Mission adaptable autonomous vehicles

The authors discuss lessons learned on a neural autonomous simulator project that can be applied to autonomous underwater vehicles (AUVs). They developed a neural network (NN)-based unmanned air vehicle (UAV) navigation demonstration. The UAV simulation shows friendly flight corridors, enemy air-defense sites and the UAV mission targets. The UAV navigates in this hostile environment and reacts to unexpected threats. The study concentrated on the feasibility for noncomputer experts to prepare the UAVs for the specialized missions dictated by mission requirements and the battle situation, such as SAM sites and goal locations, corridors or way points. It was shown that NNs are successful in operating UAVs, and that the mission success rate is improved over fixed way point to way point flying. The simulation shows the potential for enhancing AUV survivability in hostile environments.<>