Research On Multi-UAV Mission Planning Method In Battlefield Environment

Unmanned aerial vehicle（UAV）has become an indispensable new weapon in modern military warfare.However,due to the constraints of a single UAV’s own performance and the number of weapons,it cannot meet the changing battlefield environment and complex mission requirements.Therefore,multi-UAV mission planning comes into being.Multi-UAV mission planning refers to determining the mission plan for the UAV to ensure that the UAV performs all tasks with the minimum mission cost.Using more efficient control algorithms and strategies achieves UAV mission planning,and improving the efficiency of mission planning has become the focus of research.This thesis mainly studies the task allocation and path planning in mission planning.Firstly,the improved particle swarm optimization algorithm based on variable neighborhood search algorithm is used to solve the muti-UAV task pre-assignment problem.The task allocation model is established,including task allocation constraints and allocation cost index.Particle coding is carried out according to the task model.Aiming at the defects of particle swarm algorithm,the inertia weight w,individual learning factor(8(8₁and social learning factor(8(8₂ are improved.When the particle has the possibility of falling into local optimum,the variable neighborhood search algorithm is used to jump out of local optimum.Through the algorithm simulation comparison,it proves that improved hybrid algorithm can avoid local optimality and obtain a better solution,and the stability of the algorithm is also significantly improved.Secondly,for the emergencies of the battlefield environment,the improved contract net method is used to solve the task redistribution problem.Aiming at the defects of the traditional contract net method such as fast communication frequency and delay,it is improved by making bidding drones participate in bidding,adopting two kinds of contracts such as buying and selling,exchange,introducing task load and concurrent transaction mechanism,etc.The simulation proves that the improved algorithm results in a reduced communication frequency between UAVs and a higher real-time performance.Finally,the improved ant colony algorithm based on beetle antennae search algorithm is used to solve UAV three-dimensional global path planning problem.Path planning model is established,including constraints and comprehensive path evaluation index.Environment terrain model and threat model are established.Aiming at the defects of ant colony algorithm,its heuristic function,state transition rule and pheromone volatilization coefficient are improved,and the path generated by the improved ant colony algorithm is further optimized by using beetle antennae search algorithm.And using cubic B-spline smoothes the path obtained by the path planning algorithm,so that the smoothed path can actually fly.In response to the emergence of sudden threats,the improved artificial potential field method is used to carry out local path planning,so that the UAV can avoid threats and perform tasks safely.The GUI mission planner system based on MATLAB is designed.Importing the mountainous battlefield environment information,the static and dynamic mission planning system is simulated.the mission is reassigned online when new tasks appear or UAVs are disconnected,and local path planning is performed to avoid them when new threats appear.Simulations prove the feasibility of the algorithm.