Toward AI-Augmented Cooperative Engineering Workflows: Requirements and Architecture the European Rover Challenge

2026-09-25

AI Summary

This study addresses the insufficient process-level AI support in collaborative engineering and the bottlenecks in team coordination, knowledge transfer, and system integration encountered in the European Mars rover project. We propose an AI-enhanced collaborative engineering architecture that facilitates a paradigm shift from isolated tasks to process-level collaboration. Grounded in empirical research within the European Rover Challenge (ERC), this work integrates questionnaire surveys, requirements engineering, microservice architectures, and natural language processing to construct a hybrid human-machine collaboration framework encompassing task clarification, compliance management, and risk detection. By precisely identifying critical workflow bottlenecks and establishing design directions for AI-assisted systems, the proposed architecture significantly enhances integration efficiency and knowledge management in complex systems engineering.

Abstract

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.

Problem

• cooperative engineering workflows
• AI-augmented systems
• subsystem integration
• workflow bottlenecks
• human-AI teams

Innovation

• AI-Augmented Workflows
• Cooperative Engineering
• System Architecture
• Human-AI Teaming
• Requirements Engineering