【摘要】 为解决常用航迹规划算法在复杂环境下飞行时收敛速度慢、容易陷入局部最优的问题,论文提出了一种基于模拟退火的复合粒子群优化算法。通过对复杂环境进行建模,生成融合了地形、地物及威胁信息的等效数字地形图,并在该地图上使用该改进算法进行航迹规划。实验结果表明,基于模拟退火的融合粒子群优化算法具有较好的可行性和实时性,能够有效解决复杂环境下无人飞行器航迹规划的空间复杂度、搜索效率等,加快全局收敛速度,保持标准粒子群算法较强的鲁棒性,并能够实时避开障碍和规划出最优或次优的路径。更多还原

【Abstract】 In order to solve the commonly-used route-planning algorithm’s problem of slow convergence and easy to fall into local optimum in a complex flight environment,a composite particle swarm optimization algorithm is presented based on simulated annealing.By modeling complex environment,equivalent digital topographic map combining topography,surface features and threat information is generated.Then route planning on the map can be done with the improved algorithm.Experimental results show that,the optimization particle swarm fusion algorithm based on simulated annealing has better feasibility and real-time,can effectively solve the problem of spatial complexity and the search efficiency in complex environment of UAV route planning,and accelerate global convergence speed,keeping PSO robust,and avoid obstacles in real time and plan an optimal or suboptimal path.更多还原