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 99 m 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.

Unmanned aerial vehicles (UAV) specifically drones have been used for surveillance, shipping and delivery, wildlife monitoring, disaster management etc. The increase on the number of drones in the airspace worldwide will lead necessarily to full autonomous drones. Given the expected huge number of drones, if they were operated by human pilots, the possibility to collide with each other could be too high.In this paper, deep reinforcement learning (DRL) architecture is proposed to make drones behave autonomously inside a suburb neighborhood environment. The environment in the simulator has plenty of obstacles such as trees, cables, parked cars and houses. In addition, there are also another drones, acting as moving obstacles, inside the environment while the other drone has a goal to achieve. In this way the drone can be trained to detect stationary and moving obstacles inside the neighborhood and so the drones can be used safely in a public area in the future. The drone has a front camera and it can capture continuously depth images. Every depth image, with a size of 144x256 pixels, is part of the state used in DRL architecture. Also, another part of the state is the distance to the geo-fence, a virtual barrier on the environment, which is added as a scalar value. The agent will be rewarded negatively when it tries to overpass the geo-fence limits. In addition, angle to goal and elevation angle between the goal and the drone will be used as information to be added to the state. It is considered that these scalar values will improve the DRL performance and also the reward obtained. The drone is trained using Q-Network and its convergence and final reward are evaluated. The states containing image and several scalars are processed by neural network that joints the two state parts into a unique flow. This neural network is named as Joint Neural Network (JNN) [1]. The training and test results show that the agent can successfully avoid any obstacles in the environment. In training, there exist some episodes crashed at the beginning of the training session because the random drones moves randomly in the environment and thus they can hit the learner drone during training. The test results are very promising and the learner drone reaches the destination with a success rate %100 in first two tests and with a success rate %98 in the last test.

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Any emergent technology in history has raised an initial rejection by part of the society. Added to the several problems that the non-mature technology may have, the lack of any previous experience about side effects and the humans psychological fear to the unknown play an important influence in its acceptance. As drones bring up high social and economic expectations due to their capabilities and bussiness applications, the social acceptance is key to the complete development of drone technology's potential. Experts believe that social acceptance is ruled by a balance between beneficial usages and inconvenient issues regarding the technology. This balance in the aeronautical sector is also conditioned by the strict safety policies and regulations of the airspace and the current airspace users. To analyse this balance situation in actual and future environments, regarding drone technology, different use cases will be presented. These use cases have been proposed and analysed by different stakeholders from the U-space community network (UCN), a network of airspace and drone stakeholders who participated in the context of the SESAR CORUS project.The purpose of this paper is to analyse some of these use cases by obtaining responses from different stakeholders point of view using a survey in order to see how economical, safety and political aspects are balanced in each one of the cases. From the survey responses we will perform an analysis by means of three different acceptance indicators, one for each aspect commented.Main results and conclusions point out that the economical indicator is, in general, positive, especially for the low cost payload use cases. In contrast the economic indicator is close to neutral for city transport and airports use cases, which leads to propose some economic promotion action may be needed to make them a reality. For the safety indicator we observe that they are close to negative values as use case complexity increases. Thus we can conclude that some of the proposed missions start affecting the current levels of safety. Finally, the political indicator is mostly neutral, with some positive trends for scenarios related with inspection tasks or done in non-populated areas.