Unmanned Aircraft Systems (UASs), together with the miniaturisation of computers, sensors, and electronics, offer new remote sensing applications. However, there is a lack of hardware and software support to effectively develop the potential of UASs in different remote sensing applications, such as the detection of radioactive sources. This paper presents the design, development and validation of a UAS for the detection of an uncontrolled and point radioactive source. The article describes a flexible and reusable software architecture for detecting the radioactive source (NaTcO ${}_4$ , containing ${}^{99m}$ Tc) with a gamma-ray Cadmium Zinc Telluride (CZT) spectrometer as a proof of concept. The UAS is equipped with multichannel air-ground communications to perform missions beyond line of sight and onboard computation to process samples in real time and thus react to any anomaly detected during the mission. An ad hoc ground control station (GCS) has also been developed for the correct interpretation of the radioactive samples taken by the UAS. Radiological spectra plots, contour mapping and waterfall plots are some of the elements used in the ad hoc GCS. The article shows the results obtained in a flight campaign performing different flights at different altitudes and speeds over the radiological source, demonstrating the viability of the system.

Police tasks related with law enforcement and citizen protection have gained a very useful asset in drones. Crowded demonstrations, large sporting events, or summer festivals are typical situations when aerial surveillance is necessary. The eyes in the sky are moving from the use of manned helicopters to drones due to costs, environmental impact, and discretion, resulting in local, regional, and national police forces possessing specific units equipped with drones. In this paper, we describe an artificial intelligence solution developed for the Castelldefels local police (Barcelona, Spain) to enhance the capabilities of drones used for the surveillance of large events. In particular, we propose a novel methodology for the efficient integration of deep learning algorithms in drone avionics. This integration improves the capabilities of the drone for tasks related with capacity control. These tasks have been very relevant during the pandemic and beyond. Controlling the number of persons in an open area is crucial when the expected crowd might exceed the capacity of the area and put humans in danger. The new methodology proposes an efficient and accurate execution of deep learning algorithms, which are usually highly demanding for computation resources. Results show that the state-of-the-art artificial intelligence models are too slow when utilised in the drone standard equipment. These models lose accuracy when images are taken at altitudes above 30 m. With our new methodology, these two drawbacks can be overcome and results with good accuracy ($96\%$ correct segmentation and between $20\%$ and $35\%$ mean average proportional error) can be obtained in less than 20 s.

This paper presents two novel avionics subsystems that aim at overcoming two clearly identified drawbacks of current UAV technology. Firstly, on board exploitation of autopilot telemetry is a complex and autopilot dependent task. Secondly, the flight plan definition mechanism available in most autopilots is just a collection of waypoints. This approach has several limitations, among them its inability to allow interaction between the flight plan and the mission in progress. The Flight Control System Gateway is a component designed to facilitate exploitation of data obtained from the autopilot. It provides a hardware-independent interface that isolates payload components from the autopilot specificities, thus eliminating dependencies on a particular autopilot solution. The Flight Plan Manager is a subsystem that interacts with the FCS Gateway in order to direct the flight of the UAV. It follows a flight plan described via a novel specification mechanism and sends commands to the FCS Gateway for its execution. The specification formalism improves on current mechanisms by introducing higher level constructs and enabling interaction with other payload components. These subsystems are integrated into an overall avionics solution which is based on an innovative publish/subscribe service based software architecture and a LAN based distributed hardware architecture.