CAREER: Making Underwater Robots Live Underwater

该学院早期职业发展(CALEAR)项目旨在赋予水下机器人水下驻留能力。有了这一新能力，水下机器人将克服船上有限的能量，从而可以进行持续的海洋探测，并将它们与其他海洋观测系统连接起来，在沿海和偏远地区(例如，冰下或深海)收集关于关键能量、瞬变和不可预测的海洋过程(如海底火山喷发、触发的生物地球化学羽流、表观缺氧上升水)的关键观测数据。此外，水下常驻机器人可以持续监测结构健康和水性质，以支持可靠的近海能源和水产养殖生产，应对气候变化和粮食安全等重大挑战。该项目将把研究与教育项目结合起来，培训水下机器人和海洋技术方面的劳动力。具体地说，该项目将支持：1)两名研究生从事水下机器人的基础研究；2)多达20名高中生通过暑期工作坊建造水下机器人并与水下对接系统进行互动；3)两个关于水下对接臂设计和建造的顶峰项目，允许最多10名本科生进行原创性研究；以及4)以专题和演示的形式将研究整合到两个机器人班级(约80名学生)中。本项目以实现安全可靠的水下对接操作为总体目标，寻求推进三个机器人基础主题：水下定位、动态建模和非线性控制。首先，将开发一种学习增强的位姿估计器来推进基于视觉的机器人定位。该方法将采用深度神经网络(DNN)从单幅图像中获得摄像机到基于光信标的颜色强度之间的倾斜范围。其次，建立了一个新的基于动态神经网络的水下机器人动力学模型。该模型将使用真实世界的数据进行训练，以捕捉机器人执行控制信号和机器人状态之间的高度非线性映射，这些映射在现有的水下系统辨识和建模方法中通常被忽略或简化。新模型将潜在地改善飞行器控制和定位性能，这对水下对接至关重要。第三，设计了一种新的基于采样的非线性模型预测控制求解器，并进行了验证。该方法将提供一种端到端的计算易处理的解决方案，以同时控制多个机器人状态，同时包含驱动约束、能量优化和模型不确定性。最后，学生主导的研究将开发一种水下对接手臂，以适应不同的AUV系统和环境影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development (CAREER) project seeks to endow underwater resident capability for underwater robots. With the new capability, underwater robots will overcome the limited onboard energy, thus, could conduct persistent ocean sensing and connect them with other ocean observing systems to collect key observations at coastal and remote locations (e.g., under-ice or at deep sea) on critical energetic, transient, and unpredictable ocean processes (such as erupting submarine volcanos, triggered biogeochemical plumes, episodical anoxic upwelling waters). Moreover, underwater resident robots could provide persistent monitoring of structural health and water properties to support reliable offshore energy and aquaculture production, addressing the grand challenges, i.e., climate change and food security. The project will integrate research with educational programs on training the workforce in underwater robotics and ocean technology. Specifically, this project will support: 1) two graduate students to conduct fundamental research on underwater robots; 2) up to twenty high school students to build underwater robots and interact with underwater docking systems through summer workshops; 3) two capstone projects on underwater docking arm design and building allowing up to 10 undergraduate students to conduct original research; and 4) integrate research into two robotic classes (about 80 students) in the forms of special topics and demonstrations.With an overarching goal of achieving safe and reliable underwater docking operations, this project seeks to advance three fundamental robotic topics: underwater localization, dynamic modeling, and nonlinear control. First, a learning-augmented pose estimator will be developed to advance vision-based robot localization. The method will adapt deep neural networks (DNNs) to derive the slant range between a camera to a light beacon-based color intensities from a single image. Second, a new DNN-based dynamic model will be created for underwater robots. The model will be trained using real-world data to capture the highly nonlinear mapping between the robot's actuation control signals and the robot states, which are typically ignored or simplified in existing underwater system identification and modeling methods. The new model will potentially improve vehicle control and localization performance, which are critical for underwater docking. Third, a new sampling-based solver will be designed and validated for nonlinear model predictive control. The method will offer an end-to-end computational tractable solution to control multiple robot states simultaneously while incorporating actuation constraints, energy optimization, and model uncertainties. Finally, the student-led research will develop an underwater docking arm to accommodate different AUV systems and environmental impacts.This 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.