Abstract
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.
Highlights
M ULTI-HUMAN MULTI-ROBOT (MH-MR) systems have an immense potential for applicability in various independent and non-sequential tasks such as coverage problems of surveillance, patrolling, search and rescue, inspection or assembly of items in an industrial conveyor belt by robotic manipulators, and various other multi-agent scenarios
We present a generalized MH-MR framework capable of workload allocation for independent, nonsequential homogeneous tasks, consisting of independent modular function blocks assessing human and robot conditions and the performances of human-operated and autonomous robots
As validation of the effectiveness of the adaptive task allocation mechanism, we present our experimental findings of applying the proposed framework to a MH-MR patrolling application, where human operator and robot conditions affect their patrolling ability
Summary
M ULTI-HUMAN MULTI-ROBOT (MH-MR) systems have an immense potential for applicability in various independent and non-sequential tasks such as coverage problems of surveillance, patrolling, search and rescue, inspection or assembly of items in an industrial conveyor belt by robotic manipulators, and various other multi-agent scenarios. Robots allow long operation hours on repetitive tasks and provide consistent and precise performance beyond human capability, while human operators contribute improved situational awareness, experienced and intuitive decision making, and the ability to work around unexpected situations. Task/workload allocation is an important problem in MHMR systems. Previous works have investigated team organization [4], a number of operator-mediated robot control methods [5], awareness studies in human-robot systems [6], and various classifications of human-robot systems [7] for task/workload allocation.
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