Three dimensional unmanned aerial vehicle path planning by a continuous optimization method

Three-dimension (3D) path planning is a core and fundamental component to achieve high levels of autonomy for unmanned aerial vehicles (UAVs). Most of the existing methods determine the optimal path based on a discrete representation of the environmental data, which may produce metrication errors. In order to remedy this problem, we present a novel 3D path plan method, based on the framework of fast marching, to determine the optimal path for UAVs. The proposed algorithm is inspired by the phenomenon of front propagation, which is consistent with continuous front propagation rules. It allows for producing continuous and smoothed trajectory without further processing, thus improving the overall quality of the planned path. The efficiency and accuracy of the proposed approach are demonstrated by comparative simulations. The results show that the proposed algorithm outperforms commonly used A∗ based and population optimization based planners in terms of the efficiency and accuracy.