Research On Multi-UAV Collaborative Mission Decision-Making And Planning Method Based On Ant Colony Algorithm In Unknown Environment

A swarm of drones working together in coordinated combat can significantly improve mission efficiency,reduce combat risks and losses,and has important applications in the military field,which has attracted extensive attention from researchers.However,in the case of unknown environments for drone swarm cooperative mission planning,traditional decision-making and planning methods often fail to meet actual needs due to the influence of environmental changes and information gaps.Therefore,this paper proposes a drone swarm reconnaissance,surveillance,and strike integration scheme for the problem of multi-drone cooperative search coverage,surveillance,and target strike task assignment in unknown environments.Specifically,the main research results include:1.A drone swarm situational awareness system architecture is designed for the problem of multi-drone search coverage in unknown battlefield environments.The architecture mainly includes a distributed communication sensing network module and a regional situational awareness module.The distributed communication sensing network module provides basic functional support for the drone swarm,and the regional situational awareness module provides real-time decision instructions.The cooperative search coverage method of the regional situational awareness module is emphasized,and the target function modeling of the search coverage problem is carried out using the grid map method.Through the designed multi-drone cooperative search coverage algorithm,the entire search area is covered,and the approximate distribution information of the target is obtained,reducing the unit target search time.2.An efficient multi-drone cooperative surveillance decision algorithm is designed for the problem of multi-drone surveillance of multiple key areas.First,a target probability map is constructed based on the obtained target distribution information,and the evaluation function of multi-drone cooperative surveillance is modeled.Then,based on the constructed target probability map,an indirect communication mechanism of information pheromones is used to design the multi-drone cooperative surveillance decision algorithm,realizing the coverage and surveillance of key areas.Simulation results show that the proposed algorithm is effective and stable,and can ensure that the distance between drones is always maintained outside the collision warning distance,with high safety.Compared with traditional information pheromone decision algorithms based on flight probability and artificial potential fields,the proposed algorithm reduces the evaluation function more smoothly and with smaller oscillation amplitudes.3.An improved ant colony algorithm is designed for the problem of multi-drone cooperative target strike task assignment.First,based on the specific information of the targets to be attacked obtained through real-time surveillance,the mathematical target function is modeled considering factors such as flight cost,task execution time,and total strike benefits.Then,combining genetic algorithm with the existing ant colony algorithm,the ant colony population is selected,crossed,and mutated in the evolutionary process to solve the target function more efficiently.Finally,simulation results show that the improved ant colony algorithm can reasonably allocate strike tasks according to the actual ammunition carried by each drone in the cluster,and it is effective and reasonable.Compared with traditional genetic algorithms and ant colony algorithms,the proposed algorithm can obtain better target function values and faster convergence speed,demonstrating its superiority.