SEAD场景异构无人机配置与任务规划联合优化方法
2024,46(1):32-41
王建峰
国防科技大学 空天科学学院, 湖南 长沙 410073，jianfeng129@foxmail.com
贾高伟
国防科技大学 空天科学学院, 湖南 长沙 410073
辛宏博
国防科技大学 空天科学学院, 湖南 长沙 410073
郭正
国防科技大学 空天科学学院, 湖南 长沙 410073
侯中喜
国防科技大学 空天科学学院, 湖南 长沙 410073
国防科技大学 空天科学学院, 湖南 长沙 410073，jianfeng129@foxmail.com
贾高伟
国防科技大学 空天科学学院, 湖南 长沙 410073
辛宏博
国防科技大学 空天科学学院, 湖南 长沙 410073
郭正
国防科技大学 空天科学学院, 湖南 长沙 410073
侯中喜
国防科技大学 空天科学学院, 湖南 长沙 410073
摘要：
对敌防空压制(suppression of enemy air defenses, SEAD)场景是多无人机协同的典型应用,针对该场景特点,在任务规划问题基础上将各类型无人机数量也作为决策变量,充分表征目标、任务和无人机的多种约束,建立异构无人机编队路径问题模型。设计了双层联合优化方法求解该模型:上层设计了任务衔接参数指标,精确评估各类型无人机需求,指导无人机配置调整；下层设计了改进遗传算法,高效处理多类型约束并能结合无人机数量变化对任务方案进行精细调整；双层相互协调获得满足需求的无人机配置和执行方案。仿真结果表明,该方法可以在避免遍历无人机配置组合的前提下获得合理的无人机配置方案和高效可行的执行方案。
对敌防空压制(suppression of enemy air defenses, SEAD)场景是多无人机协同的典型应用,针对该场景特点,在任务规划问题基础上将各类型无人机数量也作为决策变量,充分表征目标、任务和无人机的多种约束,建立异构无人机编队路径问题模型。设计了双层联合优化方法求解该模型:上层设计了任务衔接参数指标,精确评估各类型无人机需求,指导无人机配置调整；下层设计了改进遗传算法,高效处理多类型约束并能结合无人机数量变化对任务方案进行精细调整；双层相互协调获得满足需求的无人机配置和执行方案。仿真结果表明,该方法可以在避免遍历无人机配置组合的前提下获得合理的无人机配置方案和高效可行的执行方案。
基金项目：
国家自然科学基金资助项目(61801495)
国家自然科学基金资助项目(61801495)
Joint optimization for heterogeneous multi-UAV configuration and mission planning within SEAD scenario
WANG Jianfeng
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China,jianfeng129@foxmail.com
JIA Gaowei
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
XIN Hongbo
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
GUO Zheng
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
HOU Zhongxi
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China,jianfeng129@foxmail.com
JIA Gaowei
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
XIN Hongbo
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
GUO Zheng
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
HOU Zhongxi
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
Abstract：
SEAD(suppression of enemy air defenses) is a typical application scenario of multi-UAV cooperation. Based on the characteristics of this scenario, the number of different types of UAV was also used as a decision variable in the task planning problem, fully characterizing the various constraints of the target, mission, and UAV, and establishing a heterogeneous UAV formation path problem model. A two-layer joint optimization method was designed to solve the model:the upper layer was designed with the task connection impact indicator to accurately assess the quantitative requirements of various types of UAVs and guide UAVs configuration adjustments; the lower layer improved the genetic algorithm, which can efficiently handle multiple coupling constraints and can accurately adjust the mission plan in conjunction with UAV quantity changes. The two layers coordinate with each other to obtain a UAV configuration and mission execution plan that meet the requirements. Simulation results show that the method can obtain a reasonable UAV configuration plan without traversing various UAV configurations, while obtaining an efficient and feasible mission execution plan.
SEAD(suppression of enemy air defenses) is a typical application scenario of multi-UAV cooperation. Based on the characteristics of this scenario, the number of different types of UAV was also used as a decision variable in the task planning problem, fully characterizing the various constraints of the target, mission, and UAV, and establishing a heterogeneous UAV formation path problem model. A two-layer joint optimization method was designed to solve the model:the upper layer was designed with the task connection impact indicator to accurately assess the quantitative requirements of various types of UAVs and guide UAVs configuration adjustments; the lower layer improved the genetic algorithm, which can efficiently handle multiple coupling constraints and can accurately adjust the mission plan in conjunction with UAV quantity changes. The two layers coordinate with each other to obtain a UAV configuration and mission execution plan that meet the requirements. Simulation results show that the method can obtain a reasonable UAV configuration plan without traversing various UAV configurations, while obtaining an efficient and feasible mission execution plan.
收稿日期：
2022-03-27
2022-03-27
