Image-based Visual Servoing Framework for a Multirotor UAV using Sampling-based Path Planning

摘要

This paper proposes a novel image-based visual servoing (IBVS) optimal path-planning framework and reference feature tracking scheme that not only overcome the deficiencies of traditional image-based visual servoing approaches but also improve control performance and guarantee optimality. In particular, the proposed framework provides optimal path points between the initial and desired positions of a target, called the references, by adapting the rapidly-exploring random tree (RRT~*) algorithm in order to overcome the drawbacks inherent in conventional methods. In contrast to existing techniques that generate control input using the exponentially decreasing error task function depending on the initial and desired features in the image plane only, our proposed framework generates control input using several reference features located between the initial and desired features generated by the optimal path-planning results. Consequently, it can produce relatively small and bounded control inputs that facilitate better performance in large pose difference environments. One of the major advantages of our proposed framework over existing methods is that it can generate feasible maneuvers from the results of the optimal feature path planning. It can also prevent singularities and local minima because it can maintain a small value for errors using a set of reference features. The results of simulations conducted to verify the performance of the proposed framework indicate that it can return the path points for the convergence of the initial position with the desired position, even with pixel position error at the target and relative position misalignment.