NRI: Small: Collaborative Research: Adaptive Motion Planning and Decision-Making for Human-Robot Collaboration in Manufacturing

该项目解决了由于当前算法的限制或高昂的成本和设置时间而无法完全自动化的制造任务。这些任务通常要求工人紧密协作，并适应彼此的决定和动作。该项目探索通过人机协作完成这些任务。机器人技术的最新硬件发展使人机协作成为可能，但机器人仍然需要新的算法来确保与人合作时的安全性，效率和流畅性。创建这样的算法是困难的，因为一个人要做什么，他们要怎么做，可能有很高的不确定性。这个项目探讨了推理的整合，一个人如何移动，他或她如何作出决定，到机器人运动规划和决策框架。该研究集中在开发新的算法框架，用于建模，仿真和规划人机协作，这需要在机器人训练，任务建模，人类运动理解，具有不确定性的高维运动规划以及评估人机联合行动的指标方面取得进展。该项目的结果有可能签署fi这将大大提高美国在制造业的竞争力，特别是小批量制造和批量生产，因为全自动解决方案的成本和设置时间都令人望而却步。这项工作将在研究论文中传播，并纳入课程。该项目由制造业的一个咨询委员会指导，该委员会提供了另一个传播渠道。

This project addresses manufacturing tasks that cannot be fully automated because of either the limitations of current algorithms or prohibitive cost and set-up time. Such tasks generally require workers to collaborate in close proximity and adapt to each other's decisions and motions. This project explores accomplishing these tasks through human-robot collaboration. Recent hardware developments in robotics have made human-robot collaboration physically possible, but robots still require new algorithms to ensure safety, efficiency, and fluency when working with people. Creating such algorithms is difficult because there can be high uncertainty in what a person is going to do and how they are going to do it. This project explores the integration of reasoning about how a person moves and how he or she makes decisions into a robot motion planning and decision-making framework. The research centers on the development of new algorithmic frameworks for modeling, simulating, and planning for human-robot collaboration, which requires advances in robot training, task modeling, human motion understanding, high-dimensional motion planning with uncertainty, and metrics to assess human-robot joint action. The results of this project have the potential to significantly improve American competitiveness in manufacturing; especially for small-batch manufacturing and burst production, where the cost and set-up time of fully-autonomous solutions is prohibitive. The work will be disseminated in research papers and integrated into curricula. The project is guided by an advisory board from the manufacturing industry, which provides another avenue for dissemination.