Automated Emergency Landing System for Drones: SafeEYE Project

Abstract

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 for Anomaly Detection in Unmanned Aircraft

“…Such a region can be regarded as a set of values of the vibration analysis for which the null hypothesis is not rejected. Depending on the acceptance region, if there is a significant number of critical values, the flight can be terminated and even an automated emergency landing can be performed as described in Bektash et al (2020). However, the "Z" axis of gyro at the vector highlights that even a normal operation such like the one in Flight 4 may surpass the threshold value or alternatively the abnormal flight cannot be distinguished by the available information.…”

Analyzing visual imagery for emergency drone landing on unknown environments

“…The first layer of a CNN, also known as the input layer, consist of artificial input neurons that bring the initial imagery data into the network structure for the subsequent layers. These initial data were available from a database of 10,000's of images captured from the SafeEYE lab that has been mounted, integrated, and flown in a number of test flights on a DJI Matrice 600 drone 18 (see Figure 2). There were a number of test flights that had been conducted with the SafeEYE, primarily to collect data and to test the machine vision approach for the detecting areas of interest for landing or a crash without any human injury or fatality.…”

“…17 For image recognition and visual recognition for autonomous drone landing, the unmanned systems in smaller sizes can only tolerate a small computer mounted on the vehicle which is also expected to track the health state and guidance for landing. 18,19 When there is a safetycritical condition, it is expected from this computer to determine the best course of action for the aircraft to minimize the probability of fatalities. This means a significant challenge for automated location recognition framework.…”

“…• To find good landing spots that can facilitate an emergency landing or even a crash without casualties. 18 ), which are monitored from various test flights where the primary focus was to provide images of a variety of terrains.…”

“…To further understanding, this research has the following objectives: To find good landing spots that can facilitate an emergency landing or even a crash without casualties. To analyze a class of input image taken during an ongoing flight and output a class, or simply a probability of classes, that best describes whether the location is suitable for emergency landing. To develop a conceptual framework of a machine vision approach for detecting areas by outlining theoretical underpinnings and analyzing related research and construction schemes. To test the proposed model empirically by grabbing images from the camera mounted on drone (SafeEYE lab Bektash et al 18 ), which are monitored from various test flights where the primary focus was to provide images of a variety of terrains. …”