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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Burgner-Kahrs, J., et al.: Continuum robots for medical applications: a survey. IEEE Trans. Rob. 31(6), 1261–1280 (2015)
Zemmar, A., Lozano, A.M., Nelson, B.J.: The rise of robots in surgical environments during COVID-19. Nature Machine Intelligence, vol. 2, pp. 566–572 (2020)
Teodoros, T., Hu, H.: Toward intelligent security robots: a survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(6), 1261–1280 (2012)
Sakaue, T., et al.: Survey in Fukushima Daiichi NPS by combination of human and remotely-controlled robot. In: IEEE International Symposium on Safety, Security and Rescue Robotics, pp. 11–13 (2017)
Kiva Systems: Three Engineers, Hundreds of Robots, One Warehouse. IEEE Spectrum. https://spectrum.ieee.org/robotics/robotics-software/three-engineers-hundreds-of-robots-one-warehouse. Accessed 07 Jan 2020
Maneewarn, T., et al.: Survey of Social Robots in Thailand. International Electrical Engineering Congress (iEECON), pp. 19–21 (2014)
Hospie | Panasonic Collaboration II Co-production | Matsushita Memorial Hospital. http://phio.panasonic.co.jp/kinen/pc/hospi/index.htm. Accessed 07 Jan 2020
Japan’s first patrol monitoring service for SECOM drone, an autonomous flight surveillance robot SECOM drone that completely autonomously starts, flies, returns, and recharges. PFI prison “Mine Societies” in which SECOM participates as a representative company Start on March 1 at the “Return Promotion Center” SECOM Corporation. https://www.secom.co.jp/corporate/release/2017/nr_20180301.html. Accessed 07 Jan 2020
Disaster response robot Quince | TadoLab. Tohoku University Human-Robot Informatics Laboratory. https://www.rm.is.tohoku.ac.jp/quince_mech/#Quince_1. Accessed 07 Jan 2020
Starting inter-post office transport using small unmanned aerial vehicles. Japan Post. https://www.post.japanpost.jp/notification/pressrelease/2018/00_honsha/1030_01.html. Accessed 07 Jan 2020
Amazon Uses 800 Robots to Run This Warehouse. IEEE Spectrum. https://spectrum.ieee.org/automaton/robotics/industrial-robots/amazon-introduces-two-new-warehouse-robots. Accessed 07 Jan 2020
Ministry of Economy, Trade and Industry. New Strategy for Robots, 2015 edn. (2015) http://warp.da.ndl.go.jp/info:ndljp/pid/11181294/www.meti.go.jp/committee/sankoushin/seizou/pdf/003_s01_03.pdf. Accessed 25 Dec 2019
New Energy and Industrial Technology Development Organization. Future market forecast for the robot industry toward 2035 (2010). https://www.nedo.go.jp/content/100080673.pdf. Accessed 25 Dec 2019
Shimizu, K., et al.: Proposal of management method based on motion and power consumption characteristics of multiple distributed mobile robots. In: IPSJ 78th National Convention (2015)
Kantake, T.: Distributed task processing considering rmption of multiple robots. In: Embedded Systems Symposium 2018 Proceedings, pp. 108–109 (2018)
Takasu, M., et al.: Evaluation of power saving mechanism in embedded processors. In: Embedded System Symposium 2012, pp. 79–86 (2012)
Pan, S., et al.: A low-power soc-based moving target detection system for amphibious spherical robot. In: IEEE International Conference on Mechatronics and Automation (ICMA), pp. 1116–1121 (2015)
Eckert, J., et al.: An indoor localization framework for four-rotor flying robots using low-power sensor nodes. IEEE Trans. Instrum. Meas. 60(2), 336–344 (2011)
Kim, Y., et al.: BRAIN: a low-power deep search engine for autonomous robots. IEEE Micro 37(5), 11–19 (2017)
Hosangadi, A., et al.: Energy efficient hardware synthesis of polynomial expressions. In: 18th International Conference on VLSI Design, pp. 653–658 (2005)
Sakamura, K.: Research and development of TK-SLP (T-KernelSuper-LowPower) embedded software platform (2014). http://www.soumu.go.jp/main_content/000323271.pdf. Accessed 07 Jan 2020
Create 2 Programmable Robot|iRobot. https://www.irobot.com/about-irobot/stem/create-2. Accessed 07 Jan 2020
Musha, K., Kudoh, T., Amano, H.: Deep learning on high performance FPGA switching boards: flow-in-cloud. In: Proceedings of International Symposium on Applied Reconfigurable Computing (2018)
Quigley, M., Gerkey, B., Smart, W.D.: Programming Robots with ROS, O'Reilly Media (2015). ISBN 9781449323899
Acknowledgments
This research was supported by Japan Science and Technology Agency (JST), CREST, JPMJCR19K1.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-96504-4_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-96503-7
Online ISBN: 978-3-030-96504-4
eBook Packages: Computer ScienceComputer Science (R0)