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

超过70%的地球表面被海洋所覆盖，海洋被认为是地球上生命的摇篮。然而，人们对海底和海洋的了解还不如对月球表面的了解多。对海洋的科学探索对于了解地球气候和生命形式至关重要。然而，由于海洋的深度，温度低，没有光线，大多数海洋都不适合人类居住。自20世纪60年代以来，海洋学家和工程师一直致力于开发遥控水下机器人，目的是探索海洋深处。GPS不能在水下工作的事实使得水下导航对自主机器人来说尤其具有挑战性。该项目旨在通过开发新的导航和控制方法来推进水下探测，从而使地球科学、商业和国家安全任务超越目前的能力。该项目旨在通过开发新的模型识别、基于模型的车辆状态估计和基于模型的自适应控制方法，为自主欠驱动水下航行器（uuv）开发改进的导航和控制方法。目标是将目前水下航行器导航的精度提高一到两个数量级，达到亚米精度。我们的方法是开发新的鲁棒RANSAC基于零空间的最小二乘（NBLS）算法，用于在存在传感器噪声和异常值的情况下，对欠驱动uuv的6自由度植物动态模型的双模型（水面和水下）植物和执行器参数进行实验识别，并将这些植物模型用于欠驱动uuv的6自由度状态估计器中，以提高车辆导航精度。研究基于切换非线性模型的欠驱动uuv自适应控制器，以提高车辆控制精度。所提出算法的稳定性将进行分析评估，其性能将在机器人硬件的模拟和实验中进行评估。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.