An unsupervised learning neural network for planning UAV full-area reconnaissance path

Abstract

To plan a UAV's full-area reconnaissance path under uncertain information conditions, an unsupervised learning neural network based on the genetic algorithm is proposed. Firstly, the environment model, the UAV model and evaluation indexes are presented, and the neural network model for planning the UAV's full-area reconnaissance path is established. Because it is difficult to obtain the training samples for planning the UAV's full-area reconnaissance path, the genetic algorithm is used to optimize the unsupervised learning neural network parameters. Compared with the traditional methods, the evaluation indexes constructed in this paper do not need to specify UAV maneuver rules. The offline learning method proposed in the paper has excellent transfer performances. The simulation results show that the UAV based on the unsupervised learning neural network can plan effective full-area reconnaissance paths in the unknown environments and complete full-area reconnaissance missions.

Keywords

• unmanned aerial vehicle (uav)
• full-area reconnaissance
• neural network
• genetic algorithm
• unsupervised learning