CAREER: Making Underwater Robots Live Underwater

这个教师早期职业发展（CAREER）项目旨在赋予水下机器人水下居住能力。有了新的能力，水下机器人将克服有限的船上能源，因此，可以进行持续的海洋传感，并将它们与其他海洋观测系统连接起来，以收集沿海和偏远地区的关键观测结果（例如，冰下或深海）对关键的高能、瞬变和不可预测的海洋过程（如爆发的海底火山、触发的海洋地球化学羽流、偶发性缺氧上涌沃茨）的影响。此外，水下驻留机器人可以提供对结构健康和水特性的持续监测，以支持可靠的海上能源和水产养殖生产，解决重大挑战，即，气候变化和粮食安全。该项目将把研究与培训水下机器人和海洋技术劳动力的教育计划结合起来。具体而言，该项目将支持：1）两名研究生进行水下机器人的基础研究; 2）最多20名高中生通过暑期讲习班建造水下机器人并与水下对接系统互动; 3）两个关于水下对接臂设计和建造的顶点项目，允许最多10名本科生进行原创研究; 4）以专题和演示的形式将研究整合到两个机器人班（约80名学生）中，以实现安全可靠的水下对接操作为总体目标，该项目旨在推进三个基本的机器人主题：水下定位，动力学建模和非线性控制。首先，将开发一种学习增强的姿态估计器，以推进基于视觉的机器人定位。该方法将适应深度神经网络（DNN），以从单个图像中导出相机与基于光信标的颜色强度之间的倾斜范围。其次，将为水下机器人创建一个新的基于DNN的动态模型。该模型将使用真实世界的数据进行训练，以捕获机器人的驱动控制信号和机器人状态之间的高度非线性映射，这些映射通常在现有的水下系统识别和建模方法中被忽略或简化。新模型将潜在地改善水下对接的关键的航行器控制和定位性能。第三，一个新的采样为基础的求解器将被设计和验证的非线性模型预测控制。该方法将提供一个端到端的计算易处理的解决方案，同时控制多个机器人状态，同时结合驱动约束，能量优化和模型的不确定性。最后，学生主导的研究将开发一个水下对接臂，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，并已被认为是值得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的支持。

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.