迈向AI增强的协作工程工作流：欧洲机器人挑战赛的需求与架构
Toward AI-Augmented Cooperative Engineering Workflows: Requirements and Architecture the European Rover Challenge
- Honda Research Institute Europe（本田欧洲研究院）
- NUMETO（NUMETO公司）
- Honda Research Institute USA（本田美国研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对欧洲机器人挑战赛中协作工程工作流的瓶颈，通过问卷调查提炼需求，提出连接用户界面、凭证管理、服务选择、AI服务与外部工具的助手系统架构，以支持任务澄清、需求管理、沟通摘要、集成风险检测和知识捕获。
AI中文摘要：
人工智能（AI）工具的日益普及为支持工程设计流程创造了新的机遇，然而目前其使用往往局限于编码、文档编制或信息检索等孤立任务。对于AI如何在流程层面支持协作工程工作流，即团队需要协调需求、任务、沟通、知识转移和子系统集成的场景，关注相对较少。本文在欧洲机器人挑战赛（ERC）的背景下探讨了这一挑战，在该赛事中，学生团队需在严格的时限和高度的子系统相互依赖条件下，于单个学术周期内设计和集成复杂的机器人系统。我们针对ERC 2025团队开展了一项角色自适应的40题问卷调查，收到来自14个团队的104份回复。调查涉及团队结构、知识转移、任务管理、集成实践、沟通模式及当前AI使用情况。结果显示存在反复出现的工作流瓶颈，包括文档不足、需求不明确、沟通碎片化、任务监控非正式以及大量的集成返工。基于这些发现，我们推导出AI增强协作工程工作流的需求，并提出一个初步的助手系统架构，该架构连接用户界面、凭证管理、服务选择、专业化AI服务和外部工程工具。所提出的架构旨在支持任务澄清、需求与合规管理、沟通摘要、集成风险检测和持续知识捕获。通过上述工作，本文为混合人机AI团队环境中的AI增强协作工程工作流贡献了实证需求和架构方向。
英文摘要：
The growing availability of Artificial Intelligence (AI) tools creates new opportunities to support engineering design processes, yet their current use often remains limited to isolated tasks such as coding, documentation, or information retrieval. Less attention has been given to how AI can support cooperative engineering workflows at the process level, where teams must coordinate requirements, tasks, communication, knowledge transfer, and subsystem integration. This paper investigates this challenge in the context of the European Rover Challenge (ERC), where student teams design and integrate complex rover systems within a single academic cycle under strict time constraints and high subsystem interdependence. We conducted a role adaptive 40 question survey with ERC 2025 teams, yielding 104 responses from 14 teams. The survey examined team structure, knowledge transfer, task management, integration practices, communication patterns, and current AI usage. The results reveal recurring workflow bottlenecks, including limited documentation, unclear requirements, fragmented communication, informal task monitoring, and substantial integration rework. Based on these findings, we derive requirements for AI augmented cooperative engineering work-flows and propose an initial assistant system architecture that connects user facing interfaces, credential management, service selection, specialized AI services, and external engineering tools. The proposed architecture aims to support task clarification, requirement and compliance management, communication summarization, integration risk detection, and continuous knowledge capture. In doing so, the paper contributes empirical requirements and an architectural direction for AI augmented cooperative engineering workflows in hybrid human AI team settings.
