Abstract
As discussed in Chap. 3, \(A^{*}\) algorithm and its variants are the main mechanisms used for grid path planning. On the other hand, with the emergence of cloud robotics, recent studies have proposed to offload heavy computation from robots to the cloud, to save robot energy and leverage abundant storage and computing resources in the cloud. In this chapter, we investigate the benefits of offloading path planning algorithms to be executed in the cloud rather than in the robot. The contribution consists in developing a vertex-centric implementation of the \(RA^{*}\), a version of \(A^{*}\) that we developed for grid maps and that was proven to be much faster than \(A^{*}\) (refer to Chap. 3), using the distributed graph processing framework Giraph that rely on Hadoop. We also developed a centralized cloud-based C++ implementation of the algorithm for benchmarking and comparison purposes.
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Koubaa, A. et al. (2018). Robot Path Planning Using Cloud Computing for Large Grid Maps. In: Robot Path Planning and Cooperation. Studies in Computational Intelligence, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-77042-0_5
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