Autonomous Mission Planning Method For Unmanned Aerial Vehicle Swarm Operations

Unmanned Aerial Vehic le(UAV)swarm has many advantages such as strong combat capability,high system survival rate and low attack cost,which will certainly become the main form of UAV application in the battlefield in the future.The scientif ic and efficient mission planning method is the key to fulfill the superiority of swarm operations,and it is of great signif icance for improving the system operational effectiveness.In this paper,the autonomous mission planning methods are studied for the three typical operational tasks of swarm cooperative search,saturation attack and dynamic confrontation in complex combat environment.The main contents of this work are as follows:Firstly,for the autonomous mission planning problem of UAV swarm cooperative search,the UAV state space model,swarm dynamic topological interaction model,and cooperative search mission optimization model are established.Considering the online obstacle avoidance and collis ion prevention problems of UAVs in the search process,the artificial potential field is introduced to improve the state transition rule of the ant colony optimization.And then a hybrid artificial potential field with ant colony optimization(HAPF-ACO)algor ithm is proposed for realizing the cooperative search decision of UAVs.Simulation results indicate that the designed HAPF-ACO algor ithm achieves superior search coverage performance compared with traditional algor ithms,and it has the online obstacle avoidance and collision prevention capabilities,and better real-time performance.Therefore,it is more suitable for solving the large-scale UAV swarm cooperative search problem.Secondly,for the autonomous mission planning problem of the UAV swarm saturation attack,the full system countermeasures of enemy target is taken as background.By analyzing the penetration probability of the UAV to the enemy defence systems,a calculation method of the UAV saturation attack number is proposed.And on this basis,considering the time and space coordination requirements of saturation attack mission,a distributed surrounding controller based on consensus algor ithm is designed to realize that multiple UAVs reach the target at the same time and are evenly distributed on the target encirclement,executing the synchronous multi-point saturation attack for target.The effectiveness of the designed method is verified by simulations.Finally,for the autonomous mission planning problem of the UAV swarm dynamic confrontation,the real-time combat decision-making strategy of UAVs are studied under the conditions of time-variant swarm states,UAV numbers and communication topologies.For the target allocation decision problem,a distributed extended iterative consensus-based auction algorithm(ICBAA)is developed considering the real-time requirements of large-scale UAV swarm system.And an offense-defense preference is introduced to realize the adjustment of the swarm combat strategy.As for the swarm motion decision problem,the SAC-OD rule based swarm motion model is established by introducing the UAVs' offense and defense behaviors into the classical biological swarm's separation-alignment-cohesion(SAC)bebavior rules.The UAV swarm dynamic confrontation process and its influencing factors are simulated and analyzed,which provide guidances for the selection of actual combat strategies.