This article describes the aerial platform developed in the framework of the OMICRON project for the improvement and optimisation of road maintenance operations. The aerial platform performs road pavement quality inspection in long-range drone flight scenarios. A customized multi-purpose drone adapted to the payloads used, a high-resolution camera to obtain the digital model of the road with photogrammetry and small, lightweight cameras to Detect And Avoid (DAA) possible threats in the environment while flying in Beyond Visual Line Of Sight (BVLOS) operations have been developed. For photogrammetry, a Sony full-frame camera controlled (triggering and metadata management) with a Raspberry Pi is used. DAA processing is carried out onboard on an Nvidia Jetson Orin NX, using Artificial Intelligence (AI) and transmitting real-time video to the Ground Control Station (GCS).

This paper presents a comprehensive software architecture that facilitates the testing and deployment of autonomous missions in the context of Unmanned Aerial Vehicle (UAV) simulation. Our approach integrates and enhances several tools, including the Robot Operating System (ROS), to create a robust environment for evaluating autonomous UAV operations and gathering data. The core of our simulation framework leverages Unreal Engine and the AirSim plugin to model UAV dynamics and generate sensor data. Various modifications have been implemented to enhance sensor fidelity and enable realistic movement of diverse world objects, achieving a higher degree of realism. Designed for high configurability, the proposed architecture allows for flexible adaptation to a wide range of scenarios and requirements. Thus, this simulation environment supports rigorous testing and development of autonomous UAV systems, presenting a valuable platform for researchers and developers in the field of aerial robotics.

For proper navigation of Unmanned Aerial Vehicles (UAVs), it is necessary to know their position in real-time to ensure safe navigation. Determining position in outdoor spaces is quite well solved. On the other hand, in indoor spaces, existing solutions are either imprecise or excessively costly. In this paper, the 3D localization problem is addressed in the context of UAV navigation. The main purpose of this work is to develop and evaluate a robust real-time localization scheme using exclusively the information from an embedded Event Camera and an IMU (Inertial Measurement Unit). Deep learning techniques and robust computer vision algorithms are implemented together to accurately compute the UAV pose, leveraging the strengths of well-established visual-inertial odometry algorithms and the intrinsic advantages of Event Cameras, such as high dynamic range and absence of motion blur. Throughout this study, state-of-the-art techniques are selected, refined, implemented, and evaluated. The proposed system demonstrated good performance and acceptable precision specially in situation with abrupt lighting changes.

This paper introduces a hybrid aerial robot for Non-Destructive Testing (NDT) thickness petrochemical pipes inspection and a novel method for recognizing and landing on pipes in areas where Global Navigation Satellite System (GNSS) signals are degraded. In these environments, the inspection of pipes at height presents a high risk for the workers. We have addressed the issue of landing safely on pipes by implementing tilted rotors and a force control technique. Due to the type of environment, a LiDAR-Inertial Odometry has been implemented for the aircraft localization. In addition, pipes are detected and tracked using a fusion of some of the onboard sensors: a depth camera and a 2D LiDAR. The outcome is an unmanned aerial vehicle with the capability of deploying a robotic crawler on pipes at height while performing safe landing and takeoff. A demonstration of autonomous landing in an outdoor controlled environment can be found at https://youtu.be/yYRzDUkc_Bk.

The introduction of autonomous aerial robots in everyday applications has motivated the emergence of multiple competitions, which propose unique challenges to the research community. At the same time, robotic competitions are excellent opportunities to engage engineering students and improve their skills, and also end users to adopt the newest technologies. However, the high effort that teams must devote prevents the broad participation of the research community. Thus, this has motivated the arrival of dataset-based competitions in which teams do not need to integrate and operate an actual aerial robotic system. Nevertheless, succeeding in these offline challenges does not ensure that real robots will work as expected, hence limiting the impact of the developments. We propose a comprehensive strategy to maximize team participation by recording and providing real datasets in the same environment where the competitions take place. The publicly available datasets can be accessed at https://github.com/fada-catec/rami_dataset.

