625 research outputs found

    Decentralized 3D Collision Avoidance for Multiple UAVs in Outdoor Environments

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    The use of multiple aerial vehicles for autonomous missions is turning into commonplace. In many of these applications, the Unmanned Aerial Vehicles (UAVs) have to cooperate and navigate in a shared airspace, becoming 3D collision avoidance a relevant issue. Outdoor scenarios impose additional challenges: (i) accurate positioning systems are costly; (ii) communication can be unreliable or delayed; and (iii) external conditions like wind gusts affect UAVs’ maneuverability. In this paper, we present 3D-SWAP, a decentralized algorithm for 3D collision avoidance with multiple UAVs. 3D-SWAP operates reactively without high computational requirements and allows UAVs to integrate measurements from their local sensors with positions of other teammates within communication range. We tested 3D-SWAP with our team of custom-designed UAVs. First, we used a Software-In-The-Loop simulator for system integration and evaluation. Second, we run field experiments with up to three UAVs in an outdoor scenario with uncontrolled conditions (i.e., noisy positioning systems, wind gusts, etc). We report our results and our procedures for this field experimentation.European Union’s Horizon 2020 research and innovation programme No 731667 (MULTIDRONE

    Collision avoidance strategies for unmanned aerial vehicles in formation flight

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    Collision avoidance strategies for multiple UAVs (Unmanned Aerial Vehicles) based on geometry are investigated in this study. The proposed strategies allow a group of UAVs to avoid obstacles and separate if necessary through a simple algorithm with low computation by expanding the collision-cone approach to formation of UAVs. The geometric approach uses line-of-sight vectors and relative velocity vectors where dynamic constraints are included in the formation. Each UAV can determine which plane and direction are available for collision avoidance. An analysis is performed to define an envelope for collision avoidance, where angular rate limits and obstacle detection range limits are considered. Based on the collision avoidance envelope, each UAV in a formation determines whether the formation can be maintained or not while avoiding obstacles. Numerical simulations are performed to demonstrate the performance of the proposed strategies

    Contributions to deconfliction advanced U-space services for multiple unmanned aerial systems including field tests validation

