摘要
针对多无人艇编队避障问题,对静态避障的路径消耗问题进行建模分析,在动态避障时提出一种偏置人工势场法使策略符合艇群国际海上避碰规则(swarm International Regulations for Preventing Collisions at Sea,sCOLREGS)。本方法首先对传统人工势场法进行改进,定义符合艇群会遇态势判断需求的sCOLREGS,通过速度障碍法实时判断碰撞风险,然后利用偏置斥力区域的改进人工势场法实现对规则的遵守。仿真实验表明,本文方法在障碍物与编队大小相当时可显著减少避障路程,在确保避障实时性的同时,较好地遵守了国际海上避碰规则相关条例。研究结论可为海面无人艇集群安全航行提供参考。
For the multiple unmanned surface vehicles formation obstacle avoidance problem,the path consumption problem of static obstacle avoidance was modeled and analyzed,and a biased artificial potential field method was proposed for dynamic obstacle avoidance to make the strategy conform to the swarm International Regulations for Preventing Collisions at Sea(sCOLREGS).This method firstly improved the traditional artificial potential field method,defined the rules that meet the needs of the swarm situation,judged the collision risk by the velocity obstacle method,and then used the improved artificial potential field with biased repulsive region to achieve compliance with the rules.Simulation experiments show that the method in this paper can significantly reduce the obstacle avoidance distance when the obstacle is comparable to the formation size,and better comply with the regulations of International Regulations for Preventing Collisions at Sea(COLREGS)while ensuring real-time obstacle avoidance.The conclusion can provide a reference for the safe navigation of unmanned boat clusters on the sea surface.
作者
邹子理
孙骞
黄雨杰
李一兵
ZOU Zili;SUN Qian;HUANG Yujie;LI Yibing(College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China;MIIT Key Laboratory of Advanced Marine Communication and Information Technology,Harbin Engineering University,Harbin 150001,China)
出处
《应用科技》
CAS
2024年第1期166-176,共11页
Applied Science and Technology
基金
国家自然科学基金项目(52271311)
黑龙江头雁创新团队项目。
关键词
人工势场法
路径规划
多无人艇
艇群国际海上避碰规则
速度障碍法
栅格地图
虚拟领航者
动态避碰
artificial potential field
path planing
multiple unmaned surface vehicles
swarm International Regulations for Preventing Collisions at Sea
velocity obstacle
grid map
virtual leader
dynamic obstacle avoidance