Power Consumption Reduction Method and Edge Offload Server for Multiple Robots

Sannomiya Natsuho, Takeshi Ohkawa, Hideharu Amano, Midori Sugaya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

There are emerging services for the transports and nursing with multiple robots has become more familiar to our society. Considering the increasing demand for automatic multiple robotic services, it appears the research into automatic multiple robotic services is not satisfactory. Specifically, the issues of power consumption of these robots, and its potential reduction have not been sufficiently discussed. In this research, we propose a method and system to reduce the aggregated power consumption of multiple robots by modelling the characteristics of the hardware and service of each robot. We firstly discuss the prediction model of the robot and improve the formula with consideration of its use in a wide range of situations. Then, we achieve the objective of reducing the aggregate power consumption of multiple robots, using consumption logs and re-allocating tasks of them based on the power consumption prediction model of the individual robot. We propose the design and develop a system using ROS (Robot Operating System) asynchronous server to collect the data from the robots, and make the prediction model for each robot, and reallocate tasks based on the findings of the optimized combination on the server. Through the evaluation of the design and implementation with the proposed system and the actual robot Zoom (GR-PEACH + Rasberry pi), we achieve an average power reduction effect of 14%. In addition, by offloading high-load processing to an edge server configured with FPGA instead the Intel Core i7 performance computer, we achieved and increase in processing speed of up to about 70 times.

Original languageEnglish
Title of host publicationEdge Computing - EDGE 2021 - 5th International Conference, Held as Part of the Services Conference Federation, SCF 2021, Proceedings
EditorsLiang-Jie Zhang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages1-19
Number of pages19
ISBN (Print)9783030965037
DOIs
Publication statusPublished - 2022
Event5th International Conference on Edge Computing, EDGE 2021, Held as Part of the Services Conference Federation, SCF 2021 - Virtual, Online
Duration: 2021 Dec 102021 Dec 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12990 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Edge Computing, EDGE 2021, Held as Part of the Services Conference Federation, SCF 2021
CityVirtual, Online
Period21/12/1021/12/14

Keywords

  • Aggregated power reduction
  • Multiple robots
  • Offloading
  • Power savings
  • ROS
  • Software system
  • Total power reduction method

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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