NRI: INT: COLLAB: Manufacturing USA: Intelligent Human-Robot Collaboration for Smart Factory

PI: Zhaozheng Yin
Co-PI: Ming C. Leu
Award amount $667,656
Award date: 9/15/18 to 8/31/22

This National Robotics Initiative (NRI) collaborative research project addresses the NSF Big Idea of Work at the Human-Technology Frontier by targeting human-robot collaboration in manufacturing. Recent advances in sensing, computational intelligence, and big data analytics have been rapidly transforming and revolutionizing the manufacturing industry towards robot-rich and digitally connected factories. However, effective, efficient and safe coordination between humans and robots on the factory floor has remained a significant challenge. To meet the need for safe and effective human-robot collaboration in manufacturing, the investigators will research an integrated set of algorithms and robotic test beds to sense, understand, predict and control the interaction of human workers and robots in collaborative manufacturing cells. It is expected that these methods will significantly improve the safety and productivity of hybrid human-robot production systems, thereby promoting their deployment in future "smart factories". To broaden the impact of this project, a partnership with Manufacturing USA Institute(s) and professional societies will be established to provide human-robot collaboration learning modules for inclusion in robotics and smart manufacturing-related curricula. These learning modules, together with annual events aimed at community college and pre-college students, and workshops for the dissemination of research results will raise public awareness and attract new entrants into the manufacturing and robotics industries, creating truly synergetic education opportunities in science, technology, engineering and mathematics, as well as accelerating the adoption of smart factory-enabling technologies.

The project will address fundamental challenges in human-robot collaboration in the manufacturing environment, such as the limitation of one-to-one sensing between humans and robots, the lack of adaptive and stochastic modeling methods for reliable recognition and prediction of human actions and motions in different manufacturing scenarios, and multi-scale human-robot coordination. To address these challenges, multi-disciplinary research involving sensing, machine learning, stochastic modeling, robot path planning, and advanced manufacturing will be performed. Specific tasks include algorithm development and deployment on lab-scale and real-world test beds to: (1) sense and recognize where objects (e.g., robots, humans, parts or tools) are located and what each worker is doing; (2) predict what the next human action will be; and (3) plan and control safe and optimal robot trajectories for individualized on-the-job assistance for humans, proactively avoiding worker injury. The outcomes from the project will be evaluated on the shop-floor at the collaborating company COsorizio MAcchine Uensili (COMAU) in Michigan, and the Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing of the National Research Council of Italy.