Informative path planning for algae farm surveying

Abstract: Algae farms need to be regularly monitored to control growth rate or check for signs of diseases. As marine farms are setup farther from the coasts, the need for developing robots capable of carrying out these tasks autonomously increases. This thesis investigates trajectory generation for an underwater robot monitoring an algae farm. It encompasses a Kalman Filter used to update a Gaussian Process, used to store information about the algae, Informative Path Planning to generate trajectories which maximise information gain during the survey, and a Linear Time Varying Model Predictive Controller capable of tracking complex three dimensional paths. A simulation of the farm, the robot and its sensors is used to validate the efficiency of the proposed method.