Advancing Underwater Robots in Complex Environments

在复杂环境中推进水下机器人

• 批准号: 2154901
• 负责人: Mingxi Zhou
• 金额: $ 48.34万
• 依托单位: University of Rhode Island
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2154901&HistoricalAwards=false
• 关键词: Advancing; Underwater; Robots; Complex; Environments

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.

复杂的水下环境，如水下洞穴、淹没的矿井、海上风电场和冰下沃茨，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可以提供有关我们历史的证据，而水下基础设施的健康对于可靠的能源和蓝色经济至关重要。虽然这些复杂的环境对人类潜水员进行重复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人。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。一个高度可拆卸的水下机器人将被设计，制造和测试，在紧凑的复杂空间移动的概念证明。该机器人将可访问和定制，旨在发展和扩大水下机器人研究社区。此外，还将对设计学院的非STEM学生进行外展工作，以实现融合教育。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，一种新型的水（一个术语来自水力和杂技）机器人能够执行具有挑战性的演习，如在原地转弯，悬停，垂直下降，将被设计。其次，一种新的自适应神经网络学习控制方案将开发，使机器人有效地跟踪所需的轨迹精确的动态不确定性和时变的环境干扰。同时，它将准确地识别/学习的非线性不确定的机器人动力学通过在线闭环控制与可证明的保证在控制和学习性能。最后，将创建一个基于学习的地形重建算法的大口径声纳。该算法克服了由单幅声纳图像重建水下环境时存在的高程模糊问题。改进后的地形重建结果有望推动机器人定位、导航和路径规划等其他基本方面的发展。该项目得到了机器人跨部门基础研究计划的支持，由工程局（ENG）和计算机与信息科学与工程局（CISE）共同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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。