A Dynamic Architecture for Task Assignment and Scheduling for Collaborative Robotic Cells
This addresses the challenge of unpredictable human behavior in human-robot collaboration, though it appears incremental as it builds on existing task allocation methods with a reactive adaptation layer.
The paper tackles the problem of efficient task allocation and scheduling in collaborative robotic cells where a human and robot work together, proposing a two-layer architecture that optimally allocates tasks and adapts online to dynamic behaviors, with experimental validation on a collaborative assembly job.
In collaborative robotic cells, a human operator and a robot share the workspace in order to execute a common job, consisting of a set of tasks. A proper allocation and scheduling of the tasks for the human and for the robot is crucial for achieving an efficient human-robot collaboration. In order to deal with the dynamic and unpredictable behavior of the human and for allowing the human and the robot to negotiate about the tasks to be executed, a two layers architecture for solving the task allocation and scheduling problem is proposed. The first layer optimally solves the task allocation problem considering nominal execution times. The second layer, which is reactive, adapts online the sequence of tasks to be executed by the robot considering deviations from the nominal behaviors and requests coming from the human and from robot. The proposed architecture is experimentally validated on a collaborative assembly job.