Sampling-based path planning and model predictive image-based visual servoing for quadrotor UAVs

This paper incorporates sampling-based global path planning with model predictive image-based visual servoing (IBVS) for quadrotor unmanned aerial vehicles (UAVs) equipped with a fixed camera. The proposed method produces safe control inputs of quadrotor in obstacle environment by taking image feature kinematics into account. Firstly, we utilize a sampling-based algorithm for optimal path planning, i.e., rapidly-exploring random tree (RRT*), to extend a search tree in camera space iteratively and generate a sequence of waypoints between the initial and desired poses of the camera. Afterwards, a model predictive EBVS scheme is proposed by optimizing a cost function of the predicted image trajectories under the constraints of visibility and velocity. The scheme can avoid the intractable problems of the classical IBVS approaches, especially when there are large displacements between the initial and the desired poses of the quadrotor UAV. The simulations on a quadrotor UAV demonstrate the validity and effectiveness of the proposed scheme.