基于蚁群算法的无人船平滑路径规划  

Path Planning and Smoothing for Unmanned Surface Vehicle Based on Improved Ant Colony Optimization

作　　者：孙鹏娜 张忠民[1] SUN Pengna;ZHANG Zhongmin(College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China)

机构地区：[1]哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨150001

出　　处：《电子科技》2023年第3期14-20,共7页Electronic Science and Technology

基　　金：国家自然科学基金(62001136)。

摘　　要：为解决无人驾驶船舶在复杂环境中规划路径时存在的转向角度大、路径拐点多、航行能耗高等问题,文中提出一种基于改进蚁群算法的平滑路径规划方法。该方法采用栅格法进行环境建模,通过在启发函数中引入路径平滑度、距离启发因子以及在路径转移概率中引入障碍物启发因素,提高路径寻优和静态避障能力。结合启发因素改进信息素更新标准,设置可调节信息素挥发因子增加算法的自适应性。提取输出的最优路径关键节点并对其进行平滑处理,进一步保证路径平滑度和安全性。根据不同栅格环境下的避障仿真结果可知,与传统算法相比,文中改进蚁群算法的路径寻优速度提高了45%~62%,转向次数减少了25%~44%,平滑处理后的路径安全性和可行性得到了提升,较好地实现了不同环境下无人船自主路径规划。In view of the problems of USV path planning in complex environment, such as large steering angle, many turning points, and high energy consumption, a path planning and smoothing method based on improved ant colony optimization is proposed. The method adopts the grid method for environmental modeling, and improves the path optimization and static obstacle avoidance ability by introducing the path smoothness and distance heuristic factor into the heuristic function and introducing the obstacle heuristic factor into the path transition probability. Combined with heuristic factors, the pheromone update standard is improved, and the adaptability of the algorithm to increase the volatile factor of pheromone can be adjusted. And then the key nodes of the optimal path are extracted and smoothed to further guarantee path smoothness and security. According to the simulation results of obstacle avoidance under different grid map, compared with the traditional ACO, the path optimization speed of improved ACO is increased by 45%~62%, and the steering times of path is reduced by 25%~44 %. Moreover, the path security and feasibility after smoothing are improved. The above results show that the autonomous path planning of USV in different environments is realized.

关 键 词：蚁群算法 无人驾驶船舶 路径规划 路径平滑 栅格地图 静态避障 启发函数 B样条曲线 

分 类 号：TP273.5[自动化与计算机技术—检测技术与自动化装置]