  Multi-human multi-robot (MH-MR) systems have the ability to combine the
potential advantages of robotic systems with those of having humans in the
loop. Robotic systems contribute precision performance and long operation on
repetitive tasks without tiring, while humans in the loop improve situational
awareness and enhance decision-making abilities. A system's ability to adapt
allocated workload to changing conditions and the performance of each
individual (human and robot) during the mission is vital to maintaining overall
system performance. Previous works from literature including market-based and
optimization approaches have attempted to address the task/workload allocation
problem with focus on maximizing the system output without regarding individual
agent conditions, lacking in real-time processing and have mostly focused
exclusively on multi-robot systems. Given the variety of possible combination
of teams (autonomous robots and human-operated robots: any number of human
operators operating any number of robots at a time) and the operational scale
of MH-MR systems, development of a generalized framework of workload allocation
has been a particularly challenging task. In this paper, we present such a
framework for independent homogeneous missions, capable of adaptively
allocating the system workload in relation to health conditions and work
performances of human-operated and autonomous robots in real-time. The
framework consists of removable modular function blocks ensuring its
applicability to different MH-MR scenarios. A new workload transition function
block ensures smooth transition without the workload change having adverse
effects on individual agents. The effectiveness and scalability of the system's
workload adaptability is validated by experiments applying the proposed
framework in a MH-MR patrolling scenario with changing human and robot
condition, and failing robots.

Multiagent Systems;Robotics