Evaluation of Cooperative Navigation Strategies with Maneuvering UAVs

Abstract

This thesis presents and evaluates cooperative navigation methods used to reduce navigation solution error growth between members of an unmanned aerial vehicle - unmanned ground vehicle (UAV-UGV) or all-UAV team when Global Position System (GPS) measurements are partially or completely unavailable to the group. Multiple scenarios with varying numbers of vehicles were simulated with a centralized navigation algorithm based on the Extended Kalman Filter (EKF) and with a decentralized navigation algorithm based on the Covariance Intersection (CI) filter. Measurements including relative range, relative range-rate, and relative bearing were made available to the vehicles in different simulation runs to compare their impact on navigation state observability and navigation state estimation accuracy. The UAVs were also guided along varied trajectories of a ``spiral" class during different simulation runs to investigate whether estimation accuracy can be improved by varying inter-vehicle dynamics and geometry.

The results of these studies show that cooperative navigation is a promising strategy to reduce navigation state error growth. To analyze the observability of the studied scenarios, a condition number test was performed on the observability Gramian matrix. This study indicates that the navigation state observability in cooperative navigation scenarios where a kinematic vehicle model is aided with relative measurements can be improved by the proposed vehicle maneuver. As the rate of the proposed spiral maneuver is increased, this analysis suggests an improvement in observability. This result is further validated in the simulated results which show that with relative bearing only, even low rates of inter-vehicle spiral motion allow for estimates of relative position with less than 3 meters of error. As the spiral rate increases, accurate relative positioning is shown to be possible with only relative range measurements. IMU biases are also shown to be estimated for cooperative groups with low meter-level relative positioning error but no absolute position reference. In scenarios where the vehicles can accurately estimate their relative positions and at least one vehicle in the cooperative group has access to accurate GPS information, all of the vehicles in the cooperative group benefit equally through communication with that vehicle. In UAV-UGV scenarios, the cooperative group includes a heterogeneous mixture of vehicles equipped with high and low quality inertial navigation systems (INS) and/or alternative navigation methods. In this case, if the vehicles can estimate their relative positions to meter-level accuracy, all cooperating vehicles benefit with navigation solution error characteristics matching that of the most accurate navigation system in the group.

Lastly, experimentally collected data was analyzed to validate the simulation results. This experiment demonstrated similar results to the simulated scenarios. Relative position error was reduced from over 100 meters to sub-meter accuracy, depending on relative measurement availability. Absolute error was also reduced from over 70 meters (in the IMU-only case) to meter-level accuracy depending on measurement availability.