This work considers a hybrid Visual Servoing technique for differentially flat underactuated systems. Given a goal image, the objective is to navigate to a position which aligns the camera with the desired image while keeping salient features in view along the way. We formulate the problem as an optimal control problem that can be solved using Gauss-Newton optimization. As a cost function, we use the average squared distance between SURF features matched between the desired image and the image projected from the final state of the trajectory. The optimization is performed over a polynomial parametrization of the flat outputs of the system to decrease the dimensionality of the optimization. Preliminary experimental results show that the technique has better performance compared to a typical position-based visual servoing system for the case considered, which is a simulated quadrotor with a front-facing camera. This method assumes feature depths are known or can be estimated (e.g. using stereo camera or structure from motion algorithms).
This work considers Adaptive Model Predictive Control of Quadrotor Vehicles. A nonlinear model of the quadrotor is estimated online. This model is used to plan safe trajectories around obstacles. The optimal trajectory accounts for the uncertainty in the state space, and also minimizes the control effort while achieving a goal state at the same time.