A Comparative Simulation Study of Multi-aircraft Cooperative Task Planning Based on Artificial Intelligence Optimization Algorithm

Considering the inherent characteristics of the return module in manned space missions, this paper proposes a multi-aircraft collaborative target search strategy and establishes a multi-aircraft collaborative target search model. Based on the analysis of collaborative search solutions, this paper proposes a multi-aircraft collaborative search task solution based on an artificial intelligence optimization algorithm. However, the general artificial intelligence optimization algorithm suffers from slow convergence rate and excessive resource consumption. In order to effectively address this problem, this paper proposes GA-Dstar hybrid UAV trajectory planning algorithm to reduce the computation time by using the Dstar algorithm for planning the local path for searching within the local search range. The simulation results show that the proposed hybrid algorithm is more effective than the traditional GA algorithm for performing a global search. The proposed algorithm significantly reduces resource consumption while maintaining the optimality of the trajectory planning route. The improved algorithm proposed in this work is useful for multi-aircraft collaborative mission planning and related research.