面向目标定位精度的主从式无人机编队航迹规划方法

Route Planning Method of Master-Slave UAV Formation for Target Positioning Accuracy

在定位侦察任务中无人机编队协同的工作模式由于具有良好的定位效果和较强的鲁棒性,更加符合未来电子侦察的需求.本文在考虑定位精度的前提下,提出了一种基于主从式编队控制方案的无人机编队航迹规划方法.针对主机航迹规划,以稀疏A*算法为基础,将自适应步长与粒子群算法节点选取相结合提出混合A*算法,并针对障碍物群环境提出避障策略.针对从机航迹规划,提出一种改进的多目标量子粒子群(Improved Multi-objective Quantum-behaved Particle Swarm Optimization,IMQPSO)算法,将粒子混合更新策略、非劣解的优势选取策略和无人机Y型布站方案引入算法.经验证,改进后的算法综合适应度值相较于传统的多目标粒子群(Multi-objective Particle Swarm Optimization,MPSO)算法和多目标量子粒子群(Multi-objective Quantum-behaved Particle Swarm Optimization,MQPSO)算法在算法运行时间基本持平的情况下分别减小了4.7%和1.4%.

In the positioning reconnaissance mission,the cooperative working mode of UAV formation is more in line with the needs of future electronic reconnaissance because of its good positioning effect and strong robustness.Considering the positioning accuracy,a UAV formation route planning method based on master-slave formation control scheme is proposed in this paper.For host route planning,combining adaptive step size with particle swarm optimization node selection,a hybrid A* algorithm is proposed based on sparse A* algorithm,and an obstacle avoidance strategy is proposed for obstacle swarm environment.For slave route planning,an improved multi-objective quantum-behaved particle swarm optimization(IMQPSO)algorithm is proposed,which introduces the particle hybrid update strategy,the advantage selection strategy of non inferior solution and the UAV Y-type station layout scheme into the algorithm.Compared with the traditional multi-objective particle swarm optimization(MPSO)algorithm and multi-objective quantum-behaved particle swarm optimization(MQPSO)algorithm,the comprehensive fitness value of the improved algorithm is reduced by 4.7% and 1.4% respectively when the running time of the algorithm is basically the same.