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The seemingly simple step of molding a cholesteric liquid crystal into spherical shape, yielding aCholesteric Spherical Reflector(CSR), has profound optical consequences that open a range of opportunities for potentially transformative technologies. The chiral Bragg diffraction resulting from the helical self-assembly of cholesterics becomes omnidirectional in CSRs. This turns them into selective retroreflectors that are exceptionally easy to distinguish—regardless of background—by simple and low-cost machine vision, while at the same time they can be made largely imperceptible to human vision. This allows them to be distributed in human-populated environments, laid out in the form of QR-code-like markers that help robots and Augmented Reality (AR) devices to operate reliably, and to identify items in their surroundings. At the scale of individual CSRs, unpredictable features within each marker turn them into Physical Unclonable Functions (PUFs), of great value for secure authentication. Via the machines reading them, CSR markers can thus act as trustworthy yet unobtrusive links between the physical world (buildings, vehicles, packaging,…) and its digital twin computer representation. This opens opportunities to address pressing challenges in logistics and supply chain management, recycling and the circular economy, sustainable construction of the built environment, and many other fields of individual, societal and commercial importance.

Aerial manipulators (AM) exhibit particularly challenging, non-linear dynamics; the UAV and the manipulator it is carrying form a tightly coupled dynamic system, mutually impacting each other. The mathematical model describing these dynamics forms the core of many solutions in non-linear control and deep reinforcement learning. Traditionally, the formulation of the dynamics involves Euler angle parametrization in the Lagrangian framework or quaternion parametrization in the Newton-Euler framework. The former has the disadvantage of giving birth to singularities and the latter of being algorithmically complex. This work presents a hybrid solution, combining the benefits of both, namely a quaternion approach leveraging the Lagrangian framework, connecting the singularity-free parameterization with the algorithmic simplicity of the Lagrangian approach. We do so by offering detailed insights into the kinematic modeling process and the formulation of the dynamics of a general aerial manipulator. The obtained dynamics model is validated experimentally against a real-time physics engine. A practical application of the obtained dynamics model is shown in the context of a computed torque feedback controller (feedback linearization), where we analyze its real-time capability with increasingly complex models., Comment: 26 pages, 12 figures, published in the Journal of Intelligent and Robotic Systems (JINT/Springer); corrected affiliations and typos

In this work, we present an optimization-based trajectory tracking solution for multirotor aerial robots given a geometrically feasible path. A trajectory planner generates a minimum-time kinematically and dynamically feasible trajectory that includes not only standard restrictions such as continuity and limits on the trajectory, constraints in the waypoints, and maximum distance between the planned trajectory and the given path, but also restrictions in the actuators of the aerial robot based on its dynamic model, guaranteeing that the planned trajectory is achievable. Our novel compact multi-phase trajectory definition, as a set of two different kinds of polynomials, provides a higher semantic encoding of the trajectory, which allows calculating an optimal solution but following a predefined simple profile. A Model Predictive Controller ensures that the planned trajectory is tracked by the aerial robot with the smallest deviation. Its novel formulation takes as inputs all the magnitudes of the planned trajectory (i.e. position and heading, velocity, and acceleration) to generate the control commands, demonstrating through in-lab real flights an improvement of the tracking performance when compared with a controller that only uses the planned position and heading. To support our optimization-based solution, we discuss the most commonly used representations of orientations, as well as both the difference as well as the scalar error between two rotations, in both tridimensional and bidimensional spaces $SO(3)$ and $SO(2)$. We demonstrate that quaternions and error-quaternions have some advantages when compared to other formulations.

Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for optimal robot navigation with safety guarantees. Previous work on motion planning has followed two main strategies to provide a safe bound on an obstacle's space: a polyhedron, such as a cuboid, or a nonlinear differentiable surface, such as an ellipsoid. The former approach relies on disjunctive programming, which has a relatively high computational cost that grows exponentially with the number of obstacles. The latter approach needs to be linearized locally to find a tractable evaluation of the chance constraints, which dramatically reduces the remaining free space and leads to over-conservative trajectories or even unfeasibility. In this work, we present a hybrid approach that eludes the pitfalls of both strategies while maintaining the original safety guarantees. The key idea consists in obtaining a safe differentiable approximation for the disjunctive chance constraints bounding the obstacles. The resulting nonlinear optimization problem is free of chance constraint linearization and disjunctive programming, and therefore, it can be efficiently solved to meet fast real-time requirements with multiple obstacles. We validate our approach through mathematical proof, simulation and real experiments with an aerial robot using nonlinear model predictive control to avoid pedestrians., Comment: This paper has been accepted for publication in the IEEE Robotics and Automation Letters. Please cite the paper as: M. Castillo-Lopez, P. Ludivig, S. A. Sajadi-Alamdari, J. L. Sanchez-Lopez, M. A. Olivares-Mendez, H. Voos, "A Real-Time Approach for Chance-Constrained Motion Planning with Dynamic Obstacles", IEEE Robotics and Automation Letters (RA-L), 2020

