Adaptive Human Multi-Robot Systems

 

 Welcome to the Project Website

 

Adaptive Human Multi-Robot Systems Project

This project develops adaptive human multi-robot systems that can flexibly respond to changes in situation and task needs. It develops methods for real-time monitoring and analysis of the cognitive and emotional state of operators, enabling human operators to adapt to robot system changes and robots to adapt to human cognitive and emotional states. By developing adaptive systems to improve the performance of human-robot teams, the project advances our understanding of human multi-robot interactions. The new technologies provided will improve function of human multi-robot teams deployed (for example) in environmental monitoring, nuclear cleanup, disaster response, and defense. The project develops novel concepts and tools for

  • Designing mission-specific human multi-robot teams;
  • Estimating operator cognitive and emotional state from physiological and behavioral signals; and
  • Enabling adaptive control of autonomy, tasks, and resource assignment.

This approach will enhance the fields of multi-robot systems and human-robot interactions and improve performance and versatility of human-robot teams. The project will increase our understanding of how multiple robots can more effectively interact with multiple humans and vice versa, and advance the scale and capability of human multi-robot systems.

The project also advances STEM education and workforce development by involving K-12 students, undergraduate and graduate women, minorities, and underrepresented groups in human-robot interaction and multi-robot systems.

 

This is a NSF funded project (IIS: #1846221) for 5 years for which Dr. Byung-Cheol Min is the PI who is the director of Purdue SMART LAB.

NSF Logo