Evolution-algorithm-based unmanned aerial vehicles path planning in complex environment

Abstract

With the wide application of Unmanned Aerial Vehicles (UAVs) in production and life, more and more attention has been paid to the autonomous track planning of UAVs. When UAV path planning algorithm is dealing with flying in an unknown complex environment, there are some problems, such as inability to dynamically plan the track and slow speed to calculate the path. This paper proposes a dynamic path planning based on an improved evolutionary optimization algorithm. The experimental results show that the evolutionary optimization algorithm based on improved t-distribution can effectively deal with the problems of high computational complexity and low search efficiency encountered in UAV dynamic track planning. It has strong robustness and can dynamically plan the appropriate track.