  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. Existing methods such as the boustrophedon decomposition algorithm
enable complete coverage of the environment to a specified resolution, yet in
many cases sampling at the resolution of the distribution would yield long
paths with an infeasible number of measurements. 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-Carlo
simulation across a distribution of environments. We highlight conditions where
one objective should be prioritized over the other and propose a modification
to the algorithm to improve its effectiveness by sampling more uniformly. These
results demonstrate how intelligent deployment of the boustrophedon algorithm
can effectively guide autonomous environmental sampling.
