NRI: Decision-making Foundations for Human-supervised Legged Robot Teams

在许多需要高度重复、繁琐的工作或对人类健康构成威胁的应用中，移动机器人正在成为一种强大的工具。轮式机器人在医院、仓库和杂货店等结构化环境中的许多应用都是足够的。然而，许多其他应用需要机器人穿越具有挑战性的地形。腿式机器人将更适合用于杂乱空间的消毒、杀虫剂喷洒和建筑工地检查等应用。它们可以绕过和越过障碍物，爬楼梯和斜坡，或者穿越泥泞的地形来执行功能任务。在许多关键任务中，人类和机器人合作正在成为一个有吸引力的选择。机器人可以让人类远离物理危险，人类可以做出高层决策，以确保机器人根据任务的需要采取正确的行动。因此，这项研究项目将研究决策基础，使一组腿部机器人能够在现实世界的应用程序中与人类主管一起安全有效地工作。该项目还将把研究成果整合到机器人和控制课程中。外展活动将教育和告知K-12学生在机器人领域的职业机会。腿部机器人在执行关键任务的同时在具有挑战性的地形上航行预计将遇到许多挑战。地形图不太可能提前完全知晓。此外，工作环境中可能存在移动的障碍。机器人将不得不更新地图，并在移动的障碍物周围导航，作为任务执行的一部分。车载传感器可能会遇到遮挡，并产生大量的不确定性。因此，机器人将需要考虑风险评估中的不确定性。安全也是一个重要的考虑因素。有腿的机器人将需要确保它们可以安全行走。然而，执行效率将是在现实世界应用中部署腿式机器人的基本考虑因素。为了实现任务执行效率，机器人将需要在移动的同时执行功能性任务。该项目建议开发(1)考虑风险和任务执行效率的人工监督机器人团队任务建模和任务分配的方法，(2)通过考虑未来的应急情况和解决这些情况所需的行动来开发具有风险意识的深思熟虑的运动规划算法，以及(3)多目标安全关键控制器，以降低风险，确保移动和任务执行的稳定性和安全性。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。

Mobile robots are emerging as a powerful tool in many applications that require highly repetitive, tedious work or pose a threat to human health. Wheeled robots are adequate for many applications in structured environments such as hospitals, warehouses, and grocery stores. However, many other applications require robots to traverse challenging terrain. Legged robots will be more suitable for applications such as disinfection of cluttered spaces, pesticide spray, and inspection of construction sites. They can navigate around and over obstacles, climb stairs and slopes, or traverse muddy terrains to perform functional tasks. In many critical missions, human-robot teaming is emerging as an attractive option. Robots can keep humans away from physical dangers, and humans can make high-level decisions to ensure robots are taking the right actions based on the mission's needs. Therefore, this research project will study decision-making foundations that will enable a team of legged robots to work safely and effectively with human supervisors on real-world applications. The project will also integrate research results into robotics and control courses. Outreach activities will educate and inform K-12 students about career opportunities in robotics.Legged robots navigating challenging terrains while performing critical tasks are expected to encounter many challenges. Terrain maps are not likely to be completely known in advance. Moreover, there might be moving obstacles in the work environment. Robots will have to update maps and navigate around moving obstacles as a part of the task execution. On-board sensors are likely to experience occlusion and produce a significant amount of uncertainty. Therefore, robots will need to account for the uncertainty in risk assessment. Safety is also a crucial consideration. Legged robots will need to ensure that they can walk safely. However, efficiency in execution will be an essential consideration for deploying legged robots in real-world applications. To achieve task execution efficiency, robots will need to carry out functional tasks while moving. This project proposes to develop (1) methodologies for human-supervised robot team mission modeling and task assignment by considering risk and mission execution efficiency, (2) develop algorithms for risk-aware deliberative motion planning by considering future contingency situations and actions needed to resolve them, and (3) multi-objective safety-critical controllers for mitigating risk, ensuring stability and safety for both locomotion and task execution.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.