NRI: FND: A Framework for Human-Team-Supervised Autonomy with Application to Underwater Search and Rescue

NRI：FND：应用于水下搜索和救援的人类团队监督自主框架

• 批准号: 1734272
• 负责人: Vaibhav Srivastava
• 金额: $ 75万
• 依托单位: Michigan State University
• 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: 2017-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734272&HistoricalAwards=false
• 关键词: NRI; FND; Framework; Human; Team

Advances in computing and manufacturing have led to rapid developments in autonomous robots. For sophisticated tasks such as search and rescue, it is often critical to integrate human knowledge and perception skills with the capabilities offered by robots. Taking underwater search and rescue as a motivating context, this project focuses on developing a principled design framework for optimizing the performance of a mixed human-robot team comprised of multiple human operators and heterogeneous robots. By enabling efficient and reliable human-robot interactions, this work will facilitate the use of robots in hazard response, environmental monitoring, mobility of goods and humans, healthcare, manufacturing, and many other applications of societal impact. The project will provide training opportunities for graduate and undergrad students, including those from underrepresented groups. It will also provide research training to high school students and K-12 teachers. An open-source robotic fish educational kit and demos of EEG-mediated human-robot interactions will be developed to pique the interest of K-12 students in science and engineering. The project will further produce an underwater robotics testbed available for use by the broader robotics and control community.This research will develop a generalizable framework for rigorous and systematic design of autonomy supervised by a team of interacting human operators, which will enable the leveraging of human operators' adaptivity in complex scenarios while mitigating performance deterioration due to loss of situational awareness. The framework will consist of two tightly coupled modules. The first module will involve optimal task allocation and scheduling for event-triggered human team supervision, which will be formulated as a semi-Markov decision process (SMDP) for a complex queueing network capturing task processing by a team of human operators with different skill sets. Human cognitive dynamics will be incorporated via practical models, and efficient algorithms for solving the SMDP are examined while uncertainties introduced by stochasticity in cognitive processes and variability among human operators are accommodated. The second module of the framework will deal with informative path planning for autonomous robots that optimally balances the explore-exploit trade-off in their search for targets of interest, by solving a multi-armed bandit problem that incorporates mobility constraints of the robots. The framework will be experimentally evaluated in field trials emulating underwater search and rescue, which will involve a group of gliding robotic fish and remotely operated vehicles (ROVs), supervised by a team of two human operators.

计算机和制造业的进步导致了自主机器人的快速发展。对于像搜索和救援这样复杂的任务，将人类的知识和感知技能与机器人提供的能力结合起来通常是至关重要的。以水下搜索和救援为激励背景，该项目侧重于开发一个原则性设计框架，以优化由多名人类操作员和异构机器人组成的混合人机团队的性能。通过实现高效可靠的人机交互，这项工作将促进机器人在灾害响应、环境监测、货物和人员的移动性、医疗保健、制造业和许多其他具有社会影响的应用中的使用。该项目将为研究生和本科生提供培训机会，包括来自代表性不足群体的学生。它还将为高中生和K-12教师提供研究培训。一个开源机器鱼教育工具包和脑电图介导的人机交互演示将被开发出来，以激起K-12学生对科学和工程的兴趣。该项目将进一步生产一个水下机器人试验台，供更广泛的机器人和控制社区使用。本研究将开发一个可推广的框架，用于由相互作用的人类操作员团队监督的严格和系统的自主设计，这将使人类操作员能够在复杂场景中利用适应性，同时减轻由于丧失态势感知而导致的性能下降。该框架将由两个紧密耦合的模块组成。第一个模块将涉及事件触发的人类团队监督的最佳任务分配和调度，这将被制定为一个半马尔可夫决策过程（SMDP），用于复杂的排队网络，捕获由不同技能的人类操作员团队处理的任务。人类的认知动力学将通过实际模型结合，并有效的算法来解决SMDP，同时在认知过程中的随机性和人类操作者之间的可变性引入不确定性。该框架的第二个模块将处理自主机器人的信息路径规划，通过解决包含机器人移动性约束的多臂强盗问题，在搜索感兴趣的目标时最佳地平衡探索-利用权衡。该框架将在模拟水下搜索和救援的现场试验中进行实验评估，该试验将涉及一组滑动机器鱼和远程操作车辆（rov），由两名操作员组成的团队进行监督。

项目成果

On Distributed Multi-Player Multiarmed Bandit Problems in Abruptly Changing Environment

突变环境下的分布式多人多臂强盗问题

• DOI: 10.1109/cdc.2018.8619603
• 发表时间: 2018
• 期刊: IEEE Conference on Decision and Control
• 作者: Wei, Lai;Srivastava, Vaibhav
• 通讯作者: Srivastava, Vaibhav

Multi-Armed Bandits for Human-Machine Decision Making

• DOI: 10.1109/icassp.2018.8461843
• 发表时间: 2018-04
• 期刊: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Paul B. Reverdy;Vaibhav Srivastava

Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes

通过多保真高斯过程加速多目标搜索并保证性能

• DOI: 10.1109/iros45743.2020.9341395
• 发表时间: 2020
• 期刊: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
• 作者: Wei, Lai;Tan, Xiaobo;Srivastava, Vaibhav
• 通讯作者: Srivastava, Vaibhav

Sensitivity-based data fusion for optical localization of a mobile robot

用于移动机器人光学定位的基于灵敏度的数据融合

• DOI: 10.1016/j.mechatronics.2021.102488
• 发表时间: 2021
• 期刊: Mechatronics
• 影响因子: 3.3
• 作者: Greenberg, Jason N.;Tan, Xiaobo
• 通讯作者: Tan, Xiaobo

Optical localization of a mobile robot using sensitivity-based data fusion

使用基于灵敏度的数据融合进行移动机器人的光学定位

• 发表时间: 2019
• 期刊: Proceedings of 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics
• 作者: Greenberg, Jason;Tan, Xiaobo
• 通讯作者: Tan, Xiaobo