Entry Date:
January 20, 2017

Models and Instruments for Integrating Effective Human-Robot Teams into Manufacturing

Principal Investigator Julie Shah

Project Start Date September 2014

Project End Date
 August 2017

Robots for application in collaborative manufacturing must perform manual work side-by-side with people. Such robots offer the flexibility to work on many different tasks and promise to transform manufacturing by improving the quality and efficiency of manual processes in small shops and in facilitates that manufacture highly customized products. However, in order to meet this promise, robots must be effectively integrated into existing manufacturing teams and practices. To enable this integration, this National Robotics Initiative (NRI) award supports fundamental research on the methods and instruments that manufacturing engineers will need to form effective human-robot teams based on task requirements and worker skills. These methods will also enable robots to adapt to changes in workflow to maximize safety and efficiency. The effective integration of collaborative robots into manufacturing promises improvements in many industries that have not yet benefited from robotic technology. Therefore, results from this research will contribute to the competitiveness of U.S. manufacturing and benefit the U.S. economy and society. The research will involve contributions from multiple disciplines, including robotics, human factors, computer science, and manufacturing, and by academic and industry collaborators. These collaborations will help the dissemination of research results into manufacturing organizations and the integration of research into undergraduate and graduate curriculum in engineering.

Advancements in robotics promise the use of collaborative robots that perform interdependent work with people in order to improve quality, efficiency, and safety in industrial manufacturing. However, integrating collaborative robots into these processes and ensuring their efficient operation pose significant research challenges, including the optimal allocation of work based on task requirements and constraints, the formation of human-robot teams, and the dynamic adaptation of teamwork to workflow changes. This research will address these research challenges, enabling the seamless integration of collaborative robots into these processes and achieving efficient and safe collaboration between human and robot workers. The research team will create novel methods for optimal allocation of tasks to human and robot workers based on task constraints and worker skills, design new tools that utilize these methods to facilitate workflow design for human-robot teams, and develop novel mechanisms that enable robots to more efficiently and safely collaborate with human workers in the planned manufacturing operations. These methods and instruments will be validated in real-world manufacturing operations and disseminated through industry workshops, engineering curricula, and a public outreach program.