  The growing deployment of human-robot collaborative processes in several
industrial applications, such as handling, welding, and assembly, unfolds the
pursuit of systems which are able to manage large heterogeneous teams and, at
the same time, monitor the execution of complex tasks. In this paper, we
present a novel architecture for dynamic role allocation and collaborative task
planning in a mixed human-robot team of arbitrary size. The architecture
capitalizes on a centralized reactive and modular task-agnostic planning method
based on Behavior Trees (BTs), in charge of actions scheduling, while the
allocation problem is formulated through a Mixed-Integer Linear Program (MILP),
that assigns dynamically individual roles or collaborations to the agents of
the team. Different metrics used as MILP cost allow the architecture to favor
various aspects of the collaboration (e.g. makespan, ergonomics, human
preferences). Human preference are identified through a negotiation phase, in
which, an human agent can accept/refuse to execute the assigned task.In
addition, bilateral communication between humans and the system is achieved
through an Augmented Reality (AR) custom user interface that provides intuitive
functionalities to assist and coordinate workers in different action phases.
The computational complexity of the proposed methodology outperforms literature
approaches in industrial sized jobs and teams (problems up to 50 actions and 20
agents in the team with collaborations are solved within 1 s). The different
allocated roles, as the cost functions change, highlights the flexibility of
the architecture to several production requirements. Finally, the subjective
evaluation demonstrating the high usability level and the suitability for the
targeted scenario.
