This paper outlines a roadmap to effectively leverage shared mental models in multi-robot, multi-stakeholder scenarios, drawing on experiences from the BugWright2 project. The discussion centers on an autonomous multi-robot systems designed for ship inspection and maintenance. A significant challenge in the development and implementation of this system is the calibration of trust. To address this, the paper proposes that trust calibration can be managed and optimized through the creation and continual updating of shared and accurate mental models of the robots. Strategies to promote these mental models, including cross-training, briefings, debriefings, and task-specific elaboration and visualization, are examined. Additionally, the crucial role of an adaptable, distributed, and well-structured user interface (UI) is discussed.

翻译：本文基于BugWright2项目的实践经验，提出了在多机器人、多利益相关方场景中有效利用共享心智模型的路线图。讨论聚焦于为船舶检测与维护设计的自主多机器人系统。该系统开发与实施中的一个关键挑战是信任校准。为此，本文提出通过构建并持续更新精确的机器人共享心智模型，可管理和优化信任校准。文章探讨了促进这些心智模型的策略，包括交叉训练、简报、汇报以及针对特定任务的细化与可视化。此外，还讨论了可适应、分布式且结构良好的用户界面（UI）的关键作用。