The SciRoc project, started in 2018, is an EU-H2020 funded project supporting the European Robotics League (ERL) and builds on the success of the EU-FP7/H2020 projects RoCKIn, euRathlon, EuRoC and ROCKEU2. The ERL is a framework for robot competitions currently consisting of three challenges: ERL Consumer, ERL Professional and ERL Emergency. These three challenge scenarios are set up in urban environments and converge every two years under one major tournament: the ERL Smart Cities Challenge. Smart cities are a new urban innovation paradigm promoting the use of advanced technologies to improve citizens’ quality of life. A key novelty of the SciRoc project is the ERL Smart Cities Challenge, which aims to show how robots will integrate into the cities of the future as physical agents. The SciRoc Project ran two such ERL Smart Cities Challenges, the first in Milton Keynes, UK (2019) and the second in Bologna, Italy (2021). In this chapter we evaluate the three challenges of the ERL, explain why the SciRoc project introduced a fourth challenge to bring robot benchmarking to Smart Cities and outline the process in conducting a Smart City event under the ERL umbrella. These innovations may pave the way for easier robotic benchmarking in the future.

The incorporation of uncrewed aerial vehicles (UAV) in urban environments is expanding with countless applications currently under development or even in prototype phase. The industry is facing many challenges, not only technological but also at the regulatory level, as flying in urban environments poses a significant challenge at the bureaucratic and regulatory levels. In this paper, we contribute to mitigating both challenges, showing, on the one hand, the process of obtaining permits to perform flights in a city and the publication of the data obtained with the sensors onboard the UAV for the benefit of the community. Also, to improve operations’ safety in urban environments, we propose an obstacle detection algorithm with the data obtained.

The use of aerial robots for the inspection of Oil&Gas production plants has considerably evolved in recent years. This article presents an aerial hybrid robotic system with the ability to autonomously land on the pipe to be inspected, so it can deploy a satellite crawler robot for accurate inspections. In order to ensure safe autonomous navigation in unknown environments, the aircraft is equipped with a redundant localization system. A sensor fusion of multiple pose estimation sources is performed to estimate the aircraft pose in a robust and efficient manner. Regarding the detection of the pipe to land on, its position is estimated by using the point cloud of an on-board camera. This paper presents both the localization and the pipe detection modules. Finally, successful experimental results carried out in an indoor environment are shown, being all computations performed on board the aerial platform. A video of the results including a fully autonomous mission can be accessed at https://youtu.be/N3ZGVuDy1qA.

The growing market in Remotely Piloted Aircraft Systems (RPAS) and the need for cost-effective “Detect and Avoid (DAA)” systems are critical issues up to date towards enabling safe beyond visual line of sight (BVLOS) operations. In hopes of promoting earlier threat detection on DAA systems, we benchmark several object detection algorithms on multiple graphical processing units for the concrete DAA use case. Two state-of-the-art “real-time object detection” and “object detection” model sets are trained using our CENTINELA dataset, and their performances are compared for a wide range of configurations. Results demonstrate that one-stage architecture YOLO variants outperform ViT on all tested hardware in terms of mean average precision and inference speed despite their architecture complexity gap. Additional resources are available to the reader at https://github.com/fada-catec/detection-for-safe-rpas-operation .

Bridge inspections have a large variety of procedures to ensure the safety of its facilities and personnel, and at the same time tightly budget constraints. These procedures involve extensive inspections, most of which should be performed at height and using both cameras and other sensors that require to be in contact with the surfaces being inspected. Then, bridge inspections traditionally require access to specific inspection points using man-lifts, cranes, scaffolds, or rope-access techniques, which increments importantly the costs of these inspections. This work will present a system formed by two drones that will perform complete inspection operations in bridges in less time, reducing costs, improving quality of the inspection, and increasing safety of operators. The first one is an aerial robot that can obtain pictures of the overall bridge fully autonomously thanks to a GPS-free navigation system. The second one is the AeroX drone platform, a novel solution for inspection of difficult access areas. The AeroX can perform contact inspection due to its robotic contact device, which is equipped with an end-effector. Finally, both drones will be presented with videos of the validation experiments.

Although ground robots have been successfully used for many years in manufacturing, the capability of aerial robots to agilely navigate in the often sparse and static upper part of factories makes them suitable for performing tasks of interest in many industrial sectors. This paper presents the design, development, and validation of a fully autonomous aerial robotic system for manufacturing industries. It includes modules for accurate pose estimation without using a Global Navigation Satellite System (GNSS), autonomous navigation, radio-based localization, and obstacle avoidance, among others, providing a fully onboard solution capable of autonomously performing complex tasks in dynamic indoor environments in which all necessary sensors, electronics, and processing are on the robot. It was developed to fulfill two use cases relevant in many industries: light object logistics and missing tool search. The presented robotic system, functionalities, and use cases have been extensively validated with Technology Readiness Level 7 (TRL-7) in the Centro Bahia de C ' adiz (CBC) Airbus D&S factory in fully working conditions.

