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ElasticROS: An Elastically Collaborative Robot Operation System for Fog and Cloud Robotics (2209.01774v2)

Published 5 Sep 2022 in cs.RO

Abstract: Robots are integrating more huge-size models to enrich functions and improve accuracy, which leads to out-of-control computing pressure. And thus robots are encountering bottlenecks in computing power and battery capacity. Fog or cloud robotics is one of the most anticipated theories to address these issues. Approaches of cloud robotics have developed from system-level to node-level. However, the present node-level systems are not flexible enough to dynamically adapt to changing conditions. To address this, we present ElasticROS, which evolves the present node-level systems into an algorithm-level one. ElasticROS is based on ROS and ROS2. For fog and cloud robotics, it is the first robot operating system with algorithm-level collaborative computing. ElasticROS develops elastic collaborative computing to achieve adaptability to dynamic conditions. The collaborative computing algorithm is the core and challenge of ElasticROS. We abstract the problem and then propose an algorithm named ElasAction to address. It is a dynamic action decision algorithm based on online learning, which determines how robots and servers cooperate. The algorithm dynamically updates parameters to adapt to changes of conditions where the robot is currently in. It achieves elastically distributing of computing tasks to robots and servers according to configurations. In addition, we prove that the regret upper bound of the ElasAction is sublinear, which guarantees its convergence and thus enables ElasticROS to be stable in its elasticity. Finally, we conducted experiments with ElasticROS on common tasks of robotics, including SLAM, grasping and human-robot dialogue, and then measured its performances in latency, CPU usage and power consumption. The algorithm-level ElasticROS performs significantly better than the present node-level system.

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References (26)
  1. K. E. Chen, Y. Liang, N. Jha, J. Ichnowski, M. Danielczuk, J. Gonzalez, J. Kubiatowicz, and K. Goldberg, “FogROS: An Adaptive Framework for Automating Fog Robotics Deployment,” in 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE).    Lyon, France: IEEE, Aug. 2021, pp. 2035–2042.
  2. J. Ichnowski, K. Chen, K. Dharmarajan, S. Adebola, M. Danielczuk, V. Mayoral-Vilches, H. Zhan, D. Xu, R. Ghassemi, J. Kubiatowicz, I. Stoica, J. Gonzalez, and K. Goldberg, “FogROS 2: An Adaptive and Extensible Platform for Cloud and Fog Robotics Using ROS 2,” May 2022.
  3. J. Kuffner, “Cloud-enabled humanoid robots,” in Humanoid Robots (Humanoids), 2010 10th IEEE-RAS International Conference on, Nashville TN, United States, Dec., 2010.
  4. M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, A. Y. Ng et al., “Ros: an open-source robot operating system,” in ICRA workshop on open source software, vol. 3, no. 3.2.    Kobe, Japan, 2009, p. 5.
  5. S. Macenski, T. Foote, B. Gerkey, C. Lalancette, and W. Woodall, “Robot operating system 2: Design, architecture, and uses in the wild,” Science Robotics, vol. 7, no. 66, p. eabm6074, 2022.
  6. T. Kronauer, J. Pohlmann, M. Matthé, T. Smejkal, and G. Fettweis, “Latency analysis of ros2 multi-node systems,” in 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).    IEEE, 2021, pp. 1–7.
  7. M. Inaba, “Remote-brained robots,” pp. 1593–1606, 1997.
  8. J. J. K. et al., “Cloud-enabled robots,” in International Conference on humanoid robotics.    IEEE-RAS, 2010.
  9. B. Kehoe, S. Patil, P. Abbeel, and K. Goldberg, “A survey of research on cloud robotics and automation,” IEEE Transactions on automation science and engineering, vol. 12, no. 2, pp. 398–409, 2015.
  10. M. Waibel, M. Beetz, J. Civera, R. d’Andrea, J. Elfring, D. Galvez-Lopez, K. Häussermann, R. Janssen, J. Montiel, A. Perzylo et al., “Roboearth,” IEEE Robotics & Automation Magazine, vol. 18, no. 2, pp. 69–82, 2011.
  11. L. Riazuelo, M. Tenorth, D. Di Marco, M. Salas, D. Gálvez-López, L. Mösenlechner, L. Kunze, M. Beetz, J. D. Tardós, L. Montano et al., “Roboearth semantic mapping: A cloud enabled knowledge-based approach,” IEEE Transactions on Automation Science and Engineering, vol. 12, no. 2, pp. 432–443, 2015.
  12. B. Liu, L. Wang, and M. Liu, “Lifelong federated reinforcement learning: a learning architecture for navigation in cloud robotic systems,” IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 4555–4562, 2019.
  13. B. Liu, L. Wang, M. Liu, and C.-Z. Xu, “Federated imitation learning: A novel framework for cloud robotic systems with heterogeneous sensor data,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3509–3516, 2020.
  14. B. Liu, L. Wang, X. Chen, L. Huang, D. Han, and C.-Z. Xu, “Peer-assisted robotic learning: a data-driven collaborative learning approach for cloud robotic systems,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).    IEEE, 2021, pp. 4062–4070.
  15. M. Karrer, P. Schmuck, and M. Chli, “Cvi-slam—collaborative visual-inertial slam,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 2762–2769, 2018.
  16. J. Mahler, F. T. Pokorny, B. Hou, M. Roderick, M. Laskey, M. Aubry, K. Kohlhoff, T. Kröger, J. Kuffner, and K. Goldberg, “Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards,” in 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 1957–1964.
  17. T. L. Lai, H. Robbins et al., “Asymptotically efficient adaptive allocation rules,” Advances in applied mathematics, vol. 6, no. 1, pp. 4–22, 1985.
  18. X. Guo, X. Wang, and X. Liu, “Adalinucb: opportunistic learning for contextual bandits,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019, pp. 2420–2427.
  19. K. Jamieson, M. Malloy, R. Nowak, and S. Bubeck, “lil’ucb: An optimal exploration algorithm for multi-armed bandits,” in Conference on Learning Theory.    PMLR, 2014, pp. 423–439.
  20. L. Zhang, L. Chen, and J. Xu, “Autodidactic neurosurgeon: Collaborative deep inference for mobile edge intelligence via online learning,” in Proceedings of the Web Conference 2021, 2021, pp. 3111–3123.
  21. Y. Abbasi-Yadkori, D. Pál, and C. Szepesvári, “Improved algorithms for linear stochastic bandits,” Advances in neural information processing systems, vol. 24, 2011.
  22. B. Hubert, J. Geul, and S. Séhier, “Wondershaper: Command-line utility for limiting an adapter’s bandwidth,” URl: https://github. com/magnific0/wondershaper, 2021.
  23. Z. Teed and J. Deng, “Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras,” Advances in Neural Information Processing Systems, vol. 34, pp. 16 558–16 569, 2021.
  24. luigifreda, “pySLAM V2,” https://github.com/luigifreda/pyslam/, 2022.
  25. F.-J. Chu, R. Xu, and P. A. Vela, “Real-world multiobject, multigrasp detection,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3355–3362, 2018.
  26. P. Beckmann, M. Kegler, H. Saltini, and M. Cernak, “Speech-vgg: A deep feature extractor for speech processing,” arXiv preprint arXiv:1910.09909, 2019.
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Authors (1)
  1. Boyi Liu (49 papers)
Citations (6)
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