Unsupervised learning for monocular camera motion and 3D scene understanding has gained popularity over traditional methods, which rely on epipolar geometry or non-linear optimization. Notably, deep learning can overcome many issues of monocular vision, such as perceptual aliasing, low-textured areas, scale drift, and degenerate motions. In addition, concerning supervised learning, we can fully leverage video stream data without the need for depth or motion labels. However, in this work, we note that rotational motion can limit the accuracy of the unsupervised pose networks more than the translational component. Therefore, we present RAUM-VO, an approach based on a model-free epipolar constraint for frame-to-frame motion estimation (F2F) to adjust the rotation during training and online inference. To this end, we match 2D keypoints between consecutive frames using pre-trained deep networks, Superpoint and Superglue, while training a network for depth and pose estimation using an unsupervised training protocol. Then, we adjust the predicted rotation with the motion estimated by F2F using the 2D matches and initializing the solver with the pose network prediction. Ultimately, RAUM-VO shows a considerable accuracy improvement compared to other unsupervised pose networks on the KITTI dataset, while reducing the complexity of other hybrid or traditional approaches and achieving comparable state-of-the-art results.

Mobile robots should be aware of their situation, comprising the deep understanding of their surrounding environment along with the estimation of its own state, to successfully make intelligent decisions and execute tasks autonomously in real environments. 3D scene graphs are an emerging field of research that propose to represent the environment in a joint model comprising geometric, semantic and relational/topological dimensions. Although 3D scene graphs have already been combined with SLAM techniques to provide robots with situational understanding, further research is still required to effectively deploy them on-board mobile robots. To this end, we present in this paper a novel, real-time, online built Situational Graph (S-Graph), which combines in a single optimizable graph, the representation of the environment with the aforementioned three dimensions, together with the robot pose. Our method utilizes odometry readings and planar surfaces extracted from 3D LiDAR scans, to construct and optimize in real-time a three layered S-Graph that includes (1) a robot tracking layer where the robot poses are registered, (2) a metric-semantic layer with features such as planar walls and (3) our novel topological layer constraining the planar walls using higher-level features such as corridors and rooms. Our proposal does not only demonstrate state-of-the-art results for pose estimation of the robot, but also contributes with a metric-semantic-topological model of the environment, 8 pages, 6 figures, RAL/IROS 2022

This work introduces TRIGGER, the first lighTweight univeRsal jammInG Gripper for aErial gRasping. TRIGGER is an omnidirectional, landing-capable aerial grasping system with resilience and robustness to collisions and inherent passive compliance. In particular, this work presents the design, fabrication, and experimental validation of a novel, intelligent, modular, universal jamming gripper specifically designed for aerial applications. Leveraging recent developments in particle jamming and soft granular materials, TRIGGER produces 15N of holding force with only a relatively small activation force of 2.5N. Experiments show the relationship between fill ratio and activation force and reveal that adding an additive to the membrane's silicone mixture improves the holding force by up to 52%. The concept is validated by mounting TRIGGER onto a multicopter performing a pick-and-release task under laboratory conditions. Based on the experimental data, a model for robotic simulators is introduced to facilitate future controller developments., Comment: 23 pages, 20 figures; changed title

In this work, we present a semantic situation awareness system for multirotor aerial robots equipped with a 2D LIDAR sensor, focusing on the understanding of the environment, provided to have a drift-free precise localization of the robot (e.g. given by GNSS/INS or motion capture system). Our algorithm generates in real-time a semantic map of the objects of the environment as a list of ellipses represented by their radii, and their pose and velocity, both in world coordinates. Two different Convolutional Neural Network (CNN) architectures are proposed and trained using an artificially generated dataset and a custom loss function, to detect ellipses in a segmented (i.e. with one single object) LIDAR measurement. In cascade, a specifically designed indirect-EKF estimates the ellipses based semantic map in world coordinates, as well as their velocity. We have quantitative and qualitatively evaluated the performance of our proposed situation awareness system. Two sets of Software-In-The-Loop simulations using CoppeliaSim with one and multiple static and moving cylindrical objects are used to evaluate the accuracy and performance of our algorithm. In addition, we have demonstrated the robustness of our proposed algorithm when handling real environments thanks to real laboratory experiments with non-cylindrical static (i.e. a barrel) objects and moving persons.

To achieve fully autonomous operation for Unmanned Aerial Systems (UAS) it is necessary to integrate multiple and heterogeneous technical solutions (e.g., control-based methods, computer vision methods, automated planning, coordination algorithms, etc.). The combination of such methods in an operational system is a technical challenge that requires efficient architectural solutions. In a robotic engineering context, where productivity is important, it is also important to minimize the effort for the development of new systems. As a response to these needs, this paper presents Aerostack, an open-source software framework for the development of aerial robotic systems. This framework facilitates the creation of UAS by providing a set of reusable components specialized in functional tasks of aerial robotics (trajectory planning, self localization, etc.) together with an integration method in a multi-layered cognitive architecture based on five layers: reactive, executive, deliberative, reflective and social. Compared to other software frameworks for UAS, Aerostack can provide higher degrees of autonomy and it is more versatile to be applied to different types of hardware (aerial platforms and sensors) and different types of missions (e.g. multi robot swarm systems). Aerostack has been validated during four years (since February 2013) by its successful use on many research projects, international competitions and public exhibitions. As a representative example of system development, this paper also presents how Aerostack was used to develop a system for a (fictional) fully autonomous indoors search and rescue mission.

