Fuzzy Decision Tree Cloning of Flight Trajectory Optimisation for Rapid Path Planning
Oliver Turnbull, Arthur Richards, Jonathan Lawry, Mark Lowenberg · 2006
A decision tree algorithm is used to infer a set of linguistic decision rules from a set of two-dimensional obstacle avoidance trajectories optimised using mixed integer linear programming (MILP). A method to predict a discontinuous function with fuzzy decision trees is proposed and shown to make a good approximation to the optimisation behaviour with significantly reduced computational expense. Decision trees are shown to generalise to new scenarios of greater complexity than those represented in the training data and to make decisions on a time scale that would enable implementation in a real-time system. It is also demonstrated that the transparency of the rule based approach is useful in understanding the behaviour exhibited by the controller. Therefore, the decision trees are shown to have the potential to be effective online controllers for obstacle avoidance when trained on data generated by a suitable optimisation technique such as MILP