Heterogeneous Multiple Unmanned Air Vehicles Collaborative Mission Planning with Complex Constraints

Heterogeneous multiple Unmanned Air Vehicles cooperate to execute mission in the form of team, which can improve the efficiency of mission execution and better adapt to complex mission requirements. Mission planning is the key to heterogeneous Unmanned Air Vehicle team resource scheduling. In this paper, a hybrid discrete genetic algorithm is proposed for solving heterogeneous multiple Unmanned Air Vehicles collaborative mission planning problem with complex constraints. The feasible encoding method is proposed, which effectively expressed the problem model under multiple constraints. Simulated annealing mechanism was introduced to prevent the algorithm from falling into local optimum and improve the convergence accuracy of the algorithm. The initial temperature and fitness adjustment functions are designed. Simulations verify the effectiveness of the proposed algorithm to deal with complex constrained mission planning problems. Through the comparative analysis of the performance of the algorithm, the superiority of the algorithm performance is verified.