🤖 AI Summary
This study addresses critical compliance gaps of autonomous robotic systems under the EU AI Act (Regulation 2024/1689), specifically concerning deficiencies in explainability, absence of real-time human oversight, and non-auditable knowledge bases within cybersecurity frameworks. Following the PRISMA protocol, we systematically reviewed 243 studies from IEEE Xplore, ACM Digital Library, Scopus, and Web of Science, ultimately selecting 22 for in-depth analysis. Quantitative evaluation revealed that only 40% of existing approaches satisfy transparency requirements, and merely 30% incorporate robust fault intervention mechanisms. To bridge these gaps, we propose a modular compliance framework integrating dynamic risk management, real-time human-in-the-loop supervision, and continuous auditability. Crucially, transparency and fault intervention are systematically embedded into the architectural design—not treated as post-hoc add-ons. The framework provides both a methodological foundation and an engineering pathway for implementing the EU AI Act in high-autonomy operational contexts.
📝 Abstract
This systematic literature review analyzes the current state of compliance with Regulation (EU) 2024/1689 in autonomous robotic systems, focusing on cybersecurity frameworks and methodologies. Using the PRISMA protocol, 22 studies were selected from 243 initial records across IEEE Xplore, ACM DL, Scopus, and Web of Science. Findings reveal partial regulatory alignment: while progress has been made in risk management and encrypted communications, significant gaps persist in explainability modules, real-time human oversight, and knowledge base traceability. Only 40% of reviewed solutions explicitly address transparency requirements, and 30% implement failure intervention mechanisms. The study concludes that modular approaches integrating risk, supervision, and continuous auditing are essential to meet the AI Act mandates in autonomous robotics.