The goal of this award is to develop theories, methods, and tools to understand the mechanisms of neuromotor adaptation in human-robot physical interaction. Human power-assisting systems, e.g., powered lifting devices that aid human operators in manipulating heavy or bulky loads, require physical contact between the operator and machine, creating a coupled dynamic system. This coupled dynamic has been shown to introduce inherent instabilities and performance degradation due to a change in human stiffness; when instability is encountered, a human operator often attempts to control the oscillation by stiffening their arm, which leads to a stiffer system with more instability. The project will establish control algorithms for robot co-workers that proactively adjust the contact impedance between the operator and robotic manipulator for achieving higher performance and stability. This research will 1) understand the association between neuromuscular adaptations and system performance limits, 2) develop probabilistic methods to classify and predict the transition of operator’s cognitive and physical states from physiological measures, and 3) integrate this knowledge into a structure of shared human-robot and demonstrate the efficacy in a powered lifting device with real-world constraints at vehicle assembly facilities.
If successful, the research will benefit the communities interested in the adaptive shared control approach for advanced manufacturing and process design, including automobile, aerospace, and military. Such next-generation manufacturing is expected to improve productivity and reduce assembly time as well as the physical burden of assembly line workers. Research outcomes will be integrated into current courses at both graduate and undergraduate levels.
This work is in collaboration with Dr. Jun Ueda (PI), Dr. Minoru Shinohara, and Dr. Wayne Book