Real Time Motion Planning for Path Coverage with Applications in Ocean Surveying

Ocean surveying is the acquisition of acoustic data representing various features of the seafloor and the water above it, including water depth, seafloor composition, the presence of fish, and more. Historically, this was a task performed solely by manned vessels, but with advances in robotics and sensor technology, autonomous surface vehicles (ASVs) with sonar equipment are beginning to supplement and replace their more costly crewed counterparts. The popularity of these vessels calls for advances in software to control them. In this thesis we define the problem of path coverage to represent and generalize that of ocean surveying, and propose a real-time motion planning algorithm to solve it. We prove theorems of completeness and local asymptotic optimality regarding the proposed algorithm, and evaluate it in a simulated environment. We also discover a lack of robustness in the Dubins vehicle model when applied to real-time motion planning. We implement a model-predictive controller and other components for an autonomous surveying system, and evaluate it in simulation. The system documented in this thesis takes a step towards fully autonomous ocean surveying, and proposes further extensions that get even closer to that goal