The ability of the robots to imitate human movements has been an active research study since the dawn of the robotics. Obtaining a realistic imitation is essential in terms of perceived quality in human-robot interaction, but it is still a challenge due to the lack of effective mapping between human movements and the degrees of freedom of robotics systems. If high-level programming interfaces, software and simulation tools simplified robot programming, there is still a strong gap between robot control and natural user interfaces. In this paper, a system to reproduce on a robot the head movements of a user in the field of view of a consumer camera is presented. The system recognizes the presence of a user and its head pose in real-time by using a deep neural network, in order to extract head position angles and to command the robot head movements consequently, obtaining a realistic imitation. At the same time, the system represents a natural user interface to control the Aldebaran NAO and Pepper humanoid robots with the head movements, with applications in human-robot interaction.

Deliberative capabilities are essential for intelligent aerial robotic applications in modern life such as package delivery and surveillance. This paper presents a real-time 3D path planning solution for multirotor aerial robots to obtain a feasible, optimal and collision-free path in complex dynamic environments. High-level geometric primitives are employed to compactly represent the situation, which includes self-situation of the robot and situation of the obstacles in the environment. A probabilistic graph is utilized to sample the admissible space without taking into account the existing obstacles. Whenever a planning query is received, the generated probabilistic graph is then explored by an A⋆ discrete search algorithm with an artificial field map as cost function in order to obtain a raw optimal collision-free path, which is subsequently shortened. Realistic simulations in V-REP simulator have been created to validate the proposed path planning solution, integrating it into a fully autonomous multirotor aerial robotic system., This work was supported by the “Fonds National de la Recherche” (FNR), Luxembourg, under the project C15/15/10484117 (BEST-RPAS).

In this work, we present a semantic situation awareness system for multirotor aerial robots, based on 2D LIDAR measurements, targeting the understanding of the environment and assuming to have a precise robot localization as an input of our algorithm. Our proposed situation awareness system calculates a semantic map of the objects of the environment as a list of circles represented by their radius, and the position and the velocity of their center in world coordinates. Our proposed algorithm includes three main parts. First, the LIDAR measurements are preprocessed and an object segmentation clusters the candidate objects present in the environment. Secondly, a Convolutional Neural Network (CNN) that has been designed and trained using an artificially generated dataset, computes the radius and the position of the center of individual circles in sensor coordinates. Finally, an indirect-EKF provides the estimate of the semantic map in world coordinates, including the velocity of the center of the circles in world coordinates.We have quantitative and qualitative evaluated the performance of our proposed situation awareness system by means of Software-In-The-Loop simulations using VRep with one and multiple static and moving cylindrical objects in the scene, obtaining results that support our proposed algorithm. In addition, we have demonstrated that our proposed algorithm is capable of handling real environments thanks to real laboratory experiments with non-cylindrical static (i.e. a barrel) and moving (i.e. a person) objects.

Planning feasible trajectories given desired collision-free paths is an essential capability of multirotor aerial robots that enables the trajectory tracking task, in contrast to path following. This paper presents a trajectory planner for multirotor aerial robots carefully designed considering the requirements of real applications such as aerial inspection or package delivery, unlike other research works that focus on aggressive maneuvering. Our planned trajectory is formed by a set of polynomials of two kinds, acceleration/deceleration and constant velocity. The trajectory planning is carried out by means of an optimization that minimizes the trajectory tracking time, applying some typical constraints as m-continuity or limits on velocity, acceleration and jerk, but also the maximum distance between the trajectory and the given path. Our trajectory planner has been tested in real flights with a big and heavy aerial platform such the one that would be used in a real operation. Our experiments demonstrate that the proposed trajectory planner is suitable for real applications and it is positively influencing the controller for the trajectory tracking task.

El objetivo de este trabajo es la validacion de una metodologia para la inspeccion termica de edificios en zonas urbanas mediante vehiculos aereos no tripulados e imagenes en el espectro visible e infrarrojo termico. Las envolventes de los edificios presentan retos para la teledeteccion, dada la diversidad y complejidad de las mismas y sus materiales, asi como su disposicion vertical y dificil accesibilidad. Este estudio valida un metodo para la inspeccion rapida de envolventes, apoyado en tecnicas geoespaciales. Se analizan los principales tipos de patologias que se presentan en fachadas urbanas. Como caso de estudio se seleccionaron dos edificios de uso publico de la Universidad Politecnica de Madrid para la validacion de la metodologia. Se concluye que esta tecnologia esta preparada para su uso en inspeccion de edificios, siendo muy util en el diseno de proyectos de rehabilitacion de edificios altos y complejos en el marco de la eficiencia energetica.

