RI: Small: Robust Model-Based Identification, Navigation, and Control of Underactuated Underwater Vehicles

RI：小型：基于鲁棒模型的欠驱动水下航行器识别、导航和控制

基本信息

批准号：
1909182
负责人：
Louis Whitcomb
金额：
$ 49.97万
依托单位：
Johns Hopkins University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909182&HistoricalAwards=false
关键词：
RI Small Robust Model Based

项目摘要

Over 70 percent of the Earth's surface is hidden by its oceans, which are believed to be the cradle of life on Earth. Yet, less is known about the sea floor and oceans than about the surface of the Moon. Scientific exploration of the oceans is critical to the understanding of the Earth's climate and life forms. However, most of the oceans are inhospitable to humans due to the immense depth, low temperatures, and the absence of light. Since the 1960's, oceanographers and engineers have worked towards the development of remotely-controlled underwater robots, with the goal of exploring the depths of the oceans. The fact that GPS does not work underwater makes underwater navigation especially challenging for autonomous robots. This project seeks to advance underwater exploration by developing new navigation and control methods to enable earth-science, commercial, and national-security missions that are beyond present capabilities.This project seeks to develop improved navigation and control methods for autonomous underactuated underwater vehicles (UUVs) through the development of novel approaches to model identification, model-based vehicle state estimation, and model-based adaptive control. The goal is to improve the accuracy of present-day underwater vehicle navigation by one to two orders of magnitude, down to sub-meter accuracy. Our approach is to develop novel robust RANSAC Nullspace-based Least-Square (NBLS) algorithms for the experimental identification of dual-model (surfaced and submerged) plant and actuator parameters of 6-DOF plant dynamic models for underactuated UUVs in the presence of sensor noise and outliers, to use these plant models in 6-DOF state estimators for underactuated UUVs to improve vehicle navigation precision, and to investigate switched nonlinear model-based adaptive controllers for underactuated UUVs to improve vehicle control precision. The stability of the proposed algorithms will be evaluated analytically and their performance will be evaluated both in simulation and in experiments with robotic hardwareThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

超过70％的地球表面被其海洋隐藏，这被认为是地球上生命的摇篮。然而，对海底和海洋知之甚少，而不是月球表面。对海洋的科学探索对于理解地球气候和生命形式至关重要。然而，由于巨大的深度，低温和没有光线，大多数海洋对人类来说是无所事事的。自1960年代以来，海洋学家和工程师一直致力于开发远程控制的水下机器人，目的是探索海洋深处。 GP不起作用的事实使水下导航尤其针对自动机器人具有挑战性。该项目旨在通过开发新的导航和控制方法来推动水下勘探，以实现超出当前能力的地球科学，商业和国家安全任务。该项目旨在开发改进的自动驾驶水下无型水下汽车的导航和控制方法（UUVS（UUVS））通过开发新颖的方法来模型识别，基于模型的车辆状态估计和基于模型的自适应控制。目的是将当今水下车辆导航的准确性提高一到两个数量级，直至次级准确性。我们的方法是开发新型的鲁棒RANSAC NULLSPACE的最小二乘（NBLS）算法，用于实验鉴定双模型（表面和淹没）植物的植物以及6-DOF植物动态模型的致动参数，用于在传感器的情况下，在没有传感器的情况下进行UUV。噪声和离群值，将这些植物模型在6-DOF状态估计器中使用不足的UUV来提高车辆导航精度，并研究基于开关的非线性模型的自适应控制器，以提高不足的UUV，以提高车辆控制精度。该算法的稳定性将通过分析进行评估，并将在模拟中评估其性能，并在具有机器人硬战争奖的实验中进行评估，这反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响标准通过评估来获得支持。