Collaborative Research: NRI: INT: Cooperative Underwater Structure Inspection and Mapping

合作研究：NRI：INT：合作水下结构检查和测绘

• 批准号: 2024741
• 负责人: Ioannis Rekleitis
• 金额: $ 54.37万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024741&HistoricalAwards=false
• 关键词: Collaborative; Research; NRI; INT; Cooperative

This project develops a system of co-robots collaborating with a human operator to map underwater structures. Underwater structure mapping is an important capability applicable to multiple domains: marine archaeology, infrastructure maintenance, resource utilization, security, and environmental monitoring. The underwater environment is challenging and dangerous for humans in many aspects, while robotic operations face additional challenges compared to the above-water ones. In particular, both sensing and communications are restricted, and planning is required in three dimensions with limited information. The project will generate a 3D model of the underwater structure providing a high-resolution photo-realistic representation. Autonomous Underwater Vehicles (AUVs)will be operating in close cooperation, generating a dense vision-based reconstruction of the observed surface, and coordinated with remote human operators.. The project integrates research and education through training of undergraduate and graduate students, who will have the opportunity to work in an inclusive, interdisciplinary team across South Carolina, New Jersey, and New Hampshire. The system will be integrated and tested for archaeological mapping at field sites. Research will be conducted along three directions. (1) Robust underwater state estimation based on a deep learning approach and a hybrid representation for 3-D reconstruction that will encode probabilistic occupancy for both navigation and initial inspection from users. (2) Collaborative planning, for the proximal observers based on a local optimization framework that originally considers multiple criteria, including information gain, uncertainty reduction, and loop closure, active positioning of distal observers, and user preference to make joint measurements and inform proximal observers on where to go. (3) Information driven communications, with careful design of efficient data representation of the 3-D reconstruction and of a cross-layer optimization for deciding when and how to share. These three components will contribute towards the overarching goal of enabling a team of co-robots to operate autonomously and produce a realistic map of an underwater structure.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.

该项目开发了与人类操作员合作以绘制水下结构的共同机器人系统。水下结构映射是适用于多个领域的重要功能：海洋考古学，基础设施维护，资源利用，安全性和环境监控。在许多方面，水下环境对人类具有挑战性和危险，而与上述水相比，机器人操作面临着其他挑战。特别是，感应和通信受到限制，并且在三个维度中需要计划有限的信息。该项目将生成水下结构的3D模型，从而提供高分辨率的照片现实代表。自动驾驶水下车辆（AUV）将在密切合作中运作，产生基于观察到的表面的密集重建，并与遥远的人类运营商进行协调。该项目通过对本科生和研究生的培训来整合研究和教育，他们将有机会在南卡罗来纳州南卡罗来纳州，新Jersey，New Jersey和New Hampshire和New Jersey和New Hampshire的整个跨学科团队中工作。该系统将在现场位点进行集成和测试，以进行考古映射。 研究将沿三个方向进行。 （1）基于深度学习方法的强大水下状态估计以及3-D重建的混合表示，将对用户的导航和初步检查编码概率占用。 （2）基于本地优化框架的近端观察者的合作计划，该框架最初考虑了多个标准，包括信息增益，降低不确定性和循环封闭，远端观察者的主动定位以及用户的优先选择，以进行关节测量并告知近端观察者。 （3）信息驱动的通信，并仔细设计了3-D重建的有效数据表示以及用于决定何时以及如何共享的跨层优化。这三个组成部分将有助于使一组共同机器人团队自主操作并制作出水下结构的现实地图。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来获得支持的。

项目成果

Real-Time Dense 3D Mapping of Underwater Environments

水下环境的实时密集 3D 测绘

• DOI: 10.1109/icra48891.2023.10160266
• 发表时间: 2023
• 期刊: IEEE
• 作者: Wang, Weihan;Joshi, Bharat;Burgdorfer, Nathaniel;Batsos, Konstantinos;Quattrini Li, Alberto;Mordohai, Philippos;Rekleitis, Ioannis
• 通讯作者: Rekleitis, Ioannis

AquaVis: A Perception-Aware Autonomous Navigation Framework for Underwater Vehicles

AquaVis：水下航行器的感知感知自主导航框架

• DOI: 10.1109/iros51168.2021.9636124
• 发表时间: 2021
• 期刊: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
• 作者: Xanthidis, Marios;Kalaitzakis, Michail;Karapetyan, Nare;Johnson, James;Vitzilaios, Nikolaos;O'Kane, Jason M.;Rekleitis, Ioannis
• 通讯作者: Rekleitis, Ioannis

DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization

• DOI: 10.1109/iros45743.2020.9341201
• 发表时间: 2020-03
• 期刊: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Bharat Joshi;M. Modasshir;Travis Manderson;Hunter Damron;M. Xanthidis;Alberto Quattrini Li;Ioannis M. Rekleitis;G. Dudek

3-D Reconstruction Using Monocular Camera and Lights: Multi-View Photometric Stereo for Non-Stationary Robots

• DOI: 10.1109/icra48891.2023.10160459
• 发表时间: 2023-05
• 期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Monika Roznere;Philippos Mordohai;Ioannis M. Rekleitis;Alberto Quattrini Li

Human Diver-Inspired Visual Navigation: Towards Coverage Path Planning of Shipwrecks

受人类潜水员启发的视觉导航：沉船覆盖路径规划

• DOI: 10.4031/mtsj.55.4.6
• 发表时间: 2021
• 期刊: Marine Technology Society Journal
• 影响因子: 0.8
• 作者: Karapetyan, Nare;Johnson, James V.;Rekleitis, Ioannis
• 通讯作者: Rekleitis, Ioannis