Lateral flow assay tests are nowadays becoming powerful, low-cost diagnostic tools. Obtaining a result is usually subject to visual interpretation of colored areas on the test by a human operator, introducing subjectivity and the possibility of errors in the extraction of the results. While automated test readers providing a result-consistent solution are widely available, they usually lack portability. In this paper, we present a smartphone-based automated reader for drug-of-abuse lateral flow assay tests, consisting of an inexpensive light box and a smartphone device. Test images captured with the smartphone camera are processed in the device using computer vision and machine learning techniques to perform automatic extraction of the results. A deep validation of the system has been carried out showing the high accuracy of the system. The proposed approach, applicable to any line-based or color-based lateral flow test in the market, effectively reduces the manufacturing costs of the reader and makes it portable and massively available while providing accurate, reliable results., We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)

During the process of design and development of an autonomous Multi-UAV System, two main problems appear. The first one is the difficulty of designing all the modules and behaviors of the aerial multi-robot system. The second one is the difficulty of having an autonomous prototype of the system for the developers that allows to test the performance of each module even in an early stage of the project. These two problems motivate this paper. A multipurpose system architecture for autonomous multi-UAV platforms is presented. This versatile system architecture can be used by the system designers as a template when developing their own systems. The proposed system architecture is general enough to be used in a wide range of applications, as demonstrated in the paper. This system architecture aims to be a reference for all designers. Additionally, to allow for the fast prototyping of autonomous multi-aerial systems, an Open Source framework based on the previously defined system architecture is introduced. It allows developers to have a flight proven multi-aerial system ready to use, so that they can test their algorithms even in an early stage of the project. The implementation of this framework, introduced in the paper with the name of ``CVG Quadrotor Swarm'', which has also the advantages of being modular and compatible with different aerial platforms, can be found at \url{https://github.com/Vision4UAV/cvg_quadrotor_swarm} with a consistent catalog of available modules. The good performance of this framework is demonstrated in the paper by choosing a basic instance of it and carrying out simulation and experimental tests whose results are summarized and discussed in this paper.

Purpose The purpose of this paper is to describe the specification language TML for adaptive mission plans that the authors designed and implemented for the open-source framework Aerostack for aerial robotics. Design/methodology/approach The TML language combines a task-based hierarchical approach together with a more flexible representation, rule-based reactive planning, to facilitate adaptability. This approach includes additional notions that abstract programming details. The authors built an interpreter integrated in the software framework Aerostack. The interpreter was validated with flight experiments for multi-robot missions in dynamic environments. Findings The experiments proved that the TML language is easy to use and expressive enough to formulate adaptive missions in dynamic environments. The experiments also showed that the TML interpreter is efficient to execute multi-robot aerial missions and reusable for different platforms. The TML interpreter is able to verify the mission plan before its execution, which increases robustness and safety, avoiding the execution of certain plans that are not feasible. Originality/value One of the main contributions of this work is the availability of a reliable solution to specify aerial mission plans, integrated in an active open-source project with periodic releases. To the best knowledge of the authors, there are not solutions similar to this in other active open-source projects. As additional contributions, TML uses an original combination of representations for adaptive mission plans (i.e. task trees with original abstract notions and rule-based reactive planning) together with the demonstration of its adequacy for aerial robotics.

The goal of the work presented in this paper is to facilitate the cooperation between human opera- tors and aerial robots to perform surface inspec- tion missions. Our approach is based on a model of human collaborative control with a mixed ini- tiative interaction. In the paper, we present our human-robot cooperation model based on the combination of a supervisory mode and an as- sistance mode with a set of interaction patterns. We developed a software system implementing this interaction model and carried out several real flight experiments that proved that this ap- proach can be used in aerial robotics for sur- face inspection missions (e.g., in vision based indoor missions). Compared to a conventional tele-operated inspection system, the solution pre- sented in this paper gives more autonomy to the aerial systems, reducing the cognitive load of the operator during the mission development.

International audience; This paper presents the description and experimental results of a versatile Visual Marker based Multi-Sensor Fusion State Estimation that allows to combine a variable optional number of sensors and positioning algorithms in a loosely-coupling fashion, incorporating visual markers to increase its performances. This technique allows an aerial robot to navigate in different environments and carrying out different missions with the same state estimation architecture, exploiting the best from every sensor. The state estimation algorithm has been successfully tested controlling a quadrotor equipped with an extra IMU and a RGB camera used only to detect visual markers. The entire framework runs on an onboard computer, including the controllers and the proposed state estimator. The whole software is made publicly available to the scientific community through an open source implementation.

A reliable estimation of the flight altitude in dynamic and unstructured indoor environments is an unsolved problem. Standalone available sensors, such as distance sensors, barometers and accelerometers, have multiple limitations in presence of non-flat ground surfaces, or in cluttered areas. To overcome these sensor limitations, maximizing their individual performance, this paper presents a modular EKF- based multi-sensor fusion approach for accurate vertical localization of multirotor UAVs in dynamic and unstructured indoor environments. The state estimator allows to combine the information provided by a variable number and type of sensors, including IMU, barometer and distance sensors, with the capabilities of sensor auto calibration and bias estimation, as well as a flexible configuration of the prediction and update stages. Several autonomous indoors real flights in unstructured environments have been conducted in order to validate our proposed state estimator, enabling the UAV to maintain the desired flight altitude when navigating over wide range of obstacles. Furthermore, it has been successfully used in IMAV 2016 competition. The presented work has been made publicly available to the scientific community as an open source software within the Aerostack1 framework.

