Advancing Underwater Robots in Complex Environments

复杂的水下环境，如水下洞穴、被淹的矿井、海上风电场和冰下水域，具有重大的社会和科学影响。例如，调查水下城市和探索水下洞穴为我们的历史提供了证据，水下基础设施的健康对于可靠的能源和蓝色经济极其重要。虽然这些复杂的环境对人类潜水员重复进行调查具有挑战性和风险，但水下机器人必须被视为可行的候选者。该奖项支持机器人设计、控制和感知方面的基础研究，以克服复杂水下环境中面临的挑战。作为概念验证，将设计、制造和测试高度机动性的水下机器人，以在狭窄的复杂空间中移动。该机器人将成为可访问和可定制的，目的是扩大和扩大水下机器人研究社区。此外，还将向设计学院的非STEM学生开展融合教育。该项目的总体目标是提高水下机器人在复杂环境中的功能和可靠性。为此，该项目将在三个方面为机器人基础研究做出贡献。首先，将设计一种新型的水力式(源于水力式和杂技)机器人，能够执行具有挑战性的动作，如原地转弯、悬停和垂直下降。其次，开发了一种新的自适应神经网络学习控制方案，使机器人能够在动态不确定和时变的环境扰动下有效地精确跟踪期望轨迹。同时，通过在线闭环控制，实现了对非线性不确定机器人动力学的精确辨识/学习，在控制和学习性能上都有可证明的保证。最后，建立了一种基于学习的大口径声纳地形重建算法。该算法克服了从单幅声纳图像重建水下环境时产生误差的仰角模糊问题。改进的地形重建结果有望推进机器人定位、导航和路径规划的其他基本方面。该项目由跨部门机器人基础研究计划支持，该计划由工程指导委员会(ENG)和计算机和信息科学与工程委员会(CEISE)联合管理和资助。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。

Complex underwater environments, such as underwater caves, flooded mines, offshore wind farms, and under-ice waters have significant societal and scientific impacts. For example, surveying underwater cities and exploring underwater caves provide evidence about our history, and the health of underwater infrastructure is extremely important for reliable energy and the blue economy. While these complex environments are challenging and risky for human divers to perform repeated surveys, underwater robots must be considered as viable candidates. This award supports fundamental research in robot design, control, and perception to overcome the challenges faced in complex underwater environments. A highly maneuverable underwater robot will be designed, fabricated, and tested for moving in tight complex spaces as proof of concept. The robot will be made accessible and customizable with the aim to grow and broaden the underwater robotics research community. Additionally, an outreach effort to non-STEM students from the School of Design will be conducted for convergent education.The overarching goal of the project is to increase the functionality and reliability of underwater robots in complex environments. To this end, the project will make contributions to the fundamental research in robotics in three aspects. First, a novel hydrobatic (a term derived from hydro and acrobatic) robot capable of performing challenging maneuvers, such as turn-in-place, hovering, and vertical descent, will be designed. Second, a novel adaptive neural-network learning control scheme will be developed to enable the robot to efficiently track a desired trajectory precisely under dynamic uncertainty and time-varying environmental disturbances. Meanwhile, it will accurately identify/learn the nonlinear uncertain robot dynamics through online closed-loop control with provable guarantees in both control and learning performances. Finally, a learning-based terrain reconstruction algorithm will be created for wide aperture sonars. This new algorithm would overcome the elevation ambiguity problem which causes errors when reconstructing underwater environments from a single sonar image. The improved terrain reconstruction result is expected to advance other fundamental aspects in robot localization, navigation, and path planning.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.