航迹规划策略学习方法研究

分析并研究航迹规划软件中的飞行器操作数据特征,提出一种基于XGBoost算法和K-prototypes算法的航迹规划策略学习方法。在样本采集与分类过程中,根据约束自身特性和规划人员操作特征,将约束分为飞行器环境约束和飞行器特性相关约束,分别采用XGBoost算法和K-prototypes算法进行策略学习,并对飞行器特性相关约束做进一步细分,实现复杂约束的针对性学习及样本分类管理。当航迹不满足约束时,需将已获得的规划策略反馈给规划人员使其得到策略引导。实验结果表明,该方法能准确选取航迹规划策略并给出策略引导信息,降低规划人员的工作强度,提升交互规划效率和规划软件的智能性。

Research on Learning Method for Flight Route Planning Strategy

This paper analyzes and studies the characteristics of aircraft operational data in flight rout planning software,proposes a learning method for route planning strategy based on the XGBoost algorithm and K-prototypes algorithm.During sample collection and classification,the features of constraints and operation of planners are analyzed,and constraints are accordingly divided into two categories:constraints of aircraft environment and constraints related to aircraft features.Relevant strategies are learnt by using the XGBoost algorithm and K-prototypes algorithm respectively.Constraints related to aircraft features are further subdivided for more specific learning of complex constraints and classified management of samples.If a flight route does not meet the constraints,the obtained planning strategies are returned to planners to provide strategic guidance.Experimental results show that the proposed method can effectively extract the flight route planning strategies and provide strategic guidance information,which reduces the workloads of planners,improving the efficiency of interactive planning and the intelligence of planning software.