“真实场景下”多机器人系统的信任与接受度：以欧盟项目BugWright2为例的路线图
Trust and Acceptance of Multi-Robot Systems "in the Wild". A Roadmap exemplified within the EU-Project BugWright2
AI总结：
本文基于欧盟BugWright2项目经验，提出通过构建更新共享心智模型、配套相关策略与合理UI来校准多机器人系统信任度的路线图，聚焦船舶检测维护场景。
摘要：
本文借鉴BugWright2项目的经验，勾勒出在多机器人、多利益相关方场景中有效利用共享心智模型的路线图。讨论围绕一款专为船舶检测与维护设计的自主多机器人系统展开。该系统开发与落地过程中的一大核心挑战是信任校准问题。为解决这一问题，本文提出可通过构建并持续更新机器人的共享、准确心智模型，来管理并优化信任校准。文中探讨了推广这类心智模型的相关策略，包括交叉训练、任务简报、任务复盘以及针对特定任务的阐释与可视化。此外，还讨论了适配性强、分布式且结构合理的用户界面（UI）的关键作用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