In this paper, a fully-autonomous quadrotor aerial robot for solving the different missions proposed in the 2016 International Micro Air Vehicle (IMAV) Indoor Competition is presented. The missions proposed in the IMAV 2016 competition involve the execution of high-level missions such as entering and exiting a building, exploring an unknown indoor environment, recognizing and interacting with objects, landing autonomously on a moving platform, etc. For solving the aforementioned missions, a fully-autonomous quadrotor aerial robot has been designed, based on a complete hardware configuration and a versatile software architecture, which allows the aerial robot to complete all the missions in a fully autonomous and consecutive manner. A thorough evaluation of the proposed system has been carried out in both simulated flights, using the Gazebo simulator in combination with PX4 Software-In-The-Loop, and real flights, demonstrating the appropriate capabilities of the proposed system for performing high-level missions and its flexibility for being adapted to a wide variety of applications.

The development of deliberative capabilities is required to achieve an intelligent fully autonomous behavior of unmanned aerial systems. An important deliberative capability is the generation of collision-free paths in complex environments. This paper presents a robust real-time collision-free path planner used for the horizontal 2D navigation of multirotor aerial robots in dynamic environments. Its design, using geometric primitives to describe the environment combined with a launching time generation of a probabilistic roadmap graph, permits an efficient management of dynamic obstacles. The use of an A∗ discrete search algorithm, together with a potential field map as the cost function, allows to speed up the collisionfree path computation ensuring that it never falls in local minima. Additionally, the velocity and acceleration along the collision-free planned path is calculated. The performance of the proposed path planner is evaluated in this paper with two simulations with complex environments including a labyrinth and dead ends, and with a real flight experiment where three fully autonomous aerial robots executed an emulated search and rescue mission. The proposed path planner has been released to the scientific community as an open-source software included in Aerostack[1]. In addition, it has extensively been used in multiple research projects with real flights, demonstrating its good performance.

The vast majority of approaches make use of features to track objects. In this paper, we address the tracking problem with a tracking-by-registration strategy based on direct methods. We propose a hierarchical strategy in terms of image resolution and number of parameters estimated in each resolution, that allows direct methods to be applied in demanding real-time visual-tracking applications. We have called this strategy the Hierarchical Multi-Parametric and Multi-Resolution strategy (HMPMR). The Inverse Composition Image Alignment Algorithm (ICIA) is used as an image registration technique and is extended to an HMPMR-ICIA. The proposed strategy is tested with different datasets and also with image data from real flight tests using an Unmanned Aerial Vehicle, where the requirements of direct methods are easily unsatisfied (e.g. vehicle vibrations). Results show that using an HMPMR approach, it is possible to cope with the efficiency problem and with the small motion constraint of direct methods, conducting the tracking task at real-time frame rates and obtaining a performance that is comparable to, or even better than, the one obtained with the other algorithms that were analyzed.

