Automated emergency response systems have been the focus for development of more reliable and robust safety systems, from simpler ones to the most complex. Such systems have specific requirements, such as high reliability, real-time response, and performance. For drones, they can be designed to allow compliance standards, track safe places for landing and provide an easier development for operational process. The automated response for increasing drone safety focuses on the system health for detecting failures that can lead to vehicle accidents. Given this outlook, this paper presents the SafeEYE project, which was initiated to develop and commercialise an automated emergency landing system for larger (> 7 kg) drones. The system consists of a small embedded computer, mounted on a drone, that keeps track of safe places to land, or even crash, as well as the health state of the drone. When there is a failure condition, the device can terminate the flight with the least probability of fatalities. This means a significantly reduced risk for automated, typically Beyond Visual Line of Sight, operations. Therefore, SafeEYE has the potential to become a safety enabler for many applications, including farming, inspection, transportation, search and rescue. With the risk mitigation ability, the project aims at achieving formal approval of the Danish authorities and abroad. SafeEYE is planned to be manufactured as a standalone unit, provided first through drone technology suppliers and later to service providers and manufacturers of autopilots.

Vibration analysis is a vital measurement tool to provide detailed examination of drone health status by examining signal levels and frequencies. As drones are progressively operating in susceptible airspace where their being might cause harm, signal processing of in-flight data is becoming a necessity to reduce drone risks in sensitive conditions. On that account, this paper investigates how vibration measurements from different flights can be analysed to infer the condition of elements inside the drone. The results should assist safety operators to ascertain whether vibration anomalies can be an indicator of diagnostic and troubleshooting tools of major fault progress in drone flights. In order to track and monitorize the anomalies on the flying drone, this research proposes a vibration spectrum analysis on the inputs from on-board vibration monitoring sensors. The reason for using this analysis is that it can conduct the anomaly detection by providing critical frequency information pinpointing the faulty conditions on the drone platform. The results provides support for the proposed framework, with the ability to determine increasing defect from an unsteady flight with high payload but those results being preliminary to further research. This suggests that further drone safety research can use the same signal processing themes regarding vibration related anomalies when operating in sensitive flight zones.