CAREER: Enabling Autonomy via Enhanced Situational Awareness for Underwater Robotics

职业：通过增强水下机器人的态势感知实现自主性

• 批准号: 1943205
• 负责人: Ioannis Rekleitis
• 金额: $ 54.99万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943205&HistoricalAwards=false
• 关键词: CAREER; Enabling; Autonomy; via; Enhanced

The role of the aquatic environment is of great importance: it plays a critical role in climate; it contains the highest biodiversity on the planet; ports are among the most critical infrastructures for trade and transportation; and as much as 40% of the global population lives within 100km of the shoreline. Improving our understanding of the underwater domain is essential. Using autonomous robots to collect additional information will be safer, more cost effective, and can be extended to a larger scale than previous methods. The goal of this project is to enable autonomous operations of robotic systems in underwater environments. In order to achieve this goal, the robot needs to be aware of the environment, and of its own position inside the environment. The robot needs to develop movement strategies that would facilitate the efficient and accurate estimation of its position and the location of obstacles/objects in the environment, while taking into account the errors in the measurements. The underwater domain presents several unique challenges: there is no Global Positioning System (GPS); communication, when available, has extremely limited bandwidth; visibility conditions, even in the best-case scenarios, are limited due to particulates in the water that obstruct the view. The investigator will advance the state of the art in four areas: information from different robot sensors will be used to calculate the position of the robot as it moves through the underwater domain; then, the investigator and his graduate students will use all available information to produce a representation of the environment the robot can use to navigate; next, planning will be implemented to guide the robot through the environment taking into account the shorter distance and the areas with viewing interest; and finally, the team will investigate new strategies for exploring unknown environments efficiently. The investigator will use his research results from the underwater realm to raise interest for students and the general populace towards science, technology, engineering, and mathematics. The goal of this project is to enable autonomous operations of robotic systems in underwater environments. In order to achieve this goal, the robot needs situational awareness. Additionally, the robot needs to develop motion strategies that would facilitate the efficient and accurate estimation of its pose and the location of points of interest in the environment, while taking into account uncertainty buildup and the effect of external forces such as wind or current. The underwater domain renders satellite-based GPS ineffective. Communications, when available, have extremely limited bandwidth; and visibility conditions are limited due to hazing and blurring, lighting variations over time, and color loss. The investigator will advance the state of the art in four areas: information from different sensors will be used to calculate the pose of the robot as it moves through the underwater domain; all available information will be utilized to produce a dense representation of the environment; next, a decision process will be implemented to guide the robot through the environment taking into account efficiency (shorter distance) and the areas with viewing interest; finally, new strategies for exploring and covering unknown environments efficiently will be investigated. More specifically, robustness measures and divergence predictors will be developed for the state estimation in order to provide early warnings of erroneous estimates. Measuring the quality of the different sensors will result in the judicious use of the subset of sensors that provide accurate information. The mapping challenge will be addressed by augmenting the feature-based map with features generated from the lighting variations, such as shadows and caustic patterns. Coverage patterns will be employed in open areas with limited obstacles, while a frontier-based strategy will guide the underwater vehicle to unexplored areas. Returning to mapped areas in a systematic manner will maintain the localization uncertainty below a user defined level. The results will be published in conferences and journals of 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.

水环境的作用非常重要：它在气候中起着关键作用;它包含地球上最高的生物多样性;港口是贸易和运输的最重要基础设施之一;全球多达40%的人口生活在海岸线100公里以内。 提高我们对水下领域的认识至关重要。使用自主机器人收集额外的信息将更安全，更具成本效益，并且可以扩展到比以前的方法更大的规模。该项目的目标是使机器人系统能够在水下环境中自主操作。为了实现这一目标，机器人需要了解环境，以及自己在环境中的位置。机器人需要制定运动策略，以便于有效和准确地估计其位置和环境中障碍物/物体的位置，同时考虑测量中的误差。水下领域提出了几个独特的挑战：没有全球定位系统（GPS）;通信，即使可用，也具有极其有限的带宽;能见度条件，即使在最好的情况下，由于水中的颗粒物阻碍视线而受到限制。调查员将在四个领域推进最新技术水平：来自不同机器人传感器的信息将用于计算机器人在水下区域移动时的位置;然后，调查员和他的研究生将利用所有现有信息制作机器人可用于导航的环境的表示;接下来，将实施规划，以引导机器人通过环境，考虑到较短的距离和具有观察兴趣的区域;最后，该团队将研究有效探索未知环境的新策略。研究人员将利用他在水下领域的研究成果，提高学生和普通民众对科学、技术、工程和数学的兴趣。该项目的目标是使机器人系统能够在水下环境中自主操作。 为了实现这一目标，机器人需要态势感知。此外，机器人需要开发运动策略，以便于有效和准确地估计其姿态和环境中感兴趣点的位置，同时考虑不确定性的积累和外力的影响，如风或电流。水下区域使基于卫星的全球定位系统失效。通信，当可用时，具有极其有限的带宽;和能见度条件是有限的，由于雾和模糊，照明变化随着时间的推移，和颜色损失。调查人员将在四个领域推进最新技术水平：来自不同传感器的信息将用于计算机器人在水下区域移动时的姿态;所有可用信息将用于生成环境的密集表示;接下来，在考虑效率的情况下，将实施一个决策过程来引导机器人通过环境（较短的距离）和具有观看兴趣的区域;最后，将研究有效探索和覆盖未知环境的新策略。更具体地说，鲁棒性措施和发散预测器将开发的状态估计，以提供错误的估计的早期预警。测量不同传感器的质量将导致明智地使用提供准确信息的传感器子集。映射的挑战将通过增加基于特征的地图与照明变化，如阴影和焦散图案产生的功能来解决。覆盖模式将在障碍物有限的开阔地区使用，而基于前沿的战略将引导水下航行器到未探索的地区。以系统的方式返回到映射区域将使定位不确定性保持在用户定义的水平以下。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。

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

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

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

Caveline Detection at the Edge for Autonomous Underwater Cave Exploration and Mapping

• DOI: 10.1109/icmla58977.2023.00210
• 发表时间: 2023-12
• 期刊: 2023 International Conference on Machine Learning and Applications (ICMLA)
• 作者: Mohammadreza Mohammadi;Sheng-En Huang;T. Barua;Ioannis Rekleitis;Md Jahidul Islam;Ramtin Zand

SVIn2: A multi-sensor fusion-based underwater SLAM system

• DOI: 10.1177/02783649221110259
• 发表时间: 2022-07-13
• 期刊: INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
• 影响因子: 9.2
• 作者: Rahman, Sharmin;Quattrini Li, Alberto;Rekleitis, Ioannis
• 通讯作者: Rekleitis, Ioannis