Nonlinear Model Predictive Control to Aid Cooperative Localization

This paper proposes a nonlinear model predictive control (NMPC) scheme to tackle the problem of localization and path planning of a group of unmanned aerial vehicles (UAVs) in global positioning system (GPS) denied environments. It is assumed that the UAVs can cooperate by sharing information among themselves. It is also assumed that the area under consideration contains some landmarks with known locations. The NMPC computes the optimal control inputs for the vehicles such that the vehicles cooperate to transit from a source location to a destination while choosing a path that will cover enough landmarks for localization. An Extended Kalman Filter (EKF) is used to estimate the vehicle positions using only relative bearing measurements. The efficacy of the proposed method was evaluated through numerical simulations, and the results are discussed.