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Power Consumption Reduction Method and Edge Offload Server for Multiple Robots

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Edge Computing – EDGE 2021 (EDGE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12990))

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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.

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Acknowledgments

This research was supported by Japan Science and Technology Agency (JST), CREST, JPMJCR19K1.

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Correspondence to Midori Sugaya .

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Natsuho, S., Ohkawa, T., Amano, H., Sugaya, M. (2022). Power Consumption Reduction Method and Edge Offload Server for Multiple Robots. In: Zhang, LJ. (eds) Edge Computing – EDGE 2021. EDGE 2021. Lecture Notes in Computer Science(), vol 12990. Springer, Cham. https://doi.org/10.1007/978-3-030-96504-4_1

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  • DOI: https://doi.org/10.1007/978-3-030-96504-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96503-7

  • Online ISBN: 978-3-030-96504-4

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