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过高的成本和安装时间而不能完全自动化的制造任务。这类任务通常要求员工密切合作，并适应彼此的决定和行动。这个项目探索通过人类-机器人协作来完成这些任务。机器人学最近的硬件发展使人与机器人的物理协作成为可能，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以及他们将如何做可能会有很高的不确定性。这个项目探索将关于人如何移动以及他或她如何做出决策的推理集成到机器人运动规划和决策框架中。研究的重点是开发用于人-机器人协作的建模、仿真和规划的新的算法框架，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-机器人联合动作的度量方面取得进展。该项目的结果有可能显著提高美国制造业的竞争力，特别是在小批量制造和突发性生产方面，因为在这些领域，完全自主的解决方案的成本和启动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种传播途径。

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.