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On the Robot Path Planning using Cloud Computing for Large Grid Maps
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Orientador(es)
Resumo(s)
Global path planning consists in finding the optimal
path for a mobile robot with the lowest cost in the minimum
amount of time, without colliding with the obstacles scattered
in the workspace. In this paper, 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 RA∗ [1], a version of A∗
that we developed for grid maps and that was proven to be
much faster than A∗, 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. Experimental results on
a real cloud shows that the distributed graph processing Giraph
fails to provide faster execution as compared to centralized C++
implementation for different map sizes and configuration due to
non-real time properties of Hadoop.
Descrição
Palavras-chave
Autonomous robots Cloud computing C++ language Graph theory
Contexto Educativo
Citação
Editora
Institute of Electrical and Electronics Engineers