The widespread availability of Unmanned Aerial Vehicles (UAVs) poses potential threats for people and properties on the ground, and other airspace users. This work introduces the design, development and validation of a UAV neutralization system that is based on another UAV with a capture device. The operation is fully autonomous, and only relies on data captured by two cameras onboard the captor UAV: one for long-range detections up to 40m, and another one for short-range accurate estimations prior to the actual capture. The approach has been extensively validated in field experiments, proving robustness and computational efficiency.

The relevance of unmanned aerial robots in industrial applications has increased with the prevalence of such systems in recent years. Moreover, their use in indoor environments, or where GNSS signals are degraded, is growing. This manuscript presents a solution for robust localization of aerial robots without the need for GNSS signals. In order to truly use them for added-value cases in such scenarios, high levels of robustness are required. Our proposed method is based on a probabilistic approach that makes use of a 3D LiDAR, UWB sensors and a previously built map of environment, to obtain aerial robot pose estimations. Experimental results show the feasibility of the approach, both in accuracy and computational efficiency, being all computations carried out onboard the aerial platform. A video of the results can be accessed at https://youtu.be/Dn6LxH-WLRA.

The inspection of public infrastructure, such as viaducts and bridges, is crucial for their proper maintenance given the heavy use of many of them. Current inspection techniques are very costly and manual, requiring highly qualified personnel and involving many risks. This article presents a novel solution for the detailed inspection of viaducts using aerial robotic platforms. The system provides a highly automated visual inspection platform that does not rely on GPS and could even fly underneath the infrastructure. Unlike commercially available solutions, our system automatically references the inspection to a global coordinate system usable throughout the lifespan of the infrastructure. In addition, the system includes another aerial platform with a robotic arm to make contact inspections of detected defects, thus providing information that cannot be obtained only with images. Both aerial robotic platforms feature flexibility in the choice of camera or contact measurement sensors as the situation requires. The system was validated by performing inspection flights on real viaducts.

Perimeter detection systems detect intruders penetrating protected areas, but modern solutions require the combination of smart detectors, information networks and controlling software to reduce false alarms and extend detection range. The current solutions available to secure a perimeter (infrared and motion sensors, fiber optics, cameras, radar, among others) have several problems, such as sensitivity to weather conditions or the high failure alarm rate that forces the need for human supervision. The system exposed in this paper overcomes these problems by combining a perimeter security system based on CEMF (control of electromagnetic fields) sensing technology, a set of video cameras that remain powered off except when an event has been detected. An autonomous drone is also informed where the event has been initially detected. Then, it flies through computer vision to follow the intruder for as long as they remain within the perimeter. This paper covers a detailed view of how all three components cooperate in harmony to protect a perimeter effectively, without having to worry about false alarms, blinding due to weather conditions, clearance areas, or privacy issues. The system also provides extra information of where the intruder is or has been, at all times, no matter whether they have become mixed up with more people or not during the attack.

The use of unmanned aerial robots has increased exponentially in recent years, and the relevance of industrial applications in environments with degraded satellite signals is rising. This article presents a solution for the 3D localization of aerial robots in such environments. In order to truly use these versatile platforms for added-value cases in these scenarios, a high level of reliability is required. Hence, the proposed solution is based on a probabilistic approach that makes use of a 3D laser scanner, radio sensors, a previously built map of the environment and input odometry, to obtain pose estimations that are computed onboard the aerial platform. Experimental results show the feasibility of the approach in terms of accuracy, robustness and computational efficiency.

For the Remotely Piloted Aircraft Systems (RPAS) market to continue its current growth rate, cost-effective 'Detect and Avoid' systems that enable safe beyond visual line of sight (BVLOS) operations are critical. We propose an audio-based 'Detect and Avoid' system, composed of microphones and an embedded computer, which performs real-time inferences using a sound event detection (SED) deep learning model. Two state-of-the-art SED models, YAMNet and VGGish, are fine-tuned using our dataset of aircraft sounds and their performances are compared for a wide range of configurations. YAMNet, whose MobileNet architecture is designed for embedded applications, outperformed VGGish both in terms of aircraft detection and computational performance. YAMNet's optimal configuration, with >70% true positive rate and precision, results from combining data augmentation and undersampling with the highest available inference frequency (i.e., 10 Hz). While our proposed 'Detect and Avoid' system already allows the detection of small aircraft from sound in real time, additional testing using multiple aircraft types is required. Finally, a larger training dataset, sensor fusion, or remote computations on cloud-based services could further improve system performance.