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Unmanned surface vehicles (USVs) are of increasing importance to a growing number of sectors in the maritime industry, including offshore exploration, marine transportation, and defense operations. A major factor in the growth in use and deployment of USVs is the increased operational flexibility that is offered through use of optimized motion planners that generate optimized trajectories. Unlike path planning in terrestrial environments, planning in the maritime environment is more demanding as there is need to assure mitigating action is taken against the significant, random, and often unpredictable environmental influences from winds and ocean currents. With the focus on these necessary requirements as the main basis of motivation, this article proposes a novel motion planner, denoted as Gaussian process motion planning 2 star (GPMP2*), extending the application scope of the fundamental Gaussian-process-based motion planner, Gaussian process motion planning 2 (GPMP2), into complex maritime environments. An interpolation strategy based on Monte Carlo stochasticity has been innovatively added to GPMP2* to produce a new algorithm named GPMP2* with Monte Carlo stochasticity, which can increase the diversity of the paths generated. In parallel with algorithm design, a robotic operating system (ROS)-based fully-autonomous framework for an advanced USV, the Wave Adaptive Modular Vessel 20, has been proposed. The practicability of the proposed motion planner as well as the fully-autonomous framework has been functionally validated in a simulated inspection missions for an offshore wind farm in ROS.

A Fully-Autonomous Framework of Unmanned Surface Vehicles in Maritime Environments Using Gaussian Process Motion Planning