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    Unmanned Aerial Systems (UAS) will become commonplace, the number of UAS flying in European airspace is expected to increase from a few thousand to hundreds of thousands by 2050. To prepare for this approaching, national and international organizations involved in aerial traffic management are now developing new laws and restructuring the airspace to incorporate UAS into civil airspace. The Single European Sky ATM Research considers the development of the U-space, a crucial step to enable the safe, secure, and efficient access of a large set of UAS into airspace. The design, integration, and validation of a set of modules that contribute to our UTM architecture for advanced U-space services are described in this Thesis. With an emphasis on conflict detection and resolution features, the architecture is flexible, modular, and scalable. The UTM is designed to work without the need for human involvement, to achieve U-space required scalability due to the large number of expected operations. However, it recommends actions to the UAS operator since, under current regulations, the operator is accountable for carrying out the recommendations of the UTM. Moreover, our development is based on the Robot Operating System (ROS) and is open source. The main developments of the proposed Thesis are monitoring and tactical deconfliction services, which are in charge of identifying and resolving possible conflicts that arise in the shared airspace of several UAS. By limiting the conflict search to a local search surrounding each waypoint, the proposed conflict detection method aims to improve conflict detection. By splitting the issue down into smaller subproblems with only two waypoints, the conflict resolution method tries to decrease the deviation distance from the initial flight plan. The proposed method for resolving potential threats is based on the premise that UAS can follow trajectories in time and space properly. Therefore, another contribution of the presented Thesis is an UAS 4D trajectory follower that can correct space and temporal deviations while following a given trajectory. Currently, commercial autopilots do not offer this functionality that allows to improve the airspace occupancy using time as an additional dimension. Moreover, the integration of onboard detect and avoid capabilities, as well as the consequences for U-space services are examined in this Thesis. A module capable of detecting large static unexpected obstacles and generating an alternative route to avoid the obstacle online is presented. Finally, the presented UTM architecture has been tested in both software-in-theloop and hardware-in-the-loop development enviroments, but also in real scenarios using unmanned aircraft. These scenarios were designed by selecting the most relevant UAS operation applications, such as the inspection of wind turbines, power lines and precision agriculture, as well as event and forest monitoring. ATLAS and El Arenosillo were the locations of the tests carried out thanks to the European projects SAFEDRONE and GAUSS.Los sistemas aéreos no tripulados (UAS en inglés) se convertirán en algo habitual. Se prevé que el número de UAS que vuelen en el espacio aéreo europeo pase de unos pocos miles a cientos de miles en 2050. Para prepararse para esta aproximación, las organizaciones nacionales e internacionales dedicadas a la gestión del tráfico aéreo están elaborando nuevas leyes y reestructurando el espacio aéreo para incorporar los UAS al espacio aéreo civil. SESAR (del inglés Single European Sky ATM Research) considera el desarrollo de U-space, un paso crucial para permitir el acceso seguro y eficiente de un gran conjunto de UAS al espacio aéreo. En esta Tesis se describe el diseño, la integración y la validación de un conjunto de módulos que contribuyen a nuestra arquitectura UTM (del inglés Unmanned aerial system Traffic Management) para los servicios avanzados del U-space. Con un énfasis en las características de detección y resolución de conflictos, la arquitectura es flexible, modular y escalable. La UTM está diseñada para funcionar sin necesidad de intervención humana, para lograr la escalabilidad requerida por U-space debido al gran número de operaciones previstas. Sin embargo, la UTM únicamente recomienda acciones al operador del UAS ya que, según la normativa vigente, el operador es responsable de las operaciones realizadas. Además, nuestro desarrollo está basado en el Sistema Operativo de Robots (ROS en inglés) y es de código abierto. Los principales desarrollos de la presente Tesis son los servicios de monitorización y evitación de conflictos, que se encargan de identificar y resolver los posibles conflictos que surjan en el espacio aéreo compartido de varios UAS. Limitando la búsqueda de conflictos a una búsqueda local alrededor de cada punto de ruta, el método de detección de conflictos pretende mejorar la detección de conflictos. Al dividir el problema en subproblemas más pequeños con sólo dos puntos de ruta, el método de resolución de conflictos intenta disminuir la distancia de desviación del plan de vuelo inicial. El método de resolución de conflictos propuesto se basa en la premisa de que los UAS pueden seguir las trayectorias en el tiempo y espacio de forma adecuada. Por tanto, otra de las aportaciones de la Tesis presentada es un seguidor de trayectorias 4D de UAS que puede corregir las desviaciones espaciales y temporales mientras sigue una trayectoria determinada. Actualmente, los autopilotos comerciales no ofrecen esta funcionalidad que permite mejorar la ocupación del espacio aéreo utilizando el tiempo como una dimensión adicional. Además, en esta Tesis se examina la capacidad de integración de módulos a bordo de detección y evitación de obstáculos, así como las consecuencias para los servicios de U-space. Se presenta un módulo capaz de detectar grandes obstáculos estáticos inesperados y capaz de generar una ruta alternativa para evitar dicho obstáculo. Por último, la arquitectura UTM presentada ha sido probada en entornos de desarrollo de simulación, pero también en escenarios reales con aeronaves no tripuladas. Estos escenarios se diseñaron seleccionando las aplicaciones de operación de UAS más relevantes, como la inspección de aerogeneradores, líneas eléctricas y agricultura de precisión, así como la monitorización de eventos y bosques. ATLAS y El Arenosillo fueron las sedes de las pruebas realizadas gracias a los proyectos europeos SAFEDRONE y GAUSS

    Safe Trajectory Planning for Multiple Aerial Vehicles with Segmentation-Adaptive Pseudospectral Collocation

