Towards Robust and Natural Underwater Human-Robot Interaction

实现稳健、自然的水下人机交互

• 批准号: 1845364
• 负责人: Junaed Sattar
• 金额: $ 10.15万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-15 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845364&HistoricalAwards=false
• 关键词: Towards; Robust; Natural; Underwater; Human

The underwater domain takes up almost four-fifths of the planet and is inherently hostile towards human exploration. However, in numerous applications in the marine environment (e.g., in surveillance, environmental monitoring, security, and search-and-rescue), human assessment is necessary for efficient and effective task completion. Current technology for underwater exploration sees limited applications of autonomous underwater vehicles (AUVs) but relies heavily on remotely operated vehicles, which unfortunately does not take advantage of robot autonomy. This project will develop novel algorithms and protocols to enable humans to communicate safely with AUVs while preserving and leveraging their autonomy. Specifically, the intent is to create novel methods for gesture- and motion-based bidirectional human-robot communication methods and enable autonomous underwater robots to detect, identify and interact with specific individuals. The research objectives will be evaluated individually and as an integrated, coherent system onboard underwater vehicles. The proposed research has the potential to create a fundamentally new direction in human-in-the-loop field robotics, with underwater robot companions being able to assist divers in a range of tasks and even learning to carry out these tasks in an autonomous manner, greatly reducing risk to humans. This research will also impact a broad range of disciplines, including human-machine dialog, machine vision, activity recognition, and robot control.This research will develop novel algorithms and protocols to enable humans to communicate safely with AUVs while preserving and leveraging their autonomy. Specific goals include: development of a gesture-based human-to-robot language with multiple communication granularities; creation of algorithms for visual identification of humans by learning from spatial and periodic cues; and development of a non-verbal, motion-based underwater robot-to-human communication scheme. The research objectives will be evaluated individually and as an integrated, coherent system onboard underwater vehicles. The investigation into gesture-based visual languages will quantify the detectability, usability, and efficacy of such methods in realistic settings. Statistical, convolutional and generative learning-based approaches will be applied to understand both identifiable human features and gestural communication. Additionally, robot body language and motion will be used as cues for robot-to-human non-verbal communication. These methods will be quantitatively and qualitatively validated via user studies and robot field trials to characterize their advantages and limitations.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.

水下领域几乎占据了地球五分之四的面积，天生就对人类的探索怀有敌意。然而，在海洋环境中的许多应用中(例如，在监视、环境监测、安全和搜救中)，人类评估对于高效和有效地完成任务是必要的。目前的水下探测技术认为自主水下机器人(AUV)的应用有限，但严重依赖于远程操作的机器人，不幸的是，远程操作的机器人没有利用机器人的自主性。该项目将开发新的算法和协议，使人类能够安全地与AUV进行通信，同时保持和利用它们的自主性。具体地说，其目的是为基于手势和运动的双向人-机器人通信方法创造新的方法，并使自主水下机器人能够检测、识别特定的个人并与其互动。研究目标将单独评估，并作为水下航行器上的综合、连贯系统进行评估。拟议中的研究有可能在人类在环领域机器人领域开创一个全新的方向，水下机器人同伴能够帮助潜水员完成一系列任务，甚至能够学习以自主方式执行这些任务，从而极大地降低人类面临的风险。这项研究还将影响广泛的学科，包括人机对话、机器视觉、活动识别和机器人控制。这项研究将开发新的算法和协议，使人类能够与AUV安全通信，同时保持和利用它们的自主性。具体目标包括：开发具有多种通信粒度的基于手势的人与机器人之间的语言；通过学习空间和周期线索创建用于人类视觉识别的算法；以及开发非语言的、基于运动的水下机器人与人之间的交流方案。研究目标将单独评估，并作为水下航行器上的综合、连贯系统进行评估。对基于手势的视觉语言的研究将量化这些方法在现实环境中的可检测性、可用性和有效性。将应用基于统计、卷积和生成性学习的方法来理解可识别的人类特征和手势交流。此外，机器人的肢体语言和动作将被用作机器人与人类之间非语言交流的线索。这些方法将通过用户研究和机器人现场试验进行定量和定性的验证，以表征它们的优势和局限性。该奖项反映了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。

项目成果

Semantically-Aware Strategies for Stereo-Visual Robotic Obstacle Avoidance

• DOI: 10.1109/icra48506.2021.9561863
• 发表时间: 2021-05
• 期刊: 2021 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Jungseok Hong;Karin de Langis;Cole Wyeth;Christopher Walaszek;Junaed Sattar

Person-following by autonomous robots: A categorical overview

• DOI: 10.1177/0278364919881683
• 发表时间: 2019-10-23
• 期刊: INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
• 影响因子: 9.2
• 作者: Islam, Md Jahidul;Hong, Jungseok;Sattar, Junaed
• 通讯作者: Sattar, Junaed

Design and Experiments with LoCO AUV: A Low Cost Open-Source Autonomous Underwater Vehicle

LoCO AUV 的设计和实验：低成本开源自主水下航行器

• DOI: 10.1109/iros45743.2020.9341007
• 发表时间: 2021
• 期刊: IEEE/RSJ International Conference on Intelligent Robots and Systems
• 作者: Edge, Chelsey;Sakib Enan, Sadman;Fulton, Michael;Hong, Jungseok;Mo, Jiawei;Barthelemy, Kimberly;Bashaw, Hunter;Kallevig, Berik;Knutson, Corey;Orpen, Kevin
• 通讯作者: Orpen, Kevin