Authors: Ibrahim Salman, Jason Raiti, Nare Karapetyan, Archana Venkatachari, Annie Bourbonnais, Jason O'Kane, Ioannis Rekleitis
Abstract: This paper presents a novel algorithm for monitoring marine environments utilizing a resource\hyp constrained robot. Collecting water quality data from large bodies of water is paramount for monitoring the ecosystem’s health, particularly for predicting harmful cyanobacteria blooms. The large spatial dimensions of such bodies of water and the slow varying of water quality parameters make exhaustive, complete coverage impractical and unnecessary. This work explores a new strategy for efficiently measuring water quality quantities with an autonomous surface vehicle (ASV). The method utilizes the medial axis of the water body producing a guideline for the ASV trajectory that visits representative areas of the environment. The proposed method ensures data collection in the narrower parts of the lake, where researchers have historically observed harmful blooms while also visiting open water areas. It also presents an analysis of the Spatio-temporal sensitivity of the target sensor. A comparison with the traditional lawnmower algorithm demonstrates that the conventional BCD-based complete coverage method cannot sample the small coves of a lake. As such, we show that the proposed method captures more diverse regions of the area with a partial coverage technique. Offline analysis of several lakes and reservoirs and results from field deployments at Lake Murray, SC, USA, demonstrate the proposed method’s effectiveness.