采用粒子群优化算法的无人机实时航迹规划

Real-time track-planning of UAVs based on PSO algorithm

战场环境是动态变化的,很难预先获得全局精确的威胁信息,因此需要无人机具备一定的实时航迹规划能力。采用连续型粒子群优化(PSO)算法进行无人机参考航迹的实时规划,以最大转弯半径、步进、最短距离和回避威胁作为适应度函数的评价指标,得到代表最优航路的离散点。对算法进行了相应的仿真,结果表明该方法费时短,占用内存少,可以满足在线实时航迹规划的要求。

It is difficult to gain accurate information about the threat on the whole because of the huge battle area and dynamically changed environment for UAVs. It is necessary for UAVs to have the capability of trackplanning in real-time. Continuous Particle Swarm Optimization （PSO） algorithm is used to plan reference track for UAVs in real-time. This method uses maximal turning radius, step, shortest distance and threat-evading as evaluation indexes of the fitness function. A series of discrete points are obtained, which can express the optimum reference track. The result of the simulation shows that the method is effective and needs less memory space, so it can meet with the request of track-planning in real-time.