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

该项目开发了一个协同机器人系统，与人类操作员合作绘制水下结构。水下结构测绘是适用于多个领域的重要能力：海洋考古，基础设施维护，资源利用，安全和环境监测。水下环境在许多方面对人类来说都具有挑战性和危险性，而与水上环境相比，机器人操作面临更多挑战。特别是，传感和通信都受到限制，需要在信息有限的情况下进行三维规划。该项目将生成水下结构的3D模型，提供高分辨率的照片逼真的表示。自主水下航行器（AUV）将密切合作，对观察到的表面进行密集的基于视觉的重建，并与远程操作人员进行协调。该项目通过本科生和研究生的培训整合了研究和教育，他们将有机会在南卡罗来纳州，新泽西和新罕布什尔州的包容性，跨学科团队中工作。将对该系统进行整合和测试，以便在实地进行考古制图。 研究将沿着沿着三个方向进行。(1)基于深度学习方法和用于3-D重建的混合表示的鲁棒水下状态估计，该表示将对导航和用户初始检查的概率占用进行编码。(2)协作规划，为近端观察员的基础上，最初考虑多个标准，包括信息增益，不确定性降低，环路闭合，远端观察员的主动定位，以及用户偏好，使联合测量和通知近端观察员去哪里的本地优化框架。(3)信息驱动的通信，精心设计了3D重建的有效数据表示和跨层优化，以决定何时以及如何共享。这三个组成部分将有助于实现使一组协作机器人能够自主操作并绘制水下结构的逼真地图的总体目标。该奖项反映了NSF的法定使命，并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，被认为值得支持。

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.