摘要
[目的]针对复杂水下环境中可能存在的多种障碍物,影响多自主式水下机器人(AUV)集群运动规划问题,提出一种基于人工势场的多AUV集群实时避障方法。[方法]首先,采用一种基于动态网络拓扑的编队方法,将AUV看作网络中的节点,通过设置势场函数来满足编队要求;然后,基于人工势场法对同时存在目标和障碍的区域建立势场函数,并将势场函数改进为指数函数,对AUV进行在线规划,实时完成多AUV集群避障的任务;最后,在Matlab软件中仿真设置10台AUV和6个障碍物进行仿真验证。[结果]仿真结果表明,采用该方法,AUV可以全部顺利地避开障碍物,准确到达目标点的安全区域。[结论]人工势场函数法可准确实现多AUV的实时避障,该技术的进步对提高军事作战能力具有重要的意义。
[Objectives]This paper proposes a real-time obstacle avoidance method based on artificial potential field,in order to cope with the many potential obstacles in complex underwater environment which affects the movement plan for multi-autonomous underwater vehicle(AUV)cluster.[Methods]Firstly,a formation method based on dynamic network topology is adopted,and the AUV is regarded as a node in the network.The potential field function is set to meet the formation requirements.Then,based on the artificial potential field method,the potential field function is established for the region where both targets and obstacles exist simultaneously.Afterwards,the potential field function is upgraded to an exponential function,such that the AUVs can be planned online for real-time accomplishment of the mission of obstacle avoidance for multi-AUV cluster.Finally,10 AUVs and 6 obstacles are simulated with Matlab software.[Results]The simulation results show that with this method,each AUV can successfully avoid obstacles and reach the safe area at the target point.[Conclusions]The artificial potential field function method may enable the multi-AUV to accurately avoid obstacle in real time.The advancement of this technology has important and positive significance for improving military operational capability.
作者
徐博
张娇
王超
XU Bo;ZHANG Jiao;WANG Chao(College of Automation,Harbin Engineering University,Harbin 150001,China)
出处
《中国舰船研究》
CSCD
北大核心
2018年第6期66-71,共6页
Chinese Journal of Ship Research
基金
黑龙江省自然科学基金资助项目(F2018009)
装备预研重点实验室基金资助项目(614221801050717)
上海交通大学海洋工程国家重点实验室开放课题(1616)
关键词
自主式水下机器人
人工势场
集群运动
编队控制
避障
Autonomous Underwater Vehicle(AUV)
artificial potential field
movement in a cluster
formation control
obstacle avoidance
