Design of intelligent thruster decision-making system for USVs

Marine environmental surveys using unmanned surface vessels (USVs) can be a challenging task, especially if the surveyed area takes several weeks to cover. There is also the constant risk of depleting the battery before the mission is completed, which is associated with the challenge of vehicle power management. Thrusters in unmanned vehicles are the main power drainers. Waves, currents, and wind unpredictable behavior have a great influence on the motion of the vehicle and, hence, affect whether the vehicle is to be able to fulfill its mission in the allocated time. The primary objective of the present research is to design an algorithm that optimize USV's power consumption by predicting the amount of power devoted to the thruster as a function of time. Thruster power predictions were performed by a genetic algorithm that uses battery, vehicle speed, solar power, and wave height as well as wave period information to forecast generated and consumed electric power. The Wave Glider was utilized as the USV of study in this work. Simulation results showed that the presented algorithm outperforms a human pilot in reducing thruster power utilization per unit distance by 17%, producing semi-consistent thruster activation plan that satisfy mission objectives as well as constraints. •Research explores energy-flow relationship between thruster force and electrical power in wave-powered USV.•A genetic algorithm was used to produce semi-consistent battery-saving plans despite stochasticity.•The resultant thruster action plans outperformed human pilots, saving over 20% of energy on real missions.•Proposed algorithm can help human pilots in mission planning and reacting properly when battery is at critical level.•Training and validation dataset was collected in a field mission, filtered and prepared specifically for this research.