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 Software
as a Service (SaaS) Model. This reduces the burden of exploration and map
building for the new robot and minimizes its 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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