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The automation of data collection via mobile robots holds promise forincreasing the efficacy of environmental investigations, but requires thesystem to autonomously determine how to sample the environment while avoidingobstacles. Existing methods such as the boustrophedon decomposition algorithmenable complete coverage of the environment to a specified resolution, yet inmany cases sampling at the resolution of the distribution would yield longpaths with an infeasible number of measurements. Downsampling these paths canresult in feasible plans at the expense of distribution estimation accuracy.This work explores this tradeoff between distribution accuracy and path lengthfor the boustrophedon decomposition algorithm. We quantify algorithmperformance by computing metrics for accuracy and path length in a Monte-Carlosimulation across a distribution of environments. We highlight conditions whereone objective should be prioritized over the other and propose a modificationto the algorithm to improve its effectiveness by sampling more uniformly. Theseresults demonstrate how intelligent deployment of the boustrophedon algorithmcan effectively guide autonomous environmental sampling.

Environmental Sampling with the Boustrophedon Decomposition Algorithm