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DAvinCi: A cloud computing framework for service robots
Abstract
We propose DAvinCi, a software framework that provides the scalability and parallelism advantages of cloud computing for service robots in large environments. We have implemented such a system around the Hadoop cluster with ROS (Robotic Operating system) as the messaging framework for our robotic ecosystem. We explore the possibilities of parallelizing some of the robotics algorithms as Map/Reduce tasks in Hadoop. We implemented the FastSLAM algorithm in Map/Reduce and show how significant performance gains in execution times to build a map of a large area can be achieved with even a very small eight-node Hadoop cluster. The global map can later be shared with other robots introduced in the environment via a Software as a Service (SaaS) Model. This reduces the burden of exploration and map building for the new robot and minimizes it's need for additional sensors. Our primary goal is to develop a cloud computing environment which provides a compute cluster built with commodity hardware exposing a suite of robotic algorithms as a SaaS and share data co-operatively across the robotic ecosystem.
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Cites
Coordinated multi-robot exploration
Burgard, W., Moors, M., Stachniss, C., & Schneider, F. E. (2005) · IEEE Transactions on Robotics · doi:10.1109/tro.2004.839232 · Cited by 1,177
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Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving
Bresson, G., Alsayed, Z., Yu, L., & Glaser, S. (2017) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2017.2749181 · Cited by 885
A Survey of Research on Cloud Robotics and Automation
Kehoe, B., Patil, S., Abbeel, P., & Goldberg, K. (2015) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2014.2376492 · Cited by 833
Cloud robotics: architecture, challenges and applications
Hu, G., Tay, W., & Wen, Y. (2012) · IEEE Network · doi:10.1109/mnet.2012.6201212 · Cited by 488
Waibel, M., Beetz, M., Civera, J., D'Andrea, R., Elfring, J., Gálvez-López, D., Häussermann, K., Janssen, R., Montiel, J. M. M., Perzylo, A., et al. (2011) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2011.941632 · Cited by 429
C2TAM: A Cloud framework for cooperative tracking and mapping
Riazuelo, L., Civera, J., & Montiel, J. M. M. (2014) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2013.11.007 · Cited by 254
Metadata
- Authors
- Arumugam, R., Enti, V. R., Bingbing, L., Xiaojun, W., Baskaran, K., Kong, F. F., Kumar, A. S., Meng, K. D., & Kit, G. W.
- Venue
- 2010 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2010-05
- Pages
- 3084-3089
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2097299275
- Cited by
- 348
- References
- 1
- FWCI
- 26.19
- Type
- conference paper
- Language
- en
- Open access
- closed
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- The paper
- Arumugam, R., Enti, V. R., Bingbing, L., Xiaojun, W., Baskaran, K., Kong, F. F., Kumar, A. S., Meng, K. D., & Kit, G. W. (2010). DAvinCi: A cloud computing framework for service robots. 2010 IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/robot.2010.5509469
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