Three-dimensional Path Planning for Unmanned Aerial Vehicle (UAV) Based on Improved Mayfly Algorithm

Abstract

A three-dimensional (3D) path planning method based on the improved mayfly algorithm (IMA) is proposed in this paper for the unmanned aerial vehicle (UAV) path planning problem under the condition of diverse static features and obstacle threats. Firstly, the 3D flight area environment model with obstacles is built. Then, the path planning method is developed, which can increase the global search capability by keeping population diversity with the improved Tent chaotic map, and balance the global and local searching capability through incorporating the dynamic adaptive inertia weight into the algorithm. In addition, Gaussian mutation strategy is used to increase the solution accuracy and the ability of the algorithm jumping out from the local optimum. Finally, the optimal collision-free flight path is obtained by smoothing the planned path using the cubic B-spline curve. Results show that the developed algorithm can plan a smooth flight path, and avoid obstacle threats.