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Environmental monitoring and surveying operations on rivers currently areperformed primarily with manually-operated boats. In this domain, autonomouscoverage of areas is of vital importance, for improving both the quality andthe efficiency of coverage. This paper leverages human expertise in riverexploration and data collection strategies to automate and optimize theseprocesses using autonomous surface vehicles(ASVs). In particular, threedeterministic algorithms for both partial and complete coverage of a riversegment are proposed,providing varying path length, coverage density, andturning patterns. These strategies resulted in increases in accuracy andefficiency compared to human performance.The proposed methods were extensivelytested in simulation using maps of real rivers of different shapes and sizes.In addition, to verify their performance in real world operations, thealgorithms were deployed successfully on several parts of the Congaree River inSouth Carolina, USA, resulting in total of more than 35km of coveragetrajectories in the field.

Riverine Coverage with an Autonomous Surface Vehicle over Known Environments