Unmanned vehicles

This thesis presents the concept of dynamics-aware parabolic blends for an unmanned surface vehicle. Typically, trajectory generation techniques consider only kinematic constraints on a vehicle. By transforming the equations of motion for a surface vehicle to the body fixed frame, the dynamical constraints on the system are more intuitively integrated into the trajectory generator, when compared to working in the Earth fixed frame. Additionally, the accelerations, velocities, and positions generated by the parabolic blend algorithm are incorporated into the dynamic equations of motion for the vehicle to provide the feedforward control input of a two degree of freedom control law. The feedback control input of the two degree of freedom scheme is an integral sliding mode control law, which tracks the error between the vehicle state and the desired states generated by the novel parabolic blend technique. The approach is numerically validated through simulation, where the described control law demonstrates a 71.93% reduction in error when compared to a standard proportional-derivative control law subjected to the same desired trajectory. Furthermore, on water experiments were performed using both a proportional-derivative control law and an integral sliding mode control law. Both showed the ability to track the proposed parabolic blend approach.

The research shows a novel approach for the Magnetic Anomaly Differentiation and Localization Algorithm, which simultaneously localizes multiple magnetic anomalies with weak total field signatures (tens of nT). In particular, it focuses on the case where there are two homogeneous targets with known magnetic moments. This was done by analyzing the magnetic signals and adapting Independent Component Analysis (ICA) and Simulated Annealing (SA) to solve the problem statement. The results show the groundwork for using a combination of fastICA and SA to give localization errors of 3 meters or less per target in simulation and achieved a 58% success rate. Experimental results experienced additional errors due to the effects of magnetic background, unknown magnetic moments, and navigation error. While one target was localized within 3 meters, only the latest experimental run showed the second target approaching the localization specification. This highlighted the need for higher signal-to-noise ratio and equipment with better navigational accuracy. The data analysis was used to provide recommendations on the needed equipment to minimize observed errors and improve algorithm success.