Stochastically optimized monocular vision-based guidance design

This paper designs a relative navigation and guidance system for unmanned aerial vehicles for monocular vision-based control applications. Since 2-D vision-based measurement is nonlinear with respect to the 3-D relative state, an extended Kalman filter (EKF) is applied in the navigation system design. It is well-known that the vision-based estimation performance highly depends on the relative motion of the vehicle to the target. Therefore, a main objective of this paper is to derive an optimal guidance law to achieve given missions under the condition that the EKF-based relative navigation outputs are fed back to the guidance system. This paper suggests a stochastic optimal guidance design that minimizes the expected value of a cost function of the guidance error and control effort subject to the EKF prediction and update procedures. A one-step-ahead suboptimal optimization technique is implemented to avoid iterative computations. The approach is applied to vision-based target tracking and obstacle avoidance, and simulation results are illustrated.