Riverine Coverage with an Autonomous Surface Vehicle over Known Environments
This work addresses the problem of inefficient and labor-intensive river surveying for environmental monitoring, offering an incremental improvement through automation.
The paper tackled automating riverine environmental monitoring by proposing three deterministic algorithms for autonomous surface vehicles to cover river segments, resulting in increased accuracy and efficiency compared to human performance, with field tests covering over 35km on the Congaree River.
Environmental monitoring and surveying operations on rivers currently are performed primarily with manually-operated boats. In this domain, autonomous coverage of areas is of vital importance, for improving both the quality and the efficiency of coverage. This paper leverages human expertise in river exploration and data collection strategies to automate and optimize these processes using autonomous surface vehicles(ASVs). In particular, three deterministic algorithms for both partial and complete coverage of a river segment are proposed,providing varying path length, coverage density, and turning patterns. These strategies resulted in increases in accuracy and efficiency compared to human performance.The proposed methods were extensively tested in simulation using maps of real rivers of different shapes and sizes. In addition, to verify their performance in real world operations, the algorithms were deployed successfully on several parts of the Congaree River in South Carolina, USA, resulting in total of more than 35km of coverage trajectories in the field.