Offloading SLAM for Indoor Mobile Robots with Edge-Fog-Cloud Computing

K., Sarker, T, Tenhunen, J., Queralta and T., Westerlund Offloading SLAM for Indoor Mobile Robots with Edge-Fog-Cloud Computing., 2019 . In International Conference on Advances in Science, Engineering and Robotics Technology, Bangladesh, 5/5/2019. [Conference paper]

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English abstract

Indoor mobile robots are widely used in industrial environments such as large logistic warehouses. They are often in charge of collecting or sorting products. For such robots, computation-intensive operations account for a significant per- centage of the total energy consumption and consequently affect battery life. Besides, in order to keep both the power con- sumption and hardware complexity low, simple micro-controllers or single-board computers are used as onboard local control units. This limits the computational capabilities of robots and consequently their performance. Offloading heavy computation to Cloud servers has been a widely used approach to solve this problem for cases where large amounts of sensor data such as real-time video feeds need to be analyzed. More recently, Fog and Edge computing are being leveraged for offloading tasks such as image processing and complex navigation algorithms involving non-linear mathematical operations. In this paper, we present a system architecture for offloading computationally expensive localization and mapping tasks to smart Edge gateways which use Fog services. We show how Edge computing brings computational capabilities of the Cloud to the robot environment without compromising operational reliability due to connection issues. Furthermore, we analyze the power consumption of a prototype robot vehicle in different modes and show how battery life can be significantly improved by moving the processing of data to the Edge layer.

Item type: Conference paper
Keywords: Edge, Fog, Cloud, SLAM, Efficiency, Computa- tion, Offloading, Energy, Performance, Mobile, Robots
Subjects: B. Information use and sociology of information
L. Information technology and library technology
Depositing user: T. Westerlund
Date deposited: 22 Aug 2020 22:05
Last modified: 22 Aug 2020 22:05
URI: http://hdl.handle.net/10760/40269

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References

[1] N. Mitabe and N. Shinomiya. An intelligent care support system for the elderly with an autonomous robot in ambient sensors. In 2017 IEEE GCCE, pages 1–4, 2017.

[2] M. Jiang et al. Iot-based remote facial expression monitoring system with semg signal. In 2016 IEEE SAS, pages 1–6. IEEE, 2016. [3]S. R. Moosavi et al. Session resumption-based end-to-end security for healthcare internet-of-things. In IEEE CIT, pages 581–588. IEEE, 2015.

[4] M. Ali et al. Autonomous patient/home health monitoring powered by energy harvesting. In IEEE GLOBECOM 2017, pages 1–7. IEEE, 2017.

[5] T. N. Gia et al. Iot-based continuous glucose monitoring system:A feasibility study. Procedia Computer Science, 109:327–334, 2017. [6]I. Tcarenko et al. Energy-efficient iot-enabled fall detection system with messenger-based notification. In International Conference on Wireless Mobile Communication and Healthcare, pages 19–26. Springer, 2016. [7]R. Petrolo et al. Integrating wireless sensor networks within a city cloud. In IEEE SECON Workshops, pages 24–27, 2014.

[8] T. Kitanouma et al. Cloud-based self-organizing localization with virtualnetwork topology for wireless sensor networks and its implementation. In 2016 IEEE PIMRC, pages 1–7, 2016.

[9] Tadapaneni, N. R. (2019). Role of Fog Computing in the Internet of Things. International Journal of Scientific Research and Engineering Trends

[10] B. Negash et al. Leveraging fog computing for healthcare iot. In Fog Computing in the Internet of Things, pages 145–169. Springer, 2018. [11]T. N. Gia et al. Exploiting fog computing in health monitoring. Fog and Edge Computing: Principles and Paradigms, pages 291–318, 2019. [12]S. Dey and A. Mukherjee. Robotic slam: a review from fog computing and mobile edge computing perspective. In MobiQuitous, pages 153–158. ACM, 2016.

[13] P. Benavidez et al. Cloud-based realtime robotic visual slam. In 2015 Annual IEEE Systems Conference (SysCon) Proceedings, pages 773–777, 2015.

[14] L. Riazuelo et al. C2tam: A cloud framework for cooperative tracking and mapping. Robotics and Autonomous Systems, 62(4):401 – 413, 2014. [15]L. Turnbull and B. Samanta. Cloud robotics: Formation control of a multi robot system utilizing cloud infrastructure. In 2013 Proceedings of IEEE Southeastcon, pages 1–4, 2013.

[16] J. Salmer o ́n-Garcı ́a et al. A tradeoff analysis of a cloud-based robot navigation assistant using stereo image processing. IEEE Transactions on Automation Science and Engineering, 12(2):444–454, 2015.

[17] Y. Hong et al. Energy efficient content-based image retrieval for mobile systems. In 2009 IEEE International Symposium on Circuits and Systems, pages 1673–1676, 2009.

[18] J. Hauswald et al. A hybrid approach to offloading mobile image classification. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8375–8379, 2014. [19]K. Kumar et al. A survey of computation offloading for mobile systems. Mobile Networks and Applications, 18(1):129–140, 2013.

[20] X. Wang et al. Offloading in internet of vehicles: A fog-enabled real- time traffic management system. IEEE Transactions on Industrial Informatics, 14(10):4568–4578, 2018.

[21] T. N. Gia et al. Fog computing approach for mobility support in internet-of-things systems. IEEE Access, 6:36064–36082, 2018.

[22] C. V. Smith et al. Enhanced situational awareness in autonomous mobile robots using context-based mapping. In 2013 IEEE CogSIMA, pages134–138, 2013.

[23] Chauhan, S., & Vermani, S. (2016). Shift from Cloud Computing to Fog Computing. Journal of Applied Computing, 1(1), 25-29.

[24] C. Briese et al. Cloud-based active disturbance rejection control for industrial robots. In 2018 IEEE ETFA, pages 559–565, 2018.

[25] Tadapaneni, N. R. (2016). Overview and Opportunities of Edge Computing. Social Science Research Network.

[26] S. D. Levy. Breezyslam. [Online] Available: https://github.com/simondlevy/BreezySLAM. Accessed: 20 Feb. 2019.

[27] T. N. Gia et al. Fog computing in healthcare internet of things: A case study on ecg feature extraction. In 2015 IEEE CIT, pages 356–363. IEEE, 2015.

[28] N. Abbas et al. Mobile edge computing: A survey. IEEE Internet of Things Journal, 5(1):450–465, 2018.

[29] Slamtec. RPLIDAR A1. [Online] Available: http://www.slamtec.com/en/lidar/a1. Accessed: 20 Feb. 2019.

[30] W. Yu et al. A survey on the edge computing for the internet of things.IEEE access, 6:6900–6919, 2018.


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