Research On Multi-UAV Target Strike Mission Planning Based On Swarm Intelligence Algorith

As the key technology of multi-UAV cooperation,mission planning technology is mainly divided into two parts: target assignment and path planning.Because the target is a threat to the UAV,it may cause damage during the strike process,so it is necessary to reduce the mission cost when planning the precise mission.However,the traditional planning algorithm is difficult to converge when the number of targets expands,and it is easy to cause uneven distribution in the target allocation stage.When planning the path,it will lead to track redundancy,the quality of the planning scheme is not high,and the probability of damage is large.The swarm intelligence algorithm can obtain high-quality approximate optimal solutions for optimization problems.Therefore,aiming at the problem of target strike,a phased task planning strategy based on swarm intelligence algorithm is designed to complete the research of multi-UAV target strike task planning.The main contents include the following three points:Firstly,it is to study the multi-UAV target assignment algorithm based on improved genetic algorithm,design the mathematical model of target assignment,and use genetic algorithm to solve the objective function.Aiming at the problem that the traditional genetic algorithm depends on the initial population,the concept of sub-chromosome is added in the chromosome coding stage to avoid premature convergence and improve the convergence accuracy.Aiming at the instability of the mutation genetic operator,the cyclic mutation method is used to make the chromosome coding sequence cover all the targets in the task scenario.Finally,the quality of the target allocation scheme is verified by simulation.Secondly,it is to study the multi-UAV trajectory planning algorithm based on improved grey wolf algorithm.Based on the single-machine path planning model,the collaborative cost is added to establish a multi-machine path planning model,and the path is planned by the gray wolf algorithm.Aiming at the problem that the traditional grey wolf algorithm is easy to fall into local optimum,the catastrophe mechanism is used to increase the diversity and search range of the population.Combined with the characteristics of fast convergence speed and high precision of Powell algorithm,the performance of the algorithm is improved.The nonlinear adjustment strategy of convergence factor and the position update formula based on fitness value are designed.Finally,the improved grey wolf algorithm is used to realize low cost cooperative path planning.Finally,it is to use multiple UAVs to build a physical experiment platform to verify the effectiveness of the swarm intelligence algorithm strategy on the cooperative target attack task.The test data analysis shows its practicability for target allocation and path planning.