Comparative Analysis of Optimal Path Planning Algorithms Using Deep Learning Technique

In this paper effective algorithms of optimal route planning in the presence of threat zones and uncertainties with the use of deep learning technology are considered. At the present time, in theory, there are a huge number of algorithms to determine the shortest route, which are widely used to create optimal aircrafts pathways in order to achieve their required destination. A comparative analysis of two investigated methods is carried out: A<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (A-star) and Dijkstra’s algorithm. The discussed algorithms are compared according to two criteria: the length of the shortest trajectory found and searching steps count. The analytical and graphical results of the comparison are presented. Because of the artificial intellect development, there are many possibilities for applying deep learning methods to build optimal path planning algorithms and methodology in the future. Conclusions about considered methods advantages and their application field have been made according to the performed estimation.