For decades, Remotely Operated underwater Vehicles (ROVs) have been helping mankind explore the depths of the ocean, and build and maintain infrastructure on the seafloor. Since the first ROV was developed in 1953, the number of uses for these vehicles has exploded. They are now an essential part of maintaining the world's energy resources, collecting scientific data about our oceans, and performing underwater search and recovery.

This research will discuss guidance, navigation, and control algorithms for use as a low-level position controller for ROVs, which will enable semi-autonomous behaviour for the vehicle. Semi-autonomous behaviour is when the pilot issues high-level position commands and the low-level controller handles station keeping and maneuvering between the commanded positions. In this configuration, the low level controller compensates for the environmental disturbances and unknown dynamics (such as current and tether dynamics), allowing the pilot to focus on other aspects of the task (such as manipulator control).

In this work, the design,implementation,and testing of a complete guidance, navigation, and control system is presented. A Saab Sea-Eye Falcon ROV is augmented with a suite of navigation instruments. The augmented vehicle is characterized and a dynamic model is developed. This model is used in an extended Kalman filter, which will be shown to produce a position estimate for the vehicle with an error of less than ±6 cm. The navigation system is combined with a guidance system and adaptive controller to enable semi-autonomous behaviour. With this suite of software, the ROV can operate semi-autonomously. The resulting ROV system is a research platform, from which the underwater community can continue research into algorithms for optimal control, remote operations, and other performance enhancing technologies.