基于双种群优化算法的舰载无人机任务规划

Multi-shipborne UAV Cooperative Mission Planning Basedon Dual Population Optimization Algorithm

多舰载无人机协同对海上目标攻击的任务规划是有效提高无人机海上作战能力的关键技术之一。不同于陆基起降平台,舰载无人机的起降平台为复杂海洋环境下的移动平台。针对这一特点,考虑了舰载无人机起降场为存在时间窗口约束的移动起降场、完成任务所需空中时间以及生存威胁等各方面因素,以攻击收益最高、无人机损毁最小为目标建立了多目标任务规划模型,并给出了双种群优化算法对其进行求解。通过与经典的NSGA-2、SPEA2等多目标优化算法进行对比,仿真结果表明该方法是可行、有效的。

Mission planning of multiple shipborne UAVs for coordinated attacks on maritime targets is one of the key technologies to effectively improve the maritime combat capability of UAVs.Different from land-based UAVs,the take-off and landing platform of shipborne UAVs is a mobile platform at sea,and there are more options for take-off and landing points,which increases the complexity of the solution.Furthermore,shipborne UAVs work in a complex marine environment.During the navigation of the ship at sea,due to the different marine environments of the sea,excessive waves will affect the state of the ship and pose a safety threat to the departure and landing of the UAV.Therefore,the study of shipboard UAV mission planning is of important military significance.Based on the principle of multi-objective optimization,this paper focuses on the UAV mission planning modeling and solution problem with the background of a shipborne UAV coordinated attack mission against targets at sea.In mission planning modeling,a multiple takeoff and landing point is considered for the feature that the takeoff and landing platform of the shipborne UAV is a mobile platform.The time window constraint is added to each takeoff and landing point for the feature of the complex marine working environment of the shipborne UAV.In addition,the shipborne UAV mission planning model is established with the goal of maximum attack revenue and minimum UAV damage by combining various factors such as the air time required for the UAV to complete the mission and the survival threat.In the aspect of the solution,due to the strong constraints of this model,the feasible solutions are likely to evolve into infeasible solutions in the process of solving the traditional algorithm,which easily makes the algorithm fall into local optimum and makes it difficult to find the solution set of Pareto front.This paper adopts a dual population optimization algorithm,in which feasible and infeasible solutions are not directly compared while evolving in parallel,so that the algorithm avoids falling into local optimum,the reasonableness of the algorithm is verified by giving different hypothetical parameters,and the simulation results show the feasibility and effectiveness of the method by comparing it with the classical multi-objective optimization algorithms such as NSGA-2 and SPEA-2.