This paper discusses the development of a multiobjective#R##N#mission flight planning algorithm for Unmanned#R##N#Aerial System (UAS) operations within the National Airspace#R##N#System (NAS). Existing methods for multi-objective planning#R##N#are largely confined to two dimensional searches and/or acyclic#R##N#graphs in deterministic environments; many are#R##N#computationally infeasible for large state spaces. In this paper,#R##N#a multi-objective fuzzy logic decision maker is used to augment#R##N#the D* Lite graph search algorithm in finding a near optimal#R##N#path. This not only enables evaluation and trade-off between#R##N#multiple objectives when choosing a path in three dimensional#R##N#space, but also allows for the modelling of data uncertainty. A#R##N#case study scenario is developed to illustrate the performance of#R##N#a number of different algorithms. It is shown that a fuzzy multiobjective#R##N#mission flight planner provides a viable method for#R##N#embedding human expert knowledge in a computationally#R##N#feasible algorithm.
['Paul P. Wu', 'Reece A. Clothier', 'Duncan A. Campbell', 'Rodney A. Walker']
Fuzzy Multi-objective Mission Flight Planning in Unmanned Aerial Systems