超小型水下机器人关键性能提升技术研究
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摘要
超小型水下机器人常被用在江河湖海的浅水水域中,广泛应用于水库堤坝检查、核电站检查、海上钻井平台与桥墩水下部分的监测与修复,沉船考古、海底光缆检测、海带收割、绿藻探查以及水雷布放、水下侦察等民用和军事领域。
本文研究了超小型水下机器人关键性能提升技术,包括位姿检测系统、扫描声纳图像校正系统和基于包容结构的开放式控制系统。水下机器人本体的位姿检测是其控制、扫描声纳图像几何校正等任务的基础;声图像校正处理是水下机器人正确感知水下环境的重要手段,是水下作业、检测等的基础;控制系统是水下机器人的核心部分,它对水下机器人的操纵性、可靠性等起决定作用。本论文的主要工作包括:
(1)超小型水下机器人智能快速定位。
超小型水下机器人位置测量的常用设备是短基线定位声纳系统,它的定位速率较低且连接不够可靠,很难与姿态测量传感器同步。本论文在对比分析常用的机器人概率定位算法基础上,使用群集智能的种群粒子优化定位算法推算短基线定位声纳实测信号间隙水下机器人的位置,提高定位速度;使用模块化的方案融合短基线定位声纳、电子罗盘、X/Y倾角传感器和深度传感器组成超小型水下机器人定位的硬件系统,增强定位系统的可扩展性,部分模块出现异常时不影响其它模块的正常工作;基于构件的软件设计方法使系统具有良好的伸缩性、协作性和重用性。在船模实验池中进行了定位效果测试实验,结果表明采用该方法能有效提高水下机器人的定位速度,使得位置测量与姿态测量同步,为后续的控制及图像采集处理系统提供及时的反馈和参考信号。
(2)基于位姿检测与形态学操作的声纳图像校正。
首先使用递归最小二乘滤波器对单一扫描角度上的声纳信号进行预处理;对于ROV位姿变化引起的扫描声纳图像的几何畸变,先根据本论文提出的位姿检测系统得到的ROV位姿信号进行几何校正,在此基础上使用了变结构元的数学形态学操作方法对校正后的扫描声纳图像做进一步处理,得到较为理想的结果。由结果图像可以看出,经过校正处理后,声纳图像的畸变现象得到改善且可以方便后续的障碍物识别。
(3)一种基于包容结构的开放式水下机器人控制系统。
水下机器人具有工作环境的复杂性和水下作业的任务多变性等特点,使得传统的层级式的机器人控制系统结构难以满足需求;而简单的开放式系统又容易引起整体性能变差、效率低下等缺点。本论文提出了一种基于包容结构的开放式控制系统,使用开放式的系统集成方式,而在水下机器人的运动控制中使用包容式结构。根据水下机器人的工作特点分析了水下机器人控制的主要内容;在此基础上使用Q-学习算法控制水下机器人运动,在学习过程的动作选择阶段采用基于径向基函数的神经网络。以艏向角锁定为例的仿真实验表明,相对于单纯的基于径向基函数的神经网络,本论文所使用的方法使得水下机器人艏向角锁定的均方误差有明显下降;而在经过初始阶段的学习后最大、最小误差也都有较大的下降。
Super-miniUnderwater Vehicles(SUV) are widely used in the shallow waters of rivers and lakes, they have now been widely used in civil and military fields such as hidden defectsdetection of dam and dyke,underwaterstructure repair and examination of offshore drilling platforms,repairing of submarine optical cable,kelp reaping,green algaeexploration,laying and sweeping of mines,underwater reconnaissance and so on.
The paper aims to study the SUV designment and its key technology, including pose detection, control system and image acquisition/processing system. Pose detection of the underwater vehicle plays a decisive role in its controlling and navigation. The control system, the foundational core of SUV,it decides the maneuverability and reliability of the underwater vehicle. The image acquisition/processing system are methods for sense underwater environment.
The main work of this paper includes:
(1) A fast intelligent positioning method for underwater vehicle. Common tool of underwater vehicle positioning system is Short BaseLine positioning sonar (SBL), which is low speed and unreliable. Based on the comparative analysis of usual probabilistic location algorithm of robots, the particle swarm optimization method is used to calculate the position of vehicle between measured SBL signals to speed up the positioning system.It fuses the SBL, X/Y angle sensor and depth sensor effectually to make up the hardware architecture of SUV positioning system, and develop the software of positioning system adopting component-based method. Positioning effect test experiment is carried out in a ship model tank, the results show that this method can improve the speed of underwater vehicle positioning system, and make SBL synchronized with pose sensors.
(2) Scanning sonar image correction based on positioning system and morphology. The sonar data in single scan angle is processed by Recursive Least Squares (RLS) filter. The distortion of scanning sonar image brought in by posture change of ROV is corrected by the results detected by pose detection system proposed in the paper. Then the variable structure element of mathematical morphology operation method is used for the corrected scan sonar images for further processing.It can be seen from the results, binary side scanning sonar image can provide facilitate for the identification of obstacles
(3) An open SUV controlling system based on subsumption architecture. The traditional hierarchical structure of the robot control system can not satisfy the demands of underwater vehicles because of complexity work environment and variability of underwater tasks, as to a simple open system, itmay cause deterioration or low efficiency. In result, this paper proposes a subsumption structure based open control system, which is roughly divided into several modules, integrate the modules based on subsumption structure while the subsystems’integration of each module based on open architecture. Analyse the main contents of underwater vechile controlling according to its working characteristics. A control scheme is applied in motion control,which is based on Q-Learning (QL) agent combined with Radial Basis Function (RBF) neural network control algorithm. The mean square error of the method used in this paper is smaller than pure RBF neural networkcontrol method in the heading angle lock simulation experiment.
