FW-HTF-R/Collaborative Research: Human-Robot Sensory Transfer for Worker Productivity, Training, and Quality of Life in Remote Undersea Inspection and Construction Tasks

这项“人类技术前沿的未来工作：核心研究”项目将为海底机器人的远程操作创建一个新颖的界面，并根据海上工业和工人的需求进行定制。新界面集成了机器人传感器读数和水动力的高速预测模拟，以创建沉浸式混合现实（MR）显示。除了机器人周围环境的增强视频图像外，该界面还将水流量、静水压力、环境温度和其他变量的测量值转换为操作员的触觉。同样，界面将操作员身体的自然运动呈现为机器人的控制命令。其目标是人类与机器人之间的“感觉传递”，即操作员和机器人之间的感知和动作的无缝转换。该项目的目标是开发和匹配接口的能力，以满足行业和工人的需求。一个预期的好处是减少目前对操作员的广泛培训，从而增加获得这些工作的机会，同时减少行业培训费用和由于人员短缺造成的停机时间。该项目将研究提高工人绩效、安全和生活质量的最有效方法，并通过要求不同的主题，展示这种人机界面如何将经济机会扩大到社会的各个领域。该界面也可以在纯虚拟模式下用作培训工具。该项目将研究如何利用这一能力从建筑等邻近领域招聘工人。将直接受益于该项目的近海应用包括海底基础设施检查、地质调查、海洋生境监测、污染评估、船体检查、未爆弹药调查、违禁品检测、水产养殖监测、搜索和救援以及考古勘探和调查。极端天气的增加和海平面的上升将对海上作业提出越来越高的要求，以保护和修复沿海破坏。同样，海上可持续能源基础设施，如风力、波浪或潮汐发电机，将增加对海底检查、施工和维护的需求。该项目将通过在未开发的海底工作场所推进水下人机交互（HRI）知识，阐明劳动力转型到海底行业的社会经济特征和成人学习需求，建立海底-工业-政府合作伙伴关系，以提高海底工程的性能、安全性和社会效益。新的人-机器人传感器传输方法，建议对水下独特的条件下的可靠性。这些方法将支持水下工作场所的快速准确重建。MR将用于根据新型机器人传感和数据传输系统的反馈，真实的实时生成远程海底工作场所的人类可感知模拟。将创建运动捕捉，以便于远程操作车辆（ROV）的导航。这项研究将通过工业合作伙伴的广泛参与，建立关于引入易于使用的协作ROV的激励和教育决定因素的新知识，作为未来海底机器人操作的变革性劳动力的一部分。评估将整合心理测量和行为科学以及工程和人为因素的技术。这项工作还将开创未来海底工作框架的发展，将ROV集成到核心海底服务的参与式交付中。采用机器人的经济效益将根据需求预测和弹性估计来估计。这项研究将改变未来海底工业背景下人与技术合作的前沿，重新定位受到其他领域自动化威胁的劳动力，提高未来工人的安全和福祉，并提高海底作业绩效，从而增强了长期的该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。

This Future of Work at the Human-Technology Frontier: Core Research project will create a novel interface for remote operation of undersea robots and customize it to the needs of offshore industries and workers. The novel interface integrates robot sensor readings and high-speed predictive simulations of hydrodynamic forces to create an immersive mixed reality (MR) display. In addition to augmented video images of the robot's surroundings, the interface converts measurements of water flow rates, hydrostatic pressure, ambient temperature, and other variables into tactile sensations for the operator. Likewise, the interface will render natural movements of the operator's body into control commands to the robot. The goal is human-robot "sensory transfer," that is, seamless translation of perceptions and actions between the operator and the robot. The goal of this project is to develop and match the capabilities of the interface to industry and worker needs. One anticipated benefit is to reduce the extensive training currently required for operators, thereby increasing access to these jobs while reducing industry training expenses and downtime due to personnel shortages. The project will study the most effective way to improve worker performance, safety, and quality of life, and by requiring a diverse set of subjects, will show how such human-robot interfaces can expand economic opportunity to broad sections of society. The interface can also be used in a purely virtual mode as a training tool. The project will examine the use of this capability to recruit workers from adjacent fields, such as construction. Offshore applications that would directly benefit from this project include subsea infrastructure inspection, geological surveys, marine habitat monitoring, pollution assessments, ship-hull inspections, unexploded ordnance surveys, contraband detection, aquaculture monitoring, search and rescue, and archaeological exploration and surveys. An increase in extreme weather and rising sea levels will place increasing demands on offshore operations to protect and repair coastal damage. Similarly offshore sustainable energy infrastructure such as wind, wave, or tidal generators will increase the demand for undersea inspection, construction, and maintenance.This project will reconceptualize future subsea industry by advancing knowledge of underwater Human-Robot Interaction (HRI) in under-explored subsea workplaces, illuminating socioeconomic features and adult-learning needs of workforce transformation to subsea industry, and establishing academia-industry-government partnerships for improving performance, safety, and societal outcomes of subsea works. Novel human-robot sensory transfer methods are suggested for reliability against conditions unique to subsea. These methods will support fast and accurate reconstruction of subsea workplaces. MR will be used to generate human-perceivable simulation of remote subsea workplaces in real time based on feedback from a novel robotic sensing and data transmission system. Motion capture will be created for easier navigation of remotely operated vehicles (ROVs). This research will establish new knowledge on motivational and educational determinants of introducing easy-to-use collaborative ROVs as part of a transformative workforce for future subsea robot operations, through extensive participation from industrial partners. The assessment will integrate techniques from psychometric and behavioral sciences as well as engineering and human factors. The work will also pioneer the development of a future subsea job framework for integration of ROVs into a participatory delivery of core subsea services. The economic benefits of robotic adoption will be estimated based on demand projection and elasticity estimation. This research will transform the frontiers of human-technology partnership in the context of the future subsea industry, reposition workforce threatened by automation in other domains, enhance future workers’ safety and well-being, and improve subsea operation performance, thus enhancing the long-term sustainable ocean exploration.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.