Maneuver Strategy Generation of UCAV for within Visual Range Air Combat Based on Multi-Agent Reinforcement Learning and Target Position Prediction

With the development of unmanned combat air vehicles (UCAVs) and artificial intelligence (AI), within visual range (WVR) air combat confrontations utilizing intelligent UCAVs are expected to be widely used in future air combats. As controlling highly dynamic and uncertain WVR air combats from the ground stations of the UCAV is not feasible, it is necessary to develop an algorithm that can generate highly intelligent air combat strategies in order to enable UCAV to independently complete air combat missions. In this paper, a 1-vs.-1 WVR air combat strategy generation algorithm is proposed using the multi-agent deep deterministic policy gradient (MADDPG). A 1-vs.-1 WVR air combat is modeled as a two-player zero-sum Markov game (ZSMG). A method for predicting the position of the target is introduced into the model in order to enable the UCAV to predict the target’s actions and position. Moreover, to ensure that the UCAV is not limited by the constraints of the basic fighter maneuver (BFM) library, the action space is considered to be a continuous one. At the same time, a potential-based reward shaping method is proposed in order to improve the efficiency of the air combat strategy generation algorithm. Finally, the efficiency of the air combat strategy generation algorithm and the intelligence level of the resulting strategy is verified through simulation experiments. The results show that an air combat strategy using target position prediction is superior to the one that does not use target position prediction.

Global Needs for Jet-Engine-Steered (JES) Strike Drones vs. Lack of Updated Textbooks to Design 6th Generation UCLASS Due to UCAV Failure

Global fight against terror, and the rapidly changing geopolitical situations, raise hot debates on urgently needed new designs and operational modes of efficient, low-cost, Jet-Engine-Steered (JES) strike drones, and about the expected roles of future UCLASS (Unmanned, Carrier-Launched, Surveillance & Strike), while current UCAV (Unmanned Combat Air Vehicles) have failed to perform the needed strike missions. The role of the jet-engine community in this rapidly unfolding geopolitical situation is reviewed in the main text and in the Appendix.

Multi-Vehicle Missions

This chapter deals with the issues associated with the autonomy of vehicle fleets, as well as some of the dimensions provided by an Artificial Intelligence (AI) solution. This presentation is developed using the example of a suppression of enemy air defense mission carried out by a group of Unmanned Combat Air Vehicles (UCAV). The environment of the Mission Management System (MMS) includes the theatre of operations, vehicle sub-systems and the MMS of other UCAV. An MMS architecture, organized around a database, including reactive and deliberative layers is described in detail. The deliberative layer includes a distributed mission planner developed using constraint programming and an agent framework. Experimental results demonstrate that the MMS is able, in a bounded time, to carry out missions, to activate the contingent behaviors, to decide whether to plan or not. Some research directions remain open in this application domain of AI.

High resolution computational unsteady aerodynamic techniques applied to manoeuvring UCAVs

A method of high resolution simulation is proposed for Unmanned Combat Air Vehicles (UCAVs) undergoing manoeuvress at high angle-of-attack or transonic speeds. Motivation for the need to develop such a method is first presented to show payoff in the design cycle, followed by results of using the method on current manned fighter aircraft. Finally, a notional UCAV shape from Boeing military aircraft is presented to show the possibilities of how the method can accurately capture the relevant phenomenon of these difficult flight regimes.