3D flight path planning based on Bayesian optimization algorithm

A Bayesian optimization algorithm for three-dimensional (3D) flight path planning problem was presented. The flight path was presented by a time sequence of velocity vectors whose elements are speed, heading and climb angle. Using a fixed time interval, this representation allows the planning algorithm to constrain the candidate solution to lie within the acceleration capability of the air vehicle. The Bayesian optimization algorithm is applied to implement explicit learning by building a Bayesian network of the joint distribution of viable candidate path genotype strings. The construct and conditional probabilities of the network indicate the qualitative and quantitative relationship among the path genotypes. A multivariate K2 metric is designed to evaluate the network. A new set of path genotype strings is generated by using the corresponding conditional probabilities. If the stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of viable path genotype strings. Experimental results demonstrate that this approach is effectively.