LAUSR

In human-robot collaboration systems, the human-robot team works together to complete all tasks of the assigned job. Tasks’ precedence order is a factor that determines which task should be completed first. A human agent has an expected performance on each task, but the actual performance of the human agent on a task may differ from the expected performance at different times. Therefore, it is also necessary to monitor the human agent so that the task can be transferred to or from the robot when the actual performance of the human agent is worse or better than the expected performance. We propose an architecture for dynamic task allocation and scheduling in a collaborative system. The method is to first identify the group of tasks that should be executed first based on the precedence order of tasks. After obtaining this group of prioritized tasks, we use the quality metric (a numerical value that indicates which agent is better at completing the task) to determine which agent should be assigned each of the tasks. Finally, real-time human agent monitoring is used to detect performance changes in the human agent so that tasks can be transferred from one agent to another. All of these processes are performed automatically without the need for a human agent to schedule or reschedule. We have validated the effectiveness of the architecture through several experiments.