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

计算和制造的进步导致了自主机器人的快速发展。对于搜索和救援这样的复杂任务，将人类的知识和感知技能与机器人提供的能力相结合往往是至关重要的。以水下搜救为激励背景，本项目致力于开发一个原则性的设计框架，以优化由多个人类操作员和不同类型的机器人组成的混合人-机器人团队的性能。通过实现高效可靠的人-机器人交互，这项工作将促进机器人在危险应对、环境监测、货物和人类的流动、医疗保健、制造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。将开发一个开源的机器鱼教育工具包和脑电介导的人与机器人交互的演示，以激发K-12学生对科学和工程的兴趣。该项目将进一步开发一个水下机器人试验台，供更广泛的机器人和控制界使用。这项研究将开发一个通用框架，用于严格和系统地设计自主性，由一组交互的人类操作员监督，这将使操作员能够在复杂场景中利用人类操作员的适应性，同时缓解由于丧失情境意识而导致的性能下降。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优任务分配和调度，这将被描述为一个复杂排队网络的半马尔可夫决策过程(SMDP)，该网络捕捉由具有不同技能集的人类操作员团队进行的任务处理。人类的认知动力学将通过实用的模型结合起来，并研究求解SMDP的有效算法，同时适应认知过程中的随机性和人类操作员之间的可变性带来的不确定性。该框架的第二个模块将处理自主机器人的信息性路径规划，通过解决包含机器人移动性限制的多臂土匪问题，在搜索感兴趣目标的过程中最佳地平衡探索和开发之间的权衡。该框架将在模拟水下搜救的现场试验中进行实验评估，其中将包括一组滑翔的机器鱼和遥控潜水器(ROV)，由一个由两名人类操作员组成的团队监督。

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.