基于改进PSO-Lévy算法的海流环境下AUV节能路径规划

Energy-Saving Path Planning for AUVs in Current Environment Based on Improved PSO-Lévy Algorithm

为了获取海流环境中自主水下航行器(AUV)的节能避障路径,建立了包含海流场速度信息和水下地形障碍的三维动态海流环境模型;基于航行能耗、AUV机动性能约束和障碍物约束,建立增广目标函数,提出了一种基于权重调节机制和随机游走特性的改进粒子群优化-Lévy(PSO-Lévy)算法。将基于最佳阻尼比的参数调节策略和基于Lévy-flight过程的步长随机游走策略引入PSO算法,通过概率执行粒子大步长游走操作以及对粒子惯性速度进行调控,弥补了PSO步长短、跳出局部最优能力弱的劣势。仿真结果表明,所提出的算法能在有效避开障碍物的同时利用海流信息规划出低能耗的最优路径。

To obtain energy-saving obstacle avoidance paths of autonomous undersea vehicles(AUVs)in the current environment,a three-dimensional dynamic current environment model based on current field velocity information and underwater topographic obstacles was established.The augmented objective function was established based on navigation energy consumption,AUV maneuvering performance,and obstacle constraints,and an improved particle swarm optimization-Lévy(PSO-Lévy)algorithm based on weight adjustment mechanism and random wandering mechanism was proposed.The parameter adjustment strategy based on the optimal damping ratio and the random step wandering strategy based on the Lévy-flight process were introduced into the PSO algorithm.By executing a long-step wandering operation and regulating the velocity inertial of particles with a certain probability,the PSO algorithm could get a longer step and jump out of the local optimum.The simulation results show that the proposed algorithm can plan the optimal path with low energy consumption according to the current information while effectively avoiding obstacles.