Agent-based simulation environment for UCAV mission planning and execution

Abstract

The projected use of Uninhabited Combat Air Vehicles (UCAVs) in combat missions raises the challenge of providing the intelligent functionality that will allow them to carry out their tasks in autonomous mode. This challenge is composed by the fact that UCAVs will have to act in a dynamic environment, which will constantly require changes in the mission plans. In this paper, we present an approach to the autonomy of planbased mission execution in the context of multi-UCAV missions. In particular, we concentrate on two essential aspects of mission execution: the assessment of the mission environment, and the adjustment of UCAV plans to the variations in the mission environment. The situation assessment and plan modification are discussed within the framework of an agent-based simulation environment, which allows the high-fidelity replication of the actual mission environment and allows users to experiment with UCAV capabilities in various types of combat situations.

Weapon configuration, allocation and route planning with time windows for multiple unmanned combat air vehicles

“…Constraints (4) -( 6) state that each UCAV leaves the air base, after arriving at a target the UCAV leaves again, and it will finally return to the air base. Constraint (7) guarantees that the UCAV carrying capacity will not be exceeded. Constraint (8) assures that the number of hanging points of each UCAV will not be exceeded.…”

Joint mission and route planning of unmanned air vehicles via a learning-based heuristic

“…Unmanned air vehicles (UAVs) have many advantages compared to manned air vehicles, such as without loss of human life, lower cost and higher stealth capability [1], and have been widely employed to conduct many kinds of military missions. When UAVs are utilized to attack multiple targets, many decisions have to be optimized coordinately in the mission planning process, including what weapons to be carried by the UAV, which and how many weapons to be used to attack the target, and how the routes of UAVs should be designed.…”

Modeling Team Mental State in Cooperative Air Combat

“…The representative research results of the mental state are the intention theory [1], and the BDI model [2], and joint intention [3] etc., and these results are applied in modeling the air combat command control system [4][5][6][7][8] on the trial basis. These researches have given some methods to describe the mental states of the single agent.…”