Controller performance of marine robots in reminiscent oil surveys

Abstract

A class of path following and formation controllers are implemented on marine robots performing autonomous surveys in regions polluted by crude oil during the Deepwater Horizon oil spill. The controllers enable the robots to follow lines and curves, and maintain formation collectively while measuring reminiscent crude oil along their paths. The controllers are mathematically sound with proven convergence and robustness. However, their performance in the surveying missions is affected by natural disturbances caused by wind and water currents, and constraints such as sensor inaccuracy, localization errors, and network delays. This paper evaluates the performance of our controllers based on data collected during a survey performed at Grand Isle, Louisiana. These results will provide guidance for mission designs and inspire the future developments of our marine robots used to perform autonomous environmental surveys.