We present an approach to planning dynamically feasible vehicle trajectories for applications where the vehicle operates in very large environments and for which a kinematic model is poor approximation of the vehicle's dynamics. Using a standard receding horizon control framework, we compute a sequence of short-horizon optimal trajectories that can be computed in real-time. We also compute a global path that is updated only occasionally. Because the environment is very large, we presume that the global path cannot be updated in real-time. By selecting a terminal state cost in the finite-time optimal control problem that approximates the cost-to-go, we are able to propose a formal condition under which the sequence of dynamically feasible trajectories is guaranteed to converge to a desired goal location. Moreover, we show that the global path need be updated only when the formal condition is not satisfied, which allows us to delay update of the global path.

Autonomous operation by a surface vehicle in a maritime setting requires that the surface vehicle detects non-water objects, including shoreline, hazards to navigation, and other moving vessels. In order to assess the utility of an omnidirectional camera for detecting and localizing non- water objects, we rigorously investigate observability of both stationary and moving features. For stationary features, we find that all but a small subset of the features are observable. For moving features, we show that an important class of feature and ASV trajectories are not observable.

An underwater navigation algorithm is considered that enables an underwater vehicle to compute its trajectory and unknown currents by utilizing range measurements from a single known location. By assessing local observability about potential vehicle trajectories, we characterize those trajectories that cannot be asymptotically estimated. Analysis is also presented to clarify the relationship between finite time observability, a theme of this work, and existence of an observer with exponentially decaying observation dynamics. The navigation algorithm is illustrated using a hardware experiment.

Navigation technology is considered that enables an underwater vehicle to compute its trajectory in real-time by utilizing range measurements from a single known location. This concept is especially attractive for small underwater vehicles where more traditional navigation technology is prohibitive due to volume constraints. By assessing local observability of the underwater vehicle and available measurements about potential vehicle trajectories, we explicitly characterize those trajectories that can be asymptotically estimated. It is shown that all but a small class of trajectories can be estimated.

Platoons of cooperating autonomous underwater vehicles have the potential to contribute significantly to scientific investigations in the marine environment. Platoons of vehicles can survey large areas, adaptively track and measure time-varying processes such as tidal fronts and algal blooms, and they are robust to single-point failures. We have developed a prototype miniature low-cost autonomous underwater vehicle to address the platform requirements of these missions. The vehicle is designed as a test-bed for the development of distributed control and estimation algorithms, and for experiments in advanced navigation and control.