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

A Unified Architecture for Dynamic Role Allocation and Collaborative Task Planning in Mixed Human-Robot Teams