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。对于搜索和救援等复杂任务，将人类知识和感知技能与机器人提供的能力相结合往往至关重要。以水下搜索和救援为激励背景，该项目的重点是开发一个原则性的设计框架，用于优化由多个人类操作员和异构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交互，这项工作将促进机器人在危险响应、环境监测、货物和人类移动、医疗保健、制造业以及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，包括代表性不足群体的学生提供培训机会。它还将为高中学生和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图介导的人机交互演示，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测试平台，供更广泛的机器人和控制社区使用。这项研究将开发一个可推广的框架，用于在一个交互的人类操作员团队的监督下进行严格和系统的自主设计，这将使人类操作员在复杂场景中的适应性得到充分利用，同时减轻由于失去态势感知而导致的性能恶化。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，这将被制定为一个复杂的嵌入式网络捕获任务处理的半马尔可夫决策过程（SMDP）由一个团队的人类操作员具有不同的技能。人类的认知动力学将通过实际的模型，并有效的算法解决SMDP进行检查，而引入的随机性在认知过程和人类操作员之间的变异性的不确定性被容纳。第二个模块的框架将处理信息的路径规划的自主机器人，最佳平衡的探索，利用权衡在他们的搜索目标的兴趣，通过解决多臂强盗问题，结合移动性的限制的机器人。该框架将在模拟水下搜索和救援的现场试验中进行实验性评估，其中将涉及一组滑翔机器鱼和遥控潜水器，由两名操作员组成的小组监督。

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.