Research on the Application of Hybrid Particle Swarm Algorithm in Multi-UAV Mission Planning with Capacity Constraints

With the rapid development of UAV technology in various industries, it is particularly urgent to solve the complex multi-UAV multi-target problem. The application scenarios of this study are the UAV has the maximum load limit, the maximum range limit, a single UAV can not complete the task, and the materials required for each target point are inconsistent. To solve this problem, a hybrid particle swarm optimization algorithm (HPSO) is proposed to allocate target points. After the allocation is completed, the ant colony algorithm is used to solve the path planning of a single UAV, so as to calculate the target points and flight routes assigned to each UAV. The hybrid particle swarm optimization (HPSO) takes the maximum payload and the range of the UAV as the constraint conditions, and the shortest path as the objective function. In order to verify the effectiveness of this algorithm, the traditional mathematical algorithm integer programming is used to allocate target points. After the allocation is completed, the ant colony algorithm is also used to plan the path of a single UAV. Finally, carry out computational simulation, and three times of calculation and simulation were carried out on the hybrid particle swarm algorithm. The simulation results show that the hybrid particle swarm optimization (HPSO) has a significant advantage in the capacity limited target assignment problem. And it provides an effective solution to the problem of multi-UAV mission planning with capacity constraints, thereby improving the efficiency of UAV mission execution, reducing the cost of mission execution, and providing theoretical support and technical guarantee for the further application of UAV technology.