面向林业资源防护的CGPSO算法UAV航迹优化应用研究

Application forestry resource protection oriented CGPSO algorithm UAV trajectory optimization application research

针对传统PSO无人机航迹规划算法在林业资源防护任务中存在收敛速度慢、易陷入局部最优的问题,提出了一种基于CGPSO的无人机航迹优化算法(cauchy gauss particle swarm optimization, CGPSO)。借助雷达传感器对林间环境进行预检,构建了无人机飞行任务环境模型;引入了自适应惯性权重和融合柯西-高斯变异算子调整粒子群算法,平衡全局-局部收敛速度,优化局部极值问题;综合分析了无人机航迹长度代价、障碍物碰撞代价和高程范围代价,建立了航迹规划适应度函数。仿真结果显示,所规划算法适应度标准差达到了0.148 6,用时54.34 s,相比PSO算法,收敛代价值减少了42%,用时提升了25%,与所有算法相比,整体航迹具有较强的鲁棒性,对环境的适应性更优。因此,采用新规划航迹算法在林区进行林业资源防护工作是可行的。

A Cauchy Gauss Particle Swarm Optimization(CGPSO)algorithm based on CGPSO is proposed to address the slow convergence and the tendency to fall into local optimum in traditional PSO UAV trajectory planning algorithms for forestry resource protection tasks.The UAV mission environment model is constructed by pre-screening the forest environment with radar sensors;adaptive inertia weights and fused Cauchy-Gauss variational operators are introduced to adjust the particle swarm algorithm to balance the global-local convergence speed and optimize the local extreme problem;the UAV track length cost,obstacle collision cost and elevation range cost are comprehensively analyzed and a track planning fitness function is built.Simulation results show the standard deviation of the planned algorithm’s fitness reaches 0.1486 and the time taken is 54.34 s,which is 42%less than the convergence generation value of the PSO algorithm and 25%better than the time taken.It is feasible to use the new planning trajectory algorithm for forestry resource protection in forest areas.