NRI: FND: Extending Autonomy in Seemingly Sensory-Denied Environments Applied to Underwater Robots

美国的许多沿海社区人口稠密，城市化程度很高，这为评估城市化和气候对沿海海洋物理和生物状态的影响提供了关键位置。有效的研究需要利用自动车辆进行长期、持续的海洋监测，以使人们能够更深入地了解这些过程。该项目旨在克服理论和技术挑战，使不同类型的自动车辆组能够在动态和感官剥夺的环境中执行智能采样，重点放在海事领域。这些努力的成功将改善天气预报、水下运输动力学的理解，以及对水环境中各种物理现象的建模和预测。研究团队将通过利用佛罗里达国际大学(一家为少数族裔服务的机构)多样化的学生群体，让STEM中代表不足的群体参与研究项目。这些教育活动旨在积极影响一代年轻的技术人员，并让他们掌握强大的跨学科技能。该项目旨在通过创造新的测绘、定位、导航和机器人与人的通信技术来推动机器人和海洋科学的发展，使自主水上机器人能够在传感器被拒绝的动态环境中进行智能采样。研究活动分为四项任务。第一项任务是从物理和生物模型、水深测量和缺乏世界参考模型的现场样本中开发水环境和特征的3D表示。第二个任务为水生机器人开发了一个动态测绘和定位的新框架，以便在动态变化的水生特征中执行有针对性的采样。任务3开发了既对人类友好又易于机器人消化的地图表示法，以促进数据交换较少的知识转移。任务4包括使用模拟、实验室实验和全面实地试验对所开发的方法进行实验验证。计划中的地图表示、定位和导航的新框架可以在移动机器人系统中找到应用，超越了水上交通工具，如地下和空间机器人。这一奖项反映了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。

Many coastal communities in the U.S. are densely populated and highly urbanized, presenting critical locations for assessing the effects of urbanization and climate on the coastal ocean's physical and biological states. Effective study requires long-term, persistent ocean monitoring utilizing autonomous vehicles to enable a deeper understanding of these processes. This project aims to overcome the theoretical and technical challenges to enable heterogeneous groups of autonomous vehicles to perform intelligent sampling in dynamic and sensory-denied environments, with a focus on the maritime domain. The success of these endeavors will improve weather forecasting, underwater transport dynamics understanding, and modeling and prediction of various physical phenomena in aquatic environments. The team of researchers will engage underrepresented groups in STEM in the research program by leveraging a diverse student population at Florida International University (a minority-serving institution). The educational activities aim to positively impact a generation of young technologists and equip them with a strong interdisciplinary skillset.This project aims to advance robotics and ocean sciences by creating novel mapping, localization, navigation, and robot-human communication techniques that will enable autonomous aquatic vehicles to sample intelligently in sensor-denied, dynamic environments. The research activities are divided into four tasks. The first task develops 3D representations of aquatic environments and features from physical and biological models, bathymetry, and in situ samples that are devoid of world reference models. The second task develops a new framework for dynamic mapping and localization for aquatic robots to perform targeted sampling within dynamically evolving aquatic features. Task 3 develops map representations that are both human-friendly and robot-digestible to facilitate knowledge transfer with low data exchange. Task 4 includes the experimental validation of the developed approaches using simulation, laboratory experiments, and full-scale field trials. The planned novel frameworks to map representation, localization, and navigation can find applications in mobile robot systems beyond aquatic vehicles such as subterranean and space robotics.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.