process, information on energy consumption of one of the two parts is captured and analyzed to manipulate various mechanical/computational actuators in a robot, such as motor speed and CPU voltage/frequency. In this article, we show that considering management of mechanical and computational segments separately does not necessarily result in an energy-optimal solution due to their co-dependence; as a consequence, a runtime co-management scheme is required. We propose a proactive energy optimization methodology in which dynamically trained internal models are utilized to predict the future energy consumption for the mechanical and computational parts of a mobile robot, and based on that, the optimal mechanical speed and CPU voltage/frequency are determined at runtime. The experimental results on a ground wheeled robot show up to 36.34% reduction in the overall energy consumption compared to the state-of-the-art methods.
期刊介绍:
The IEEE Transactions on Robotics (T-RO) is dedicated to publishing fundamental papers covering all facets of robotics, drawing on interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, and beyond. From industrial applications to service and personal assistants, surgical operations to space, underwater, and remote exploration, robots and intelligent machines play pivotal roles across various domains, including entertainment, safety, search and rescue, military applications, agriculture, and intelligent vehicles.
Special emphasis is placed on intelligent machines and systems designed for unstructured environments, where a significant portion of the environment remains unknown and beyond direct sensing or control.