Date of Award
Master of Science
Laura L. Alvarez
Robert R. Roberts
Unmanned Surface Vehicles (USVs) have been applied to earth sciences, with only a few studies conducted in water environments, as these systems provide autonomous measurement capabilities and transferability to other environmental settings. In this thesis, a reliable, yet economical, USV has been developed for bathymetric surveying of lakes. The system combines an autonomous navigation framework, environmental sensors and a multibeam echosounder to collect submerged topography, temperature, windspeed and monitor the vehicle status during prescribed path planning missions.
The main objective of this study is to provide a methodological framework to build a USV, with independent decision-making, efficient control, and long-range navigation capabilities. Integration of sensors with navigation control enabled the automatization of position, orientation, and velocity of the vehicle. A solar power integration was also tested to control the duration of the autonomous missions. Results of the solar power compared favorable against the standard LiPO Battery System. Extended and autonomous missions were achieved, with the developed platform, that is also capable of evaluating the danger level, weather circumstances, and energy consumption through real-time data analysis. With all incorporated sensors and controls, this USV is able to achieving self-governing decisions and improving its own safety. A technical evaluation of the proposed vehicle was conducted as a measurable metric of the reliability and robustness of the prototype. Overall, a reliable, economical and self-powered autonomous system has been designed and built to retreive bathymetric surveys, as a first step to develop intelligent systems for reconnaissance that combines field robotics with machine learning to make decisions an adapt to unknown environments.
Received from ProQuest
Fernando Sotelo Torres
Sotelo Torres, Fernando, "An Unmanned Surface Vehicle: Autonomous Sensor Integration System for Bathymetric Surveys" (2022). Open Access Theses & Dissertations. 3730.