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    IEEE International Conference on Robotics and Automation (ICRA), 26-30 May 2015 Seattle, WA, USAThis paper proposes a method called Segmentation-adaptive Pseudospectral collocation to address the problem of safe trajectory generation in missions with cooperating multiple aerial vehicles. Pseudospectral collocation can generate optimized collision-free trajectories, but for multiple aerial vehicles it cannot guarantee that the safety separation distance is maintained in the whole trajectories, since the constraints are only enforced in discrete points in the trajectory (collocation points). Hp-adaptive pseudospectral collocation increases iteratively the number of collocation points and the degree of the approximating polynomial, but this may lead to an exponential increase of the computational load. The proposed method solves the problem by selectively adding new collocation points where they are needed, only in the segments with conflicts in each iteration, thus effectively reducing the number of collocation points and the computation time with respect to other pseudospectral collocation formulations. The proposed method allows both changes of speed and changes of heading for each aerial vehicle to guarantee the safety distance between them. Its computational load and scalability are studied in randomly generated scenarios. Moreover, a comparison with other method is presented. Several experiments to test the validity of the approach have been also carried out in the multivehicle aerial testbed of the Center for Advanced Aerospace Technologies.Comisión europea FP7 ICT (288082)Junta de Andalucía P11-TIC-706

    UAS in the Airspace: A Review on Integration, Simulation, Optimization, and Open Challenges

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    Air transportation is essential for society, and it is increasing gradually due to its importance. To improve the airspace operation, new technologies are under development, such as Unmanned Aircraft Systems (UAS). In fact, in the past few years, there has been a growth in UAS numbers in segregated airspace. However, there is an interest in integrating these aircraft into the National Airspace System (NAS). The UAS is vital to different industries due to its advantages brought to the airspace (e.g., efficiency). Conversely, the relationship between UAS and Air Traffic Control (ATC) needs to be well-defined due to the impacts on ATC capacity these aircraft may present. Throughout the years, this impact may be lower than it is nowadays because the current lack of familiarity in this relationship contributes to higher workload levels. Thereupon, the primary goal of this research is to present a comprehensive review of the advancements in the integration of UAS in the National Airspace System (NAS) from different perspectives. We consider the challenges regarding simulation, final approach, and optimization of problems related to the interoperability of such systems in the airspace. Finally, we identify several open challenges in the field based on the existing state-of-the-art proposals

    A Survey on Aerial Swarm Robotics

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    The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

    A formal model for planning and controlling search and rescue actions at sea

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    Recently, during search and rescue actions at sea, Unmanned Aerial Vehicles (UAVs) have been used. Onboard decision capabilities allow an UAV vehicle to reach the entity that is in dis­tress at sea. UAVs are launched within a few minutes to begin search actions. When the exact location of the irtjured entity is detected, a rescue action should begin. According to the collected information about the vessel's position, manoeuvrability, and velocity, the control centre deter­mines which vessel is to be engaged in the rescue action. This highly autonomous system can be described as a discrete event system. Certain states of such systems, such as collisions, are undesirable. This paper presents implementation of information flow to supervise, control, and monitor the behaviour of the UAVs during the search, to avoid collisions and to communicate with computational onboard sub-systems. Planning algorithms and coloured Petri nets are used to specify different phases of the mission execution. When a certain UAV detects an injured entity, alternative encoded reactions are triggered and a control centre starts implementing the rescue plan

    New Development on Sense and Avoid Strategies for Unmanned Aerial Vehicles

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    Unmanned Aerial Vehicles (UAVs) can carry out more complex civilian and military applications with less cost and more flexibility in comparison of manned aircraft. Mid-air collision thus becomes profoundly important considering the safe operation of air transportation systems, when UAVs are increasingly used more with various applications and share the same airspace with manned air vehicles. To ensure safe flights, UAVs have to configure Sense and Avoid (S&A) systems performing necessary maneuvers to avoid collisions. After analyzing the manner of S&A system, avoidance strategies based on a subset of possible collision scenarios are proposed in this thesis. 1) To avoid a face-to-face intruder, a feasible trajectory is generated by differential geometric guidance, where the constraints of UAV dynamics are considered. 2