Because of their wide detection range and rich functions,autonomous underwater vehicles(AUVs)are widely used for observing the marine environment,for exploring natural resources,for security and defense purposes,and i...Because of their wide detection range and rich functions,autonomous underwater vehicles(AUVs)are widely used for observing the marine environment,for exploring natural resources,for security and defense purposes,and in many other fields of interest.Compared with a single AUV,a multi-AUV formation can better perform various tasks and adapt to complex underwater environments.With changes in the mission or environment,a change in the UAV formation may also be required.In the last decade,much progress has been made in the transformation of multi-AUV formations.In this paper,we aim to analyze the core concepts of multi-AUV formation transformation;summarize the effects of the AUV model,underwater environment,and communication between AUVs within formations on formation transformation;and elaborate on basic theories and implementation approaches for multi-AUV formation transformation.Moreover,this overview includes a bibliometric analysis of the related literature from multiple perspectives.Finally,some challenging issues and future research directions for multi-AUV formation transformation are highlighted.展开更多
Inspired by cooperative hunting of lionesses,this paper presents a Lions Group Algorithm for cooperative hunting tasks involving multiple AUVs(autonomous underwater vehicles).The lions group algorithm is developed aro...Inspired by cooperative hunting of lionesses,this paper presents a Lions Group Algorithm for cooperative hunting tasks involving multiple AUVs(autonomous underwater vehicles).The lions group algorithm is developed around two core relationships:the dynamic game relationship between the hunters and the target and the cooperative relationship between hunters.In this paper,the lions group algorithm is divided into three stages.In each stage,the dynamic game model between the hunters and the target is constructed,and the cooperation model between the hunters is constructed.At the same time,in these three phases,a dynamic allocationmechanism for the roles and tasks of hunterswas established.The simulation experiment revealed the hunting effect.The results show that the path planning and obstacle avoidance strategy of the hunters,the target’s escape strategy,the complexity of the environment,and the speed relationship between the hunter and the target affect the hunting effect.展开更多
针对多自主水下航行器(Autonomous Underwater Vehicle,AUV)的全覆盖路径规划问题,提出了一种考虑随机初始位置约束的多AUV覆盖路径规划方法(Dividing Areas based on Robots Initial Positions CPP,DARIP-CPP)。根据多自主水下机器人...针对多自主水下航行器(Autonomous Underwater Vehicle,AUV)的全覆盖路径规划问题,提出了一种考虑随机初始位置约束的多AUV覆盖路径规划方法(Dividing Areas based on Robots Initial Positions CPP,DARIP-CPP)。根据多自主水下机器人的随机初始位置对工作海域进行均衡区域划分,将划分所得的不重叠区域分配给多AUV进行独立覆盖路径规划,每台AUV利用生物启发神经网络(Bio-inspired Neural Network)优化各个区域的全覆盖路径。为了克服传统全覆盖路径规划中的“死区”问题,采用A^(*)路径规划算法进行“死区”逃离,沿着较短的路径快速到达未覆盖区域点。仿真结果表明,所提出的DARIPCPP方法可有效提高多AUV全覆盖目标区域的工作效率。展开更多
基金Support Program(No.61822304)the Basic Science Center Programs of NSFC(No.62088101)+2 种基金the Peng Cheng Laboratory,the Consulting Research Project of the Chinese Academy of Engineering(No.2019-XZ-7)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(No.61621063)the Projects of Major International(Regional)Joint Research Program of NSFC(No.61720106011).
文摘Because of their wide detection range and rich functions,autonomous underwater vehicles(AUVs)are widely used for observing the marine environment,for exploring natural resources,for security and defense purposes,and in many other fields of interest.Compared with a single AUV,a multi-AUV formation can better perform various tasks and adapt to complex underwater environments.With changes in the mission or environment,a change in the UAV formation may also be required.In the last decade,much progress has been made in the transformation of multi-AUV formations.In this paper,we aim to analyze the core concepts of multi-AUV formation transformation;summarize the effects of the AUV model,underwater environment,and communication between AUVs within formations on formation transformation;and elaborate on basic theories and implementation approaches for multi-AUV formation transformation.Moreover,this overview includes a bibliometric analysis of the related literature from multiple perspectives.Finally,some challenging issues and future research directions for multi-AUV formation transformation are highlighted.
文摘Inspired by cooperative hunting of lionesses,this paper presents a Lions Group Algorithm for cooperative hunting tasks involving multiple AUVs(autonomous underwater vehicles).The lions group algorithm is developed around two core relationships:the dynamic game relationship between the hunters and the target and the cooperative relationship between hunters.In this paper,the lions group algorithm is divided into three stages.In each stage,the dynamic game model between the hunters and the target is constructed,and the cooperation model between the hunters is constructed.At the same time,in these three phases,a dynamic allocationmechanism for the roles and tasks of hunterswas established.The simulation experiment revealed the hunting effect.The results show that the path planning and obstacle avoidance strategy of the hunters,the target’s escape strategy,the complexity of the environment,and the speed relationship between the hunter and the target affect the hunting effect.
文摘针对多自主水下航行器(Autonomous Underwater Vehicle,AUV)的全覆盖路径规划问题,提出了一种考虑随机初始位置约束的多AUV覆盖路径规划方法(Dividing Areas based on Robots Initial Positions CPP,DARIP-CPP)。根据多自主水下机器人的随机初始位置对工作海域进行均衡区域划分,将划分所得的不重叠区域分配给多AUV进行独立覆盖路径规划,每台AUV利用生物启发神经网络(Bio-inspired Neural Network)优化各个区域的全覆盖路径。为了克服传统全覆盖路径规划中的“死区”问题,采用A^(*)路径规划算法进行“死区”逃离,沿着较短的路径快速到达未覆盖区域点。仿真结果表明,所提出的DARIPCPP方法可有效提高多AUV全覆盖目标区域的工作效率。
文摘基于机器人队形控制中的领航者-跟随者方法(Leader-Follower Method)和基于行为方法(Behavior-based Method),提出了一种用于多自治水下机器人(AUV)队形控制的新方法。该方法以领航者来确定整个AUV编队的前进状态,跟随者时刻跟随领航者以保持一定的队形形状。同时,领航者和跟随者都被赋予一定的行为特性以实现奔向目标、队形维持、队形转换及障碍避碰等功能。考虑到领航者在行进过程中有可能会发生意外而导致任务失败,本方法设定一特殊的跟随者令其作为候选领航者时刻跟随领航者以防万一。分别采用模糊规划器和Line of Sight(LOS)技术来实现AUV编队的障碍避碰和导航。
