An Adaptive Multi-human Multi-robot System Framework based on Individual Human and Robot Condition and Performance
Description: Multi-human multi-robot (MH-MR) systems have the ability to combine the potential advantages of robotic systems and humans in the loop. Robotic systems contribute with precision performance and long operation of repetitive tasks without tire, while humans in the loop enhance decision making abilities following improved situational awareness. The system’s ability to adapt to changing conditions and performance of each individual (humans and robots) during the mission is vital to maintaining overall system performance.
Proposed Adaptive Multi-human multi-robot system framework
Given the variety and operational scale of such a system, development of a generalized framework is pertinent. The research goal is to develop a generalized MH-MR system framework capable of allocating the system workload adaptively to health conditions and work performance of human operated and autonomous robots. The framework consists of removable modular blocks ensuring applicability to different MH-MR scenarios. A new workload transition block ensures smooth transition without adverse affects of the workload change on the individual agents.
Publications:
- Wonse Jo, Shyam Sundar Kannan, Go-Eum Cha, Ahreum Lee, and Byung-Cheol Min, "ROS-based Framework for Monitoring Human and Robot Conditions in a Human-Multi-robot Team" arXiv preprint, arXiv:2006.03784, 2020. (Paper)
- Tamzidul Mina, Shyam Sundar Kannan, Wonse Jo, and Byung-Cheol Min, "Adaptive Workload Allocation for Multi-human Multi-robot Teams for Independent and Homogeneous Tasks", IEEE Access, Vol. 8, pp. 152697-152712, 2020. (Paper, Video)