La navegación totalmente autónoma de una flota de robots aéreos realizando misiones dinámicas complejas en entornos desafiantes no estructurados, es necesaria para simplificar el uso de los vehículos aéreos y extender su utilización a un mayor número de aplicaciones, es necesaria. El desarrollo de un sistema inteligente multi-robot completamente autónomo sigue siendo un problema abierto con soluciones parciales e incompletas en robótica aérea, habiéndose sólo desarrollado algunas arquitectura abiertas para sistemas aéreos, las cuales presentan limitaciones en su nivel de autonomía y versatilidad. Esta tesis presenta una arquitectura de sistema versátil para robótica aérea que permite el funcionamiento totalmente autónomo de un sistema multi-robot aéreo, cumpliendo con los requisitos de ser agnóstico a nivel de misión, plataforma y entorno. Se ha caracterizado a nivel abstracto y general, definiendo sus siete subsistemas, sus funcionalidades, y sus interfaces a alto nivel, garantizándose la versatilidad y flexibilidad. Los siete subsistemas propuestos son: Sistema de Extracción de Características, Sistema Motriz, Sistema de Conciencia Situacional, Sistema Ejecutivo, Sistema de Planificación, Sistema de Supervisión y Sistema de Comunicación. Esta arquitectura de sistema proporciona a los diseñadores una arquitectura inicial de la que partir cuando desarrollan sus propios sistemas inteligentes totalmente autónomos multi-robots aéreo. La validación de la arquitectura propuesta del sistema es una tarea compleja ya que el rendimiento del sistema completo depende de la misión, el entorno, la configuración del hardware, los algoritmos empleados y su implementation software. Tres tipos de escenarios se utilizaron con éxito para proporcionar una evaluación global del sistema completo, validando el desempeño de la arquitectura de sistema propuesta: (1) competiciones internacionales de robótica aérea, (2) desafíos auto-propuestos y (3) demostraciones públicas. Además, la tesis presenta varios algoritmos que permiten un aumento del nivel de autonomía de los sistemas robóticos aéreos, desarrollados en el contexto de una aplicación particular. Estos algoritmos se pueden agrupar en: (1) percepción, (2) control, (3) planificación y ejecución de tareas, (4) inteligencia y cognición, y (5) comunicación e interacción. Todos estos componentes han sido evaluados individualmente, demostrando su desempeño. Sin embargo, su importancia aparece cuando se integran en el sistema completo. Los componentes propuestos más destacados, presentados con un alto nivel de detalle, son los siguientes: (1) detección y reconstrucción de helipuertos, (2) percepción basada en odometría y marcadores visuales con reconstrucción del entorno, (3) percepción basada en odometría y visión por computador para mapas con cuadrícula, (4) percepción basada en la fusión de múltiples sensores con reconstrucción del entorno, y (5) planificación de trayectorias libre de colisión para entornos dinámicos. La tesis presenta también un entorno de software de código abierto, llamado Aerostack, que facilita la implementation eficiente de la arquitectura de sistema y los algoritmos desarrollados por medio de componentes de software. Este software se organiza de forma modular mediante paquetes agrupados por su funcionalidad, sus dependencias y su ciclo de vida. El entorno de software propuesto se basa en el middleware ROS para la comunicación interprocesos y utiliza un paradigma asincrono multiproceso en el que cada funcionalidad elemental se implementa como un solo proceso, facilitando el desarrollo y permitiendo un procesamiento distribuido. El entorno de software propuesto ha demostrado ser versátil y escalable, siendo los desarrolladores capaces de reutilizar tantos módulos de software como sea necesario, y de modificar o desarrollar nuevos módulos sin necesidad de adaptar otros componentes. ABSTRACT Achieving a fully autonomous navigation of a fleet of aerial robots when performing complex dynamic missions in challenging unstructured environments is an essential requirement to simplify the use of micro aerial vehicles and to extend their utilization to a greater number of applications. The development of a multi-robot fully autonomous intelligent system is still an open problem with partial and incomplete solutions in aerial robotics, and only some open source architecture frameworks for aerial systems have been developed so far, which present limitations in their autonomy level and in their versatility. This thesis presents a versatile system architecture for aerial robotics that enables the fully autonomous operation of an aerial multi-robot system and fulfills the requirements of being mission, platform, and environment agnostic. It has been characterized in an abstract and general level, defining its seven subsystems, their functionalities, and their interfaces in a top-level way that guarantees the versatility and flexibility. The seven proposed subsystems are: Feature Extraction System, Motor System, Situation Awareness System, Executive System, Planning System, Supervision System, and Communication System. This system architecture provides system designers the initial architecture for developing their own fully autonomous intelligent aerial multi-robot systems. The validation of the proposed system architecture is a complex task since the performance of the complete system is highly dependent on the mission, the environment, the hardware setup, the employed algorithms, and their software implementation. Three kinds of scenarios were successfully used to provide a global evaluation of the complete system, validating the performance of the proposed system architecture: (1) international aerial robotics competitions, (2) self-proposed challenges, and (3) public demonstrations. In addition, this thesis presents several algorithms, with different level of detail, that yield to an increased level of autonomy of the aerial robotic systems, developed in the context of particular applications. These algorithms can be gathered in the following five groups: (1) perception, (2) control, (3) planning and task execution, (4) intelligence and cognition, and (5) communication and interaction. All these components have been evaluated isolatedly, demonstrating their individual performance. Nevertheless, their importance stands out when integrated into the complete system. The most important components presented in this thesis, analyzed with a high level of detail, are the following: (1) helipad detection and reconstruction for shipboard landing, (2) perception based on odometry and visual markers with environment reconstruction, (3) perception based on odometry and computer vision for gridded maps, (4) perception based on multi-sensor fusion with environment reconstruction, and (5) collision-free path planning for dynamic environments. This thesis presents as well, an open-source software framework, called Aerostack, that facilitates a cost and time effective implementation of the designed system architecture and the developed algorithms by means of software components. This framework is modularly organized in software packages gathered by their functionality, their dependencies, and their life state. The proposed software framework relies on the widely used ROS middleware for interprocess communication and uses an asynchronous multiprocess paradigm where every elementary functionality is implemented as a single process, easing the development and allowing a distributed processing. The proposed software framework has demonstrated to be versatile and scalable, being the developers capable of reusing its software modules as needed, and modifying or developing new modules without adaptations of any other components.

To specify missions in aerial robotics, many existing applications follow an approach based on a list of waypoints, which has been proven to be useful and practical in professional domains (agriculture of precision, creation of terrain maps, etc.). However this approach has limitations to be used in other problems such as the one defined in the IMAV 2016 competition (e.g., a search and rescue mission). In this paper we describe a language to specify missions for aerial robotics that tries to overcome such limitations. This language has been designed as part of a complete software framework and architecture for aerial robotics called Aerostack. The paper describes the result of experimental evaluation in real flight and its adequacy for the IMAV 2016 competition.

In this paper a scalable and flexible Architecture for real-time mission planning and dynamic agent-to-task assignment for a swarm of Unmanned Aerial Vehicles (UAV) is presented. The proposed mission planning architecture consists of a Global Mission Planner (GMP) which is responsible of assigning and monitoring different high-level missions through an Agent Mission Planner (AMP), which is in charge of providing and monitoring each task of the mission to each UAV in the swarm. The objective of the proposed architecture is to carry out high-level missions such as autonomous multi-agent exploration, automatic target detection and recognition, search and rescue, and other different missions with the ability of dynamically re-adapt the mission in real-time. The proposed architecture has been evaluated in simulation and real indoor flights demonstrating its robustness in different scenarios and its flexibility for real-time mission re-planning and dynamic agent-to-task assignment.

