基于离散粒子群算法多旋翼无人机任务分配研究

应用离散粒子群算法解决消防救援队伍在现场使用多旋翼无人机时存在的多任务分配难题。通过分析现场任务分配条件,在评价指标设计上综合考虑续航、探测宽度、探测气体种类、任务时间以及任务时序约束等问题,并引入逆转算子对离散粒子群算法进行优化。仿真结果表明,优化的粒子群算法相比遗传算法具有更好的寻优性和收敛性。上述研究可以为灭火救援现场多旋翼无人机任务分配提供一定的算法支持。

Research on the multi-rotor UAV multi-task assignment based on discrete particle swarm optimization algorithm

Discrete particle swarm algorithm was used to solve the problem of multi-task assignment of UAV for the team of fire and rescue. Through analyzing the conditions of on site task assignment, the problems such as drone endurance, detection width, detection gas type, task timing and task sequence constraints, and so on were considered in the evaluation index design, and the reverse operator is introduced to optimize the algorithm of discrete particle swarm. The simulation results demonstrate that the optimized PSO has better performance and convergence than the genetic algorithm. The research above can provide some algorithm support for mission assignment of UAVs in fire and rescue site.