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Map-reduce Gaussian process (MR-GP) for multi-UAV based environment monitoring with limited battery

Environment monitoring is a challenging task owing to its ever changing dynamics. Furthermore, deploying a team of resource constrained robots to persistently monitor the environment encompasses intelligently selecting the training samples which are spread across a significantly large area to conservatively spend the resources allocated. In order to accomplish this using a team of fully autonomous self-reliant robots, we pose this problem as a map-reduce architecture: Map phase involves each individual member gathering its training samples and generating the best possible model of the environment followed by the Reduce phase where we merge all these models into a single globally consistent model to infer the environment dynamics. Our preliminary contributions to both these phases have shown significant ease to parallelize the process of gathering training samples whilst reducing the over-all model uncertainty. We demonstrated these results in a communication devoid simulated environment using publicly available datasets.