Abstract-Underwater operations present unique challenges and opportunities for robotic applications. These can be attributed in part to limited sensing capabilities, and to locomotion behaviours requiring control schemes adapted to specific tasks or changes in the environment. From enhancing teleoperation procedures, to providing high-level instruction, all the way to fully autonomous operations, enabling autonomous capabilities is fundamental for the successful deployment of underwater robots. This paper presents an overview of the approaches used during underwater sea trials in the coral reefs of Barbados, for two amphibious mobile robots and a set of underwater sensor nodes. We present control mechanisms used for maintaining a preset trajectory during enhanced teleoperations and discuss their experimental results. This is followed by a discussion on amphibious data gathering experiments conducted on the beach. We then present a tetherless underwater communication approach based on pure vision for high-level control of an underwater vehicle. Finally the construction details together with preliminary results from a set of distributed underwater sensor nodes are outlined.

This paper describes work done in the modeling and control of a low speed underwater vehicle that uses paddles instead of thrusters to move in the water. A review of previously modeled vehicles and of controller designs for underwater applications is presented. Then, a method to accurately predict the thrust produced by an oscillating flexible paddle is developed and validated. This is followed by the development of a method to determine the ideal paddle motion to produce a desired thrust. Several controllers are then developed and tested using a numerical simulation of the vehicle. We found that some model-based controllers could improve the performance of the system while others showed no benefit. Finally, we report results from experimental trials performed in an open water environment comparing the performance of the controllers. The experimental results showed that all the model-based controllers outperform the simple proportional-derivative controller. The controller giving the best performance was the model-based nonlinear controller. We also found that the vehicle was able to follow a change of a roll angle of 90 degrees in 0.7 s and to precisely follow a sinusoidal trajectory with a period of 6.28 s and an amplitude of 5 degrees.

We describe a navigation and coverage system based on unsupervised learning driven by visual input. Our objective is to allow a robot to remain continuously moving above a terrain of interest using visual feedback to avoid leaving this region. As a particular application domain, we are interested in doing this in open water, but the approach makes few domain-specific assumptions. Specifically, our system employed an unsupervised learning technique to train a kNearest Neighbor classifier to distinguish between images of different terrain types through image segmentation. A simple random exploration strategy was used with this classifier to allow the robot to collect data while remaining confined above a coral reef, without the need to maintain pose estimates. We tested the technique in simulation, and a live deployment was conducted in open water. During the latter, the robot successfully navigated autonomously above a coral reef during a 20 minutes period.

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