基于多目标优化算法的多无人机协同航迹规划

发布时间：2018-06-29 23:26

本文选题：多无人机 + 协同航迹规划　；参考：《系统工程与电子技术》2017年04期

【摘要】：多无人机协同航迹规划是无人机协同作战的关键技术之一。本文提出的一种针对多无人机协同航迹规划的多目标优化算法,即协同非支配排序进化算法(cooperated non-dominated sorting genetic algorithms-II,CO-NSGA-II),针对多架无人机的航迹距离、安全性、时间以及空间的协同性进行规划。运用多目标优化算法,克服了传统航迹规划中需要为各目标函数取权值的不足,并且可以生成多组可供选择的解。同时引入协同进化策略,将各无人机的航迹规划视作子种群,各子种群间进行合作,子种群内采用非支配排序进化算法(non-dominated sorting genetic algorithms-II,NSGA-II)进行独立优化。考虑到各机间的协同约束,用时间空间协同系数替代传统算法中的"拥挤距离"参数。仿真结果表明通过本文算法能够有效实现多无人机协同航迹规划。
[Abstract]:Multi-UAV cooperative track planning is one of the key technologies of UAV cooperative operation. In this paper, a multi-objective optimization algorithm for cooperative track planning of multi-UAV is proposed, that is, the cooperated non-dominated sorting genetic algorithm based on non-dominated ordering evolution algorithm (cooperated non-dominated sorting genetic algorithm), which can be used to plan the track distance, security, time and space of multiple UAVs in accordance with the coordination of track distance, security, time and space. The multi-objective optimization algorithm is used to overcome the shortcomings of traditional route planning which need to take weights for each objective function, and can generate multiple sets of alternative solutions. At the same time, the co-evolution strategy is introduced, the track planning of UAV is regarded as a sub-population, and the sub-populations cooperate, and the independent optimization is carried out by using the non-dominated sorting genetic algorithm s-II NSGA-II in the sub-population. Considering the coordination constraints between machines, the time and space coordination coefficients are used to replace the "congestion distance" parameters in the traditional algorithms. Simulation results show that the proposed algorithm can effectively implement cooperative track planning for multiple UAVs.
【作者单位】：西北工业大学电子信息学院;
【基金】：国家自然科学基金(61401363) 航空科学基金(20155153034)资助课题
【分类号】：TP18;V279

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