本文研究了超小型水下机器人关键性能提升技术,包括位姿检测系统、扫描声纳图像校正系统和基于包容结构的开放式控制系统。水下机器人本体的位姿检测是其控制、扫描声纳图像几何校正等任务的基础;声图像校正处理是水下机器人正确感知水下环境的重要手段,是水下作业、检测等的基础;控制系统是水下机器人的核心部分,它对水下机器人的操纵性、可靠性等起决定作用。本论文的主要工作包括:
(1)超小型水下机器人智能快速定位。
超小型水下机器人位置测量的常用设备是短基线定位声纳系统,它的定位速率较低且连接不够可靠,很难与姿态测量传感器同步。本论文在对比分析常用的机器人概率定位算法基础上,使用群集智能的种群粒子优化定位算法推算短基线定位声纳实测信号间隙水下机器人的位置,提高定位速度;使用模块化的方案融合短基线定位声纳、电子罗盘、X/Y倾角传感器和深度传感器组成超小型水下机器人定位的硬件系统,增强定位系统的可扩展性,部分模块出现异常时不影响其它模块的正常工作;基于构件的软件设计方法使系统具有良好的伸缩性、协作性和重用性。在船模实验池中进行了定位效果测试实验,结果表明采用该方法能有效提高水下机器人的定位速度,使得位置测量与姿态测量同步,为后续的控制及图像采集处理系统提供及时的反馈和参考信号。
(2)基于位姿检测与形态学操作的声纳图像校正。
首先使用递归最小二乘滤波器对单一扫描角度上的声纳信号进行预处理;对于ROV位姿变化引起的扫描声纳图像的几何畸变,先根据本论文提出的位姿检测系统得到的ROV位姿信号进行几何校正,在此基础上使用了变结构元的数学形态学操作方法对校正后的扫描声纳图像做进一步处理,得到较为理想的结果。由结果图像可以看出,经过校正处理后,声纳图像的畸变现象得到改善且可以方便后续的障碍物识别。
(3)一种基于包容结构的开放式水下机器人控制系统。
水下机器人具有工作环境的复杂性和水下作业的任务多变性等特点,使得传统的层级式的机器人控制系统结构难以满足需求;而简单的开放式系统又容易引起整体性能变差、效率低下等缺点。本论文提出了一种基于包容结构的开放式控制系统,使用开放式的系统集成方式,而在水下机器人的运动控制中使用包容式结构。根据水下机器人的工作特点分析了水下机器人控制的主要内容;在此基础上使用Q-学习算法控制水下机器人运动,在学习过程的动作选择阶段采用基于径向基函数的神经网络。以艏向角锁定为例的仿真实验表明,相对于单纯的基于径向基函数的神经网络,本论文所使用的方法使得水下机器人艏向角锁定的均方误差有明显下降;而在经过初始阶段的学习后最大、最小误差也都有较大的下降。
Super-miniUnderwater Vehicles(SUV) are widely used in the shallow waters of rivers and lakes, they have now been widely used in civil and military fields such as hidden defectsdetection of dam and dyke,underwaterstructure repair and examination of offshore drilling platforms,repairing of submarine optical cable,kelp reaping,green algaeexploration,laying and sweeping of mines,underwater reconnaissance and so on.
The paper aims to study the SUV designment and its key technology, including pose detection, control system and image acquisition/processing system. Pose detection of the underwater vehicle plays a decisive role in its controlling and navigation. The control system, the foundational core of SUV,it decides the maneuverability and reliability of the underwater vehicle. The image acquisition/processing system are methods for sense underwater environment.
The main work of this paper includes:
(1) A fast intelligent positioning method for underwater vehicle. Common tool of underwater vehicle positioning system is Short BaseLine positioning sonar (SBL), which is low speed and unreliable. Based on the comparative analysis of usual probabilistic location algorithm of robots, the particle swarm optimization method is used to calculate the position of vehicle between measured SBL signals to speed up the positioning system.It fuses the SBL, X/Y angle sensor and depth sensor effectually to make up the hardware architecture of SUV positioning system, and develop the software of positioning system adopting component-based method. Positioning effect test experiment is carried out in a ship model tank, the results show that this method can improve the speed of underwater vehicle positioning system, and make SBL synchronized with pose sensors.
(2) Scanning sonar image correction based on positioning system and morphology. The sonar data in single scan angle is processed by Recursive Least Squares (RLS) filter. The distortion of scanning sonar image brought in by posture change of ROV is corrected by the results detected by pose detection system proposed in the paper. Then the variable structure element of mathematical morphology operation method is used for the corrected scan sonar images for further processing.It can be seen from the results, binary side scanning sonar image can provide facilitate for the identification of obstacles
(3) An open SUV controlling system based on subsumption architecture. The traditional hierarchical structure of the robot control system can not satisfy the demands of underwater vehicles because of complexity work environment and variability of underwater tasks, as to a simple open system, itmay cause deterioration or low efficiency. In result, this paper proposes a subsumption structure based open control system, which is roughly divided into several modules, integrate the modules based on subsumption structure while the subsystems’integration of each module based on open architecture. Analyse the main contents of underwater vechile controlling according to its working characteristics. A control scheme is applied in motion control,which is based on Q-Learning (QL) agent combined with Radial Basis Function (RBF) neural network control algorithm. The mean square error of the method used in this paper is smaller than pure RBF neural networkcontrol method in the heading angle lock simulation experiment.
引文
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