To simplify the usage of the Unmanned Aerial Systems (UAS), extending their use to a great number of applications, fully autonomous operation is needed. There are many open-source architecture frameworks for UAS that claim the autonomous operation of UAS, but they still have two main open issues: (1) level of autonomy, being in most of the cases limited and (2) versatility, being most of them designed specifically for some applications or aerial platforms. As a response to these needs and issues, this paper presents Aerostack, a system architecture and open-source multi-purpose software framework for autonomous multi-UAS operation. To provide higher degrees of autonomy, Aerostack’s system architecture integrates state of the art concepts of intelligent, cognitive and social robotics, based on five layers: reactive, executive, deliberative, reflective, and social. To be a highly versatile practical solution, Aerostack’s open-source software framework includes the main components to execute the architecture for fully autonomous missions of swarms of UAS; a collection of ready-to-use and flight proven modular components that can be reused by the users and developers; and compatibility with five well known aerial platforms, as well as a high number of sensors. Aerostack has been validated during three years by its successful use on many research projects, international competitions and exhibitions. To corroborate this fact, this paper also presents Aerostack carrying out a fictional fully autonomous indoors search and rescue mission.

El origen y desarrollo de la aviacion no tripulada ha ido de la mano con el de la aviacion tripulada, empezando ambos desde practicamente el mismo punto. Al igual que en la aviacion tripulada convencional, en la aviacion no tripulada, las aplicaciones militares han sido el motor del desarrollo tecnologico y las aplicaciones potenciales de este tipo de sistemas se han desarrollado en gran parte del siglo XX y principios del siglo XXI. Finalmente, en los ultimos tiempos, estos sistemas estan experimentando un impresionante auge debido al descubrimiento de la gran variedad de operaciones comerciales y civiles que son capaces de realizar de manera muy eficaz. Este articulo pretende resumir la evolucion historica que estos sistemas han sufrido, para al final, presentar un breve analisis de las principales aplicaciones civiles / comerciales, tratando de ofrecer una vision general de los principales tipos de sistemas, su clasificacion y configuracion general Palabras claves: UAV, RPAS, no tripulado, tipos de aeronaves, avion no tripulado, vehiculo aereo no tripulado

Autonomous route following with road vehicles has gained popularity in the last few decades. In order to provide highly automated driver assistance systems, different types and combinations of sensors have been presented in the literature. However, most of these approaches apply quite sophisticated and expensive sensors, and hence, the development of a cost-efficient solution still remains a challenging problem. This work proposes the use of a single monocular camera sensor for an automatic steering control, speed assistance for the driver and localization of the vehicle on a road. Herein, we assume that the vehicle is mainly traveling along a predefined path, such as in public transport. A computer vision approach is presented to detect a line painted on the road, which defines the path to follow. Visual markers with a special design painted on the road provide information to localize the vehicle and to assist in its speed control. Furthermore, a vision-based control system, which keeps the vehicle on the predefined path under inner-city speed constraints, is also presented. Real driving tests with a commercial car on a closed circuit finally prove the applicability of the derived approach. In these tests, the car reached a maximum speed of 48 km/h and successfully traveled a distance of 7 km without the intervention of a human driver and any interruption., The work reported in this paper is the product of several research stages at the Computer Vision Group Universidad Politécnica de Madrid in collaboration with INSIA-UPM and Siemens España S.A. This project is partially funded by the Centre for the Development of Industrial Technology (CDTI). The authors would like to thank the company SIEMENS España S.A. that has made possible the research described in this paper through several contracts and the people at LABIE (INSIA-UPM) for their support.

Nowadays, the utilization of multiple sensors on every robotic platform is a reality due to their low cost, small size and light weight. Multi Sensor Fusion (MSF) algorithms are required to take advance of all the given measurements in a robust and complete manner. These high demanding developed algorithms need to be tested under real and challenging situations and environments. To the knowledge of the authors, none of the available datasets fulfills are fully suitable to be used for MSF applied to Robotics, due to the lack of multiple sensor measurements provided by light weight, small size and low cost sensors; due to their restricted motions (typically planar movements); or to their limited environmental conditions. The contributions of the paper are twofold. First, a low-cost portable and versatile testbed has been developed for MSF research with various types of sensors. Second, a group of datasets for MSF research for Robotics have been made public as a common framework for algorithm testing after a comparison with the existing databases in the state of the art, highlighting the differences and advantages of the one presented in this paper, that are: low-cost sensors for general use on Robotics, fully 3 dimensional movements (six degrees of freedom), as well as challenging indoor and outdoor small and large environments.

