Environmental Sampling with the Boustrophedon Decomposition Algorithm

Abstract-- The automation of data collection via mobile robots holds promise for increasing the efficacy of environmental investigations, but requires the system to autonomously determine how to sample the environment while avoiding obstacles. Downsampling these paths can result in feasible plans at the expense of distribution estimation accuracy. This work explores this tradeoff between distribution accuracy and path length for the boustrophedon decomposition algorithm. We quantify algorithm performance by computing metrics for accuracy and path length in a Monte-Figure 1: An example environment for autonomous sampling. These results demonstrate how intelligent deployment of the boustrophedon algorithm can effectively guide autonomous environmental sampling. These algorithms must be able to Environmental sampling is the process of extracting information appropriately cover the area of interest with measurements from a given environment by collecting measurements to estimate the underlying contaminant distribution or locate at different locations and analyzing the data. They must example, environmental sampling has been used for mineral also ensure that the robot is able to feasibly traverse the prospecting [1], characterization of algae blooms [2], and air resulting path, and therefore must reason about obstacles particle monitoring [3].