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Extreme Learning-Based Monocular Visual Servo of an Unmanned Surface Vessel

机译：基于极端的基于学习的无人血管的单眼视觉伺服

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摘要

In this article, suffering from unmatched visual-servo uncertainties and unknown dynamics/disturbances, an extreme learning-based monocular visual-servo (ELMVS) scheme is developed for maneuvering an unmanned surface vessel (USV) to reach the desired pose. By virtue of the backstepping philosophy, complex visual-servo unknowns are elaborately encapsulated into lumped nonlinearities, which are further accurately accommodated by devising a single-hidden layer feedforward network based adaptive compensating identifier (SACI). Within the SACI architecture, hidden nodes are completely model free and are randomly generated without tedious learning, and thereby dramatically expediting fast-dynamics identification. Moreover, by exploiting approximation residuals, direct hyperbolic-tangent links between input and output layers are deployed to enhance identification accuracy. Eventually, the Lyapunov synthesis guarantees that the proposed ELMVS scheme can asymptotically render visual-servo errors arbitrarily small while target features can be kept within the field of view. Remarkable performance and superiority is finally demonstrated on a prototype USV.
机译：在本文中，患有无与伦比的视觉伺服不确定性和未知的动力学/扰动，是开发出极端的基于学习的单眼视觉伺服（ELMV）方案，用于操纵无人面的表面容器（USV）以达到所需的姿势。借助于Backstepping理念，复杂的视觉伺服未知数被精细地封装成集总线的非线性，通过设计基于单隐藏的层前馈网络的自适应补偿标识符（SACI）进一步精确地容纳。在SACI架构中，隐藏的节点完全是免费的，并且随机生成而没有繁琐的学习，从而大大加快快速动态识别。此外，通过利用近似残差，部署输入和输出层之间的直接双曲线连接，以提高识别精度。最终，Lyapunov综合保证了所提出的ELMVS方案可以随心所欲地呈现视觉伺服错误，而目标功能可以保存在视野中。在原型USV上最终展示了显着的性能和优越性。

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