基于粒子群优化的自治水下机器人模糊路径规划

Fuzzy path planning for autonomous underwater vehicles based on particle swarm optimization

针对自治水下机器人(AUV)的路径规划问题进行了研究,依据模糊控制规则,提出了一种基于粒子群优化(PSO)的模糊路径规划算法。首先建立水下水平面内路径规划的模糊规则,并应用A/B模型进行静态和动态障碍物的避障。同时考虑到模糊边界的选择具有很大的随意性,所生成的路径并非最优,利用PSO算法进行模糊集合的优化,使得最终生成的路径最优。应用设计的粒子群优化模糊(PSO-fuzzy)算法针对动静态障碍物进行了避障路径规划,仿真结果验证了所设计的方法的有效性。

The study was conducted with the aim of solving the path planning problem of autonomous underwater vehicles （ AUV）, and a fuzzy path planning algorithm based on particle swarm optimization （PSO） was presented according to fuzzy logic control theories. First, a fuzzy rule for the path planning was set up in the underwater horizontal plane. Then, an accelerate/brake （A/B）model was applied to dealing with static and dynamic avoidance. Considering that the fuzzy boundary choice is of great arbitrariness, and the formation of the path is not optimal, the PSO algorithm was applied to optimization of the fuzzy boundary for making the final generated path most optimal. The designed PSO-fuzzy algorithm was intended to do statics and dynamic obstacle avoidance path planning. The simulation results verified the effectiveness of the proposed design method.