采用量子粒子群算法的潜器路径规划

Path planning for autonomous underwater vehicles by using QPSO

针对复杂海底环境中的潜器路径规划问题,提出了一种采用量子粒子群算法的潜器路径规划方法.该方法首先从海图中提取水深数据,基于自然邻点插值和随机中点位移插值得到密集规格水深数据.然后由此数据建立海底三维模型,确定一个路径安全性检测方案及避碰原则,将海流大小方向对潜器航行的影响和路径点的转弯角度对航行的影响转化为相应的路径长度.最后将总长度作为适应度函数,利用量子粒子群算法迭代来求取最优路径.仿真结果得到了一条安全、简洁的路径,验证了该方法的有效性和可行性.

In efforts to address the submersible path planning problem in complex undersea environment, a path planning method for autonomous underwater vehicles was put forward, which is based on quantum-behaved particle swarm optimization （PSO）. First, the bathymetric data was extracted from a nautical chart, and the intensive-spec- ification depth data was acquired by dealing with the natural neighbor interpolation and the random midpoint dis- placement interpolation. Next, we were able to establish the undersea 3D model and determine a path security tes- ting program, along with the principle to prevent collisions. The influence of the ocean current size, and direction on autonomous underwater vehicle navigation and the influence of the turning angle of the path points on navigation were transformed into corresponding path lengths. At last, the total length was used as the fitness function and the optimal path was obtained by iteration of quantum-behaved particle swarm optimization （QPSO）. A safe and simple path was achieved as the result of the simulation, verifying effectiveness and feasibility of the method.