A Mixed-Integer Linear Programming Formulation for Human Multi-Robot Task Allocation
This addresses task allocation for human-robot collaboration in assembly or similar domains, but it is incremental as it applies an existing MILP method to a new context with human factors.
The paper tackles the problem of task allocation in human multi-robot settings by formulating a Mixed-Integer Linear Programming (MILP) approach to minimize execution time and optimize task quality and workload, validated through simulations in an assembly scenario with two manipulators and a human operator.
In this work, we address a task allocation problem for human multi-robot settings. Given a set of tasks to perform, we formulate a general Mixed-Integer Linear Programming (MILP) problem aiming at minimizing the overall execution time while optimizing the quality of the executed tasks as well as human and robotic workload. Different skills of the agents, both human and robotic, are taken into account and human operators are enabled to either directly execute tasks or play supervisory roles; moreover, multiple manipulators can tightly collaborate if required to carry out a task. Finally, as realistic in human contexts, human parameters are assumed to vary over time, e.g., due to increasing human level of fatigue. Therefore, online monitoring is required and re-allocation is performed if needed. Simulations in a realistic scenario with two manipulators and a human operator performing an assembly task validate the effectiveness of the approach.