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针对复杂地形和威胁环境下的无人机航路规划问题,对粒子群算法进行了改进,提出了融入威胁启发机制的改进粒子群算法。充分利用无人机在任务区域中已知的威胁信息,将其作为威胁启发项,构成粒子群速度更新公式的一部分,有效丰富粒子群算法的搜索行为,增强粒子在搜索过程中的针对性和指导性。使用最小威胁曲面方法,降低粒子编码的维数,并采用航路在线再规划的方法解决无人机飞行过程可能遇到的突发威胁。仿真试验表明,所提方法能够有效地规划出无人机的最优航路,提高规划过程的时效性,并且满足航路再规划的实时性要求。
In order to solve the problem of UAV route planning in complex terrain and threat environment, the particle swarm optimization algorithm is improved, and an improved particle swarm optimization algorithm based on threat heuristic is proposed. Make full use of the known threat information of the UAV in the mission area and use it as a threat enlightenment component to form part of the particle swarm speed update formula to effectively enrich the search behavior of the PSO and enhance the relevance of the particle in the search process and Guiding. Using the method of minimum threat surface, the dimensionality of particle coding is reduced, and the on-line route re-planning method is used to solve the unexpected threat that the UAV may encounter during its flight. The simulation results show that the proposed method can effectively plan the optimal route of UAV, improve the timeliness of the planning process, and meet the real-time requirements of route re-planning.