针对无人战斗机(unmanned combat air vehicle，UCAV)处于存在威胁区域的战场中路径规划问题，提出一种基于分组教与学算法的UCAV自适应路径规划方法。通过分析UCAV路径评价指标，提出一种自适应的UCAV路径评价模型，根据作战环境规划出距离短、威胁小的任务路径。针对教与学算法寻优精度低、耗时长的问题，提出一种分组教与学算法，引入动态分组和高斯分布扰动策略，提高算法寻优性能。通过仿真实验，该方案求解的最优路径更短且安全。
Adaptive Path Planning of UCAV with Modified Teaching-learning-based Optimization
Aiming at the path planning problem of Unmanned Combat Air Vehicle(UCAV)in the battlefield where UCAV is located in a threat area,a UCAV adaptive path planning method based on the algorithm of modified teaching-learning-based optimization is proposed.By analyzing the evaluation index of UCAV path,an adaptive UCAV path evaluation model is proposed,and the mission path with short distance and small threat is planned according to the combat environment.Then,aiming at the problems of low precision and long time consuming in the optimization of teaching and learning algorithm,the algorithm of modified teaching-learning-based optimization is proposed,and dynamic grouping and Gaussian distribution perturbation strategy are introduced to improve the optimization performance of the algorithm.The simulation results show that the optimal path is shorter and safer.
unmanned combat air vehicle(UCAV)path planningmodified teaching-learning-based optimizationswarm intelligence
国家科技期刊平台
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