Abstract:

This paper studies the problem of generating robust optimal air-to-ground weapon delivery mission plan for unmanned combat aerial vehicles (UCAV). Aiming at the deficiency of existing approaches in processing some uncertainties, such as disturbing in battlefield, model imprecision and operating errors, a strategy based on a robust multi-objective optimization (RMO) approach is proposed. Firstly, some constraints which include the flight capability constraint, weapon constraint and battlefield constraint, are considered. Secondly, several robust optimal cost functions are defined by using the Monte Carlo method simulating criteria of weapon delivery, and then the weapon delivery planning (WDP) problem is transformed into a RMO problem. Thirdly, an improved fast non-dominated sorting genetic algorithm (NSGA-II) combining Monte Carlo simulation is proposed, and then a trajectory generating strategy based on basic trajectory segments (BTS) is presented to generate weapon delivery trajectories. Finally, a demonstrated air-to-ground weapon delivery planning considering uncertainties is proposed and solved, and the simulated results show that the proposed approach can deal with some uncertainties in WDP efficiently.