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Model-based Guidance, Navigation and Control architecture for an Autonomous Underwater Vehicle

机译:自主水下车辆的模型指导,导航和控制架构

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This article studies the design, modeling, and implementation of a model-based Guidance, Navigation, and Control (GNC) architecture for an Autonomous Underwater Vehicle (AUV). First, effective simulation modeling is developed using a theoretical six-degree-of-freedom (6DoF) dynamic model. Then, this study considers two GNC algorithms (simple and advanced). The simple GNC algorithm considers three different kinds of PID controllers (velocity, velocity-position, and position), and the advanced GNC algorithm enables path-following and data acquisition and processing from an underwater sensor. The path following is based on the position control using a unique PID controller and obtains its waypoints from a wall detection algorithm. This wall detection algorithm uses a mechanical imaging sonar as the main perception sensor. Finally, an implementation challenge in two control scenarios is addressed to validate the designed GNC architecture and to carry out model-verification of the position PID controller.
机译:本文研究了自动水下车辆(AUV)的基于模型的指导,导航和控制(GNC)架构的设计,建模和实施。首先,使用理论六维自由度(6dof)动态模型开发了有效的仿真建模。然后,本研究考虑了两个GNC算法(简单和高级)。简单的GNC算法考虑了三种不同类型的PID控制器(速度,速度 - 位置和位置),高级GNC算法使路径跟踪和数据获取和从水下传感器进行处理。路径之后是使用唯一PID控制器的位置控制,并从墙检测算法获得其航点。该墙壁检测算法使用机械成像声纳作为主要感知传感器。最后,解决了两个控制方案中的实现挑战,以验证设计的GNC架构并执行位置PID控制器的模型验证。

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