SUMMARYVisual tracking is an essential building block for target tracking and capture of the underwater vehicles. On the basis of remotely autonomous control architecture, this paper has proposed an improved kernelized correlation filter (KCF) tracker and a novel fuzzy controller. The model is trained to learn an online correlation filter from a plenty of positive and negative training samples. In order to overcome the influence from occlusion, the improved KCF tracker has been designed with an added self-discrimination mechanism based on system confidence uncertainty. The novel fuzzy logic tracking controller can automatically generate and optimize fuzzy rules. Through Q-learning algorithm, the fuzzy rules are acquired through the estimating value of each state action pairs. An S surface based fitness function has been designed for the improvement of learning based particle swarm optimization. Tank and channel experiments have been carried out to verify the proposed tracker and controller through pipe tracking and target grasp on the basis of designed open frame underwater vehicle.

中文翻译：

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开放式水下机器人目标智能视觉跟踪与控制策略

摘要视觉跟踪是水下航行器目标跟踪和捕获的重要组成部分。本文在远程自主控制架构的基础上，提出了一种改进的核相关滤波器（KCF）跟踪器和一种新颖的模糊控制器。该模型经过训练，可以从大量正负训练样本中学习在线相关过滤器。为了克服遮挡的影响，改进的 KCF 跟踪器设计了一个基于系统置信度不确定性的附加自识别机制。新颖的模糊逻辑跟踪控制器可以自动生成和优化模糊规则。通过Q-learning算法，通过每个状态动作对的估计值得到模糊规则。为了改进基于学习的粒子群优化，设计了一个基于 S 表面的适应度函数。在设计的开放式框架水下航行器的基础上，通过管道跟踪和目标抓取对所提出的跟踪器和控制器进行了坦克和通道实验，以验证所提出的跟踪器和控制器。