Schiphol Airport aims to achieve fully autonomous airside operations by 2050, driven by the goals of reducing human presence in high-risk environments and enhancing operational efficiency. Within this vision, drones present a promising innovation for airside tasks, offering autonomous inspection capabilities and adaptability to dynamic conditions. National developments, such as the Dutch Ministry of Infrastructure and Water Management's push for drone integration, reinforce this ambition. Schiphol's participation in the Dutch Drone Delta consortium highlights its commitment to exploring drone use cases in operational settings.

To explore drone integration at Schiphol, Runway Condition Reporting (RCR) was selected as a case study due to its repetitive, linear nature and critical safety function. RCR involves collecting runway surface data using a MARWIS sensor system. Currently performed manually by airside vehicles, the study proposes an autonomous drone solution flying between hubs at either end of the Polderbaan runway to perform RCR.

This research addressed autonomous drone-based RCR through three dimensions: reliability, effectiveness, and safety, leading to the central question:

"How can drones be utilized reliably, effectively and safely within Schiphol Airport's operational environment to automate runway condition reporting?"

The research followed a staged case study approach validated through stakeholder input. It began by assessing technical feasibility and system constraints, followed by designing a flight trajectory and operational protocol. A safety risk assessment was then conducted using a HAZOP analysis, and a visual simulation model was developed to communicate system insights.

Reliability was evaluated through performance assessment of commercial drones against Schiphol-specific criteria. A weighted decision-making process, supported by expert interviews, identified the MRTK drone system as suitable. However, wind gusts above 12 m/s—common in afternoons—would limit annual operability to approximately 64%, making wind resistance a key constraint.

Effectiveness was assessed by designing a geofenced, constrained flight path performing RCR in 3 minutes and 41 seconds. The protocol included emergency procedures such as Return-to-Home and Emergency Landing. An agent-based simulation integrated operational data, showing 27 drone inspections per day totaling 1 hour and 45 minutes of flight time. Results suggest potential efficiency improvements, but highlight airspace regulation bottlenecks during simultaneous drone operations.

Safety was assessed via HAZOP with six key stakeholders. The analysis identified 51 potential system deviations, four of which posed high risks: compromised airspace due to wildlife, loss of drone control, emergency protocol failure, and ground collisions. These risks could render runways unusable or halt airside operations. Root causes include wind gusts, system failures, and external interference. Mitigation measures such as stricter geofencing, redundancies, and improved coordination were identified, but require further research.

Despite the risks, this case study provides Schiphol with a reference framework for exploring autonomous drone innovation. It recommends starting with low-risk, non-runway use cases and engaging with regulators and stakeholders to build experience. Scientifically, the study offers a replicable framework combining technical analysis, stakeholder input, and risk assessment for early-stage exploration of autonomous systems.

In conclusion, while drones can perform RCR under certain conditions, safety challenges currently prevent implementation. However, this research provides a structured foundation for advancing autonomous drone integration at Schiphol and similar environments.