Building inspection is a critical issue for designing rehabilitation projects, which are recently gaining importance for environmental and energy efficiency reasons. Image sensors on-board unmanned aerial vehicles are a powerful tool for building inspection, given the diversity and complexity of facades and materials, and mainly, their vertical disposition. The UBRISTES (UAV-based Building Rehabilitation with vISible and ThErmal infrared remote Sensing) system is proposed as an effective solution for facade inspection in urban areas, validating a method for the simultaneous acquisition of visible and thermal aerial imaging applied to the detection of the main types of facade anomalies/pathologies, and showcasing its possibilities using a first principles analysis. Two public buildings have been considered for evaluating the proposed system. UBRISTES is ready to use in building inspection and has been proved as a useful tool in the design of rehabilitation projects for inaccessible, complex building structures in the context of energy efficiency.

The International Aerial Robotics Competition (IARC) aims at pulling forward the state of the art in UAV. The Mission's 7 challenge deals mainly with GPS/Laser denied navigation, Robot-Robot interaction and obstacle avoidance in the setting of a ground robot herding problem. We present in this paper our UAV which took part in the 2014 competition, in the China venue. This year, the mission was not completed by any participant but our team at Technical University of Madrid (UPM) were awarded with two special prizes: Best Target Detection and Best System Control. The platform, hardware and the developed software used in this top leading competition are presented in this paper. This software has three main components: the visual localization and mapping algorithm; the control algorithms; and the mission planner. A statement of the safety measures integrated in the drone and of our efforts to ensure field testing in conditions as close as possible to the challenger?s are also included.

This paper presents a completely autonomous solution to participate in the Indoor Challenge of the 2013 International Micro Air Vehicle Competition (IMAV 2013). Our proposal is a multi-robot system with no centralized coordination whose robotic agents share their position estimates. The capability of each agent to navigate avoiding collisions is a consequence of the resulting emergent behavior. Each agent consists of a ground station running an instance of the proposed architecture that communicates over WiFi with an AR Drone 2.0 quadrotor. Visual markers are employed to sense and map obstacles and to improve the pose estimation based on Inertial Measurement Unit (IMU) and ground optical flow data. Based on our architecture, each robotic agent can navigate avoiding obstacles and other members of the multi-robot system. The solution is demonstrated and the achieved navigation performance is evaluated by means of experimental flights. This work also analyzes the capabilities of the presented solution in simulated flights of the IMAV 2013 Indoor Challenge. The performance of the CVG UPM team was awarded with the First Prize in the Indoor Autonomy Challenge of the IMAV 2013 competition.

This paper presents a completely autonomous solution to participate in the 2013 International Micro Air Vehicle Indoor Flight Competition (IMAV2013). Our proposal is a modular multi-robot swarm architecture, based on the Robot Operating System (ROS) software framework, where the only information shared among swarm agents is each robot's position. Each swarm agent consists of an AR Drone 2.0 quadrotor connected to a laptop which runs the software architecture. In order to present a completely visual-based solution the localization problem is simplified by the usage of ArUco visual markers. These visual markers are used to sense and map obstacles and to improve the pose estimation based on the IMU and optical data flow by means of an Extended Kalman Filter localization and mapping method. The presented solution and the performance of the CVG UPM team were awarded with the First Prize in the Indoors Autonomy Challenge of the IMAV2013 competition.

The presented work is focused on GPS-denied autonomous navigation for multirotor platforms. The problem of safe navigation based on noisy odometry measurements is discussed, and experimentally tested on the case of on-board optical flow measurements. A navigation controller is proposed which allows, through a group of configurable parameters, to ensure that the vehicle will fly on a speed specified flight envelope where the quality of the optical flow measurements is guaranteed. In order to attain safe navigation, the multirotor is modeled as a flying vehicle with specific kinematic constraints. The designed unperfect odometry based controller architecture has been experimentally tested on various multirotor vehicles, where the vehicles featured similar sensoring capabilities and the tolerance of our approach have been demonstrated. This work was implemented to compete in the International Micro Air Vehicle Conference and Flight Competition IMAV 2012, gaining two awards: the Special Award on “Best Automatic Performance - IMAV 2012” and the second overall prize in the category of “Indoor Flight Dynamics - Rotary Wing MAV”. Most of the related code is available as two open-source projects hosted on GitHub.

The IARC competitions aim at making the state of the art in UAV progress. The 2014 challenge deals mainly with GPS/Laser denied navigation, Robot-Robot interaction and Obstacle avoidance in the setting of a ground robot herding problem. We present in this paper a drone which will take part in this competition. The platform and hardware it is composed of and the software we designed are introduced. This software has three main components: the visual information acquisition, the mapping algorithm and the Aritificial Intelligence mission planner. A statement of the safety measures integrated in the drone and of our efforts to ensure field testing in conditions as close as possible to the challenge?s is also included.

We present a vision based control strategy for tracking and following objects using an Unmanned Aerial Vehicle. We have developed an image based visual servoing method that uses only a forward looking camera for tracking and following objects from a multi-rotor UAV, without any dependence on GPS systems. Our proposed method tracks a user specified object continuously while maintaining a fixed distance from the object and also simultaneously keeping it in the center of the image plane. The algorithm is validated using a Parrot AR Drone 2.0 in outdoor conditions while tracking and following people, occlusions and also fast moving objects; showing the robustness of the proposed systems against perturbations and illumination changes. Our experiments show that the system is able to track a great variety of objects present in suburban areas, among others: people, windows, AC machines, cars and plants.