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.

翻译：在搬运、焊接与装配等工业应用中，人机协作流程的日益部署催生了对能够管理大规模异构团队并同时监控复杂任务执行过程的系统需求。本文提出一种适用于任意规模混合人机团队的新型架构，用于实现动态角色分配与协作任务规划。该架构基于行为树（BTs）构建集中式反应式模块化任务无关规划方法，负责动作调度；同时通过混合整数线性规划（MILP）公式化分配问题，动态为团队中各主体分配个体角色或协作关系。通过采用不同指标作为MILP代价函数，该架构能够优化协作的多种维度（如完工时间、人体工学性、人类偏好）。人类偏好通过协商阶段识别，在该阶段中人类主体可接受/拒绝执行分配的任务。此外，通过增强现实（AR）定制用户界面实现人类与系统的双向通信，该界面提供直观功能以在不同动作阶段辅助和协调工人。在工业规模作业与团队场景下（包含协作关系且动作数达50、主体数达20的问题可在1秒内求解），所提方法的计算复杂度优于文献现有方法。随代价函数变化的差异化角色分配结果凸显了该架构适应多种生产需求的灵活性。最后，主观评估证明了其高可用性水平及对目标场景的适用性。