Heterogeneous multi-unmanned aerial vehicle cooperative task allocation method based on Monte Carlo tree search architecture under time sequence constraint

The invention provides a heterogeneous multi-unmanned aerial vehicle cooperative task allocation method based on a Monte Carlo tree search architecture by aiming at an enemy air defense firepower suppression task (SEAD) scene and considering resource constraints, kinematics constraints and time sequence priority constraints among tasks during task execution of an unmanned aerial vehicle cluster. According to the method, Monte Carlo tree search and heterogeneous multi-unmanned aerial vehicle cooperative task allocation are innovatively combined, a task allocation architecture based on Monte Carlo tree search is designed, and an optimal decision in a current state is returned through limited times of iteration; the internal task sequence of the unmanned aerial vehicle and the course angle when the unmanned aerial vehicle approaches the target are optimized by adopting a genetic algorithm, the track cost of the unmanned aerial vehicle is reduced, and the system efficiency is improved; and finally, aiming at the