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S&AS: FND: Learning-Enabled Autonomous 3D Exploration for Underwater Robots

基本信息

批准号：
1723996
负责人：
Brendan Englot
金额：
$ 35.33万
依托单位：
Stevens Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2022-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1723996&HistoricalAwards=false
关键词：
S&amp FND Learning Enabled Autonomous

项目摘要

There are often high costs and safety risks associated with humans performing work underwater, which is frequently required to inspect the health of our subsea infrastructure and environment. This motivates a need for smart and taskable autonomous robots that can monitor and inspect the subsea environment, as well as explore their surroundings when they do not have access to an accurate prior model. Without precise instructions on where and how to explore, the ideal taskable robot should be able to produce comprehensive, accurate maps of its surroundings, make repeated decisions about where to travel next, and ensure that it avoids collisions in the process of doing so. This project will leverage machine learning techniques to produce and deploy new algorithms with the potential to enhance both the speed and efficiency with which underwater robots explore unknown environments, and to enable further gains in performance as the exploring robots gain more experience.Specifically, this project will introduce machine learning techniques to (1) build more descriptive occupancy maps from the sparse and noisy sonar data that typifies the subsea domain; (2) to save computational effort in the potentially exhaustive evaluation of many candidate sensing actions; and (3) to learn effective behaviors for exploring complex, unstructured environments that truly require three-dimensional spatial reasoning. The real-time, 3D exploration task will be managed in concert with other relevant objectives, such as minimizing localization and map uncertainty, and the time and energy expenditures associated with travel. A related goal is to develop robot systems whose performance improves with experience, dynamically choosing the most effective decision-making tools in its portfolio and self-parameterizing the most appropriate map representations for the task at hand.

人类在水下作业往往存在高昂的成本和安全风险，经常需要检查我们海底基础设施和环境的健康状况。这促使对智能和可执行任务的自主机器人的需求，这些机器人可以监测和检查海底环境，以及在无法获得准确的先前模型时探索周围环境。在没有关于探索地点和方法的精确指令的情况下，理想的可执行任务的机器人应该能够制作出全面、准确的周围地图，反复决定下一步要去哪里，并确保在这样做的过程中避免碰撞。这个项目将利用机器学习技术来产生和部署新的算法，有可能提高水下机器人探索未知环境的速度和效率，并随着探索机器人获得更多经验而进一步提高性能。具体地说，这个项目将引入机器学习技术来(1)从稀疏和噪声的声纳数据中建立更具描述性的占据图，(2)在潜在的穷尽评估许多候选传感动作中节省计算工作量；以及(3)学习有效的行为来探索复杂的、非结构化的环境，真正需要三维空间推理。实时的3D勘探任务将与其他相关目标协调管理，例如将定位和地图不确定性降至最低，以及与旅行相关的时间和精力支出。一个相关的目标是开发其性能随着经验的改善而提高的机器人系统，动态地在其投资组合中选择最有效的决策工具，并为手头的任务自我参数化最合适的地图表示。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Simulation-based lidar super-resolution for ground vehicles

DOI：
10.1016/j.robot.2020.103647
发表时间：
2020-12-01
期刊：
ROBOTICS AND AUTONOMOUS SYSTEMS
影响因子：
4.3
作者：
Shan, Tixiao;Wang, Jinkun;Englot, Brendan
通讯作者：
Englot, Brendan

Virtual Maps for Autonomous Exploration of Cluttered Underwater Environments

用于自主探索杂乱水下环境的虚拟地图

DOI：
10.1109/joe.2022.3153897
发表时间：
2022
期刊：
IEEE Journal of Oceanic Engineering
影响因子：
4.1
作者：
Wang, Jinkun;Chen, Fanfei;Huang, Yewei;McConnell, John;Shan, Tixiao;Englot, Brendan
通讯作者：
Englot, Brendan

Improving obstacle boundary representations in predictive occupancy mapping

改进预测占用映射中的障碍物边界表示

DOI：
10.1016/j.robot.2022.104077
发表时间：
2022
期刊：
Robotics and Autonomous Systems
影响因子：
4.3
作者：
Pearson, Erik;Doherty, Kevin;Englot, Brendan
通讯作者：
Englot, Brendan

DRACo-SLAM: Distributed Robust Acoustic Communication-efficient SLAM for Imaging Sonar Equipped Underwater Robot Teams

DOI：
10.1109/iros47612.2022.9981822
发表时间：
2022-10
期刊：
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
影响因子：
0
作者：
J. McConnell;Yewei Huang;Paul Szenher;Ivana Collado-Gonzalez;Brendan Englot
通讯作者：
J. McConnell;Yewei Huang;Paul Szenher;Ivana Collado-Gonzalez;Brendan Englot

Virtual Maps for Autonomous Exploration with Pose SLAM

使用姿势 SLAM 进行自主探索的虚拟地图

DOI：
10.1109/iros40897.2019.8967853
发表时间：
2019
期刊：
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
影响因子：
0
作者：
Wang, Jinkun;Shan, Tixiao;Englot, Brendan
通讯作者：
Englot, Brendan