LINA’s testing infrastructure enables AI to take-off in unmanned aerial vehicles (UAVs)

The development of autonomous aerial robots capable of safely navigating complex real-world environments without or with little human intervention represents a major milestone in robotics and artificial intelligence (AI). While rapid advances in AI-enabled decision-making, sensing, and control systems are unlocking new capabilities for unmanned aerial vehicles (UAVs), their translation into safe and scalable real-life applications remains a major challenge. In this Perspective, we examine key AI technologies relevant to aerial autonomy and discuss early application scenarios in unmanned aviation and airspace management, with a focus on their assurance-relevant properties. We analyze regulatory obstacles that limit deployment, particularly for AI-enabled and beyond visual line of sight (BVLOS) operations, and highlight why traditional risk assessment and certification approaches are need to be updated to account for adaptive, data-driven systems. Building on this analysis, we argue that testing infrastructure must be understood as a core scientific instrument, enabling systematic evidence generation under realistic and safety-critical conditions, validating autonomous functions, ensuring safety, and building trust among regulators and the public. As a concrete example, we introduce LINA, a scientifically-grounded, integrated experimentation and validation platform in Switzerland designed to support iterative, regulator-aware development of autonomous systems across technology readiness levels. We highlight how LINA function as sandbox for system-level science, regulatory learning, and trust building, thereby enabling the responsible and societally acceptable integration of autonomous aerial systems and strengthening Switzerland's role in advancing aerial robotics research and innovation.

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Subject (DDC)

620 - Ingenieurwissenschaften und Maschinenbau

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ISSN

2296-9144

Language

English

Created during FHNW affiliation

Yes

Strategic action fields FHNW

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Published

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peer-reviewed

Open access category

Gold

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Citation

Bolck, H. A., Vollenweider, J., Merkli, F., Barden, A., Jajcay, M., Trempeck, P., Rafailović, B., Fraefel, R., Lenhart, P. M., Chavarriaga, R., Renold, M., Bogojeska, J., Stadelmann, T., & Guillaume, M. (2026). LINA’s testing infrastructure enables AI to take-off in unmanned aerial vehicles (UAVs). Frontiers in Robotics and AI, 13, 1764248. https://doi.org/10.3389/frobt.2026.1764248

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