Hovering control of UAV based on autonomous mapping approach

The autonomous learning capability of UAV (unmanned aerial vehicle) has gained more and more attentions of researchers. As far as helicopter steering is concerned, instead of calculating dynamics of UAV in a relative short interval, pilots just take their experience and consciousness instantaneously, known as similar status has similar action, to keep the helicopter hovering. The experience and the consciousness of pilots store the mapping from the similar environment to the corresponding similar decisions. Motivated by the process, we establish the UAV mapping base from environment to decision with IHDR (Incremental Hierarchical Discriminant Regression) algorithm. UAV learns to build the mapping relation offline and online alternatively. The retrieval process from the mapping base has higher efficiency for its less mathematics complexity. Simulation results show that the approach we proposed in this paper has better performance than traditional neural network.