Abstract
We introduce a method for splitting the computation of a robot’s motion plan between the robot’s low-power embedded computer, and a high-performance cloud-based compute service. To meet the requirements of an interactive and dynamic scenario, robot motion planning may need more computing power than is available on robots designed for reduced weight and power consumption (e.g., battery powered mobile robots). In our method, the robot communicates its configuration, its goals, and the obstacles to the cloud-based service. The cloud-based service takes into account the latency and bandwidth of the connection between it and the robot and computes and returns a motion plan within the time frame necessary for the robot to meet requirements of a dynamic and interactive scenario. The cloud-based service parallelizes construction of a roadmap, and returns a sparse subset of the roadmap giving the robot the ability to adapt to changes between updates from the server. In our results, we show that with typical latency and bandwidth limitations, our method gains significant improvement in the responsiveness and quality of motion plans in interactive scenarios.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Amazon: EC2 instance pricing. https://aws.amazon.com/ec2/pricing/ Accessed: 2016-07.
Amato, N.M., Dale, L.K.: Probabilistic roadmap methods are embarrassingly parallel. In: Proc. IEEE Int. Conf. Robotics and Automation (ICRA). (May 1999) 688–694
Reif, J.H.: Complexity of the Mover’s Problem and Generalizations. In: 20th Annual IEEE Symp. on Foundations of Computer Science. (1979) 421–427
Mell, P., Grance, T.: The NIST definition of cloud computing. http://dx.doi.org/10.6028/NIST.SP.800-145 (2011)
Kehoe, B., Patil, S., Abbeel, P., Goldberg, K.: A survey of research on cloud robotics and automation. IEEE Transactions on Automation Science and Engineering 12(2) (2015) 398–409
Bekris, K., Shome, R., Krontiris, A., Dobson, A.: Cloud automation: Precomputing roadmaps for flexible manipulation. IEEE Robotics & Automation Magazine 22(2) (2015) 41–50
Kehoe, B., Matsukawa, A., Candido, S., Kuffner, J., Goldberg, K.: Cloud-based robot grasping with the google object recognition engine. In: Robotics and Automation (ICRA), 2013 IEEE International Conference on, IEEE (2013) 4263–4270
Kehoe, B., Warrier, D., Patil, S., Goldberg, K.: Cloud-based grasp analysis and planning for toleranced parts using parallelized monte carlo sampling. IEEE Transactions on Automation Science and Engineering 12(2) (2015) 455–470
Ichnowski, J., Alterovitz, R.: Scalable multicore motion planning using lock-free concurrency. IEEE Transactions on Robotics 30(5) (2014) 1123–1136
Carpin, S., Pagello, E.: On parallel RRTs for multi-robot systems. In: Proc. 8th Conf. Italian Association for Artificial Intelligence. (2002) 834–841
Otte, M., Correll, N.: C-FOREST: Parallel shortest path planning with superlinear speedup. IEEE Trans. Robotics 29(3) (2013) 798–806
ROS.org: Robot Operating System (ROS). http://ros.org (2012)
Kavraki, L.E., Svestka, P., Latombe, J.C., Overmars, M.: Probabilistic roadmaps for path planning in high dimensional configuration spaces. IEEE Trans. Robotics and Automation 12(4) (1996) 566–580
Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robotics Research 30(7) (June 2011) 846–894
Dobson, A., Bekris, K.E.: Sparse roadmap spanners for asymptotically nearoptimal motion planning. The International Journal of Robotics Research 33(1) (2014) 18–47
Marble, J.D., Bekris, K.E.: Asymptotically near-optimal planning with probabilistic roadmap spanners. IEEE Transactions on Robotics 29(2) (2013) 432–444
Likhachev, M., Gordon, G.J., Thrun, S.: ARA*: Anytime A* with provable bounds on sub-optimality. In: Advances in Neural Information Processing Systems. (2003)
Likhachev, M., Ferguson, D.I., Gordon, G.J., Stentz, A., Thrun, S.: Anytime dynamic A*: An anytime, replanning algorithm. In: ICAPS. (2005) 262–271
Van Den Berg, J., Ferguson, D., Kuffner, J.: Anytime path planning and replanning in dynamic environments. In: Proc. IEEE Int. Conf. Robotics and Automation (ICRA). (2006) 2366–2371
Fetch Robotics: Fetch research robot. http://fetchrobotics.com/research/
Fielding, R., Gettys, J., Mogul, J., Frystyk, H., Masinter, L., Leach, P., Berners- Lee, T.: Hypertext transfer protocol–http/1.1. Technical report (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Ichnowski, J., Prins, J., Alterovitz, R. (2020). Cloud-based Motion Plan Computation for Power-Constrained Robots. In: Goldberg, K., Abbeel, P., Bekris, K., Miller, L. (eds) Algorithmic Foundations of Robotics XII. Springer Proceedings in Advanced Robotics, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-43089-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-43089-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-43088-7
Online ISBN: 978-3-030-43089-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)