Research On Conflict Resolution Method Of UAV Based On Improved Ant Colony Algorithm

In recent years,with the rapid development of UAV technology,UAV has been widely used in both military and civil fields.The large increase in the number of unmanned aerial vehicles(UAVs)leads to the increasing difficulty in the allocation of flight airspace and the increasing probability of UAVs’ flight conflicts.Therefore,the safety issue in UAVs’ flight missions has been paid more attention by people.UAV conflict resolution technology is the key to air traffic collision prevention,which is also of great significance to reduce flight cost and improve airspace utilization.The core part of conflict resolution is the conflict resolution algorithm.Based on the conflict resolution algorithm,this paper studies the conflict resolution of UAV.Specific research content and work are as follows:1.The conflict resolution of UAVs is summarized,and the adjustment strategies to avoid collision during the flight of UAVs are summarized.The constraint conditions of UAVs in flight are summarized,and the advantages and disadvantages of the current mainstream relief algorithms are compared.Finally,the fusion algorithm is proposed to solve the conflict resolution problem of UAVs.2.Aiming at the shortcoming that ant colony algorithm is easy to fall into local optimum,this paper proposes an improved ant colony algorithm,which improves the convergence speed of the algorithm by adjusting the pheromone evaporation coefficient in sections.The improved algorithm is combined with UAV conflict resolution strategy to solve UAV conflict resolution problem.The simulation results show that compared with the basic ant colony algorithm,the improved ant colony algorithm has a faster solving speed,and the planned relief path is more reasonable,which can better solve the conflict resolution problem of two-dimensional UAV.3.In order to enhance the ability of the improved ant colony algorithm to solve the conflict resolution problem The artificial potential field method was used to establish the initial search path for the ant colony algorithm.The course Angle adjustment strategy of UAV is used to optimize the escape path of UAV.The simulation results show that the hybrid algorithm can effectively solve the conflict resolution problem of two-dimensional UAV,and compared with the improved ant colony algorithm,the solving speed and the rationality of the extricated flight path are significantly improved.4.In order to make the artificial potential field-improved ant colony hybrid algorithm be able to solve the conflict resolution problem of unmanned aerial vehicles in threedimensional space,the conflict resolution model of unmanned aerial vehicles in threedimensional space is established,and the transition probability formula of ant colony algorithm is improved to improve the algorithm performance.The course Angle adjustment strategy of UAV is adjusted,and the combination of course Angle adjustment strategy of UAV and flight altitude adjustment strategy can effectively improve the smoothness of track relief and the flexibility of UAV flight.The simulation results show that the hybrid algorithm has better performance than the improved ant colony algorithm in solving the threedimensional conflict resolution problem.Through data comparison,it is shown that the ability of artificial potential field-improved ant colony algorithm to solve three-dimensional conflict problems continues to be superior to the other two algorithms in solving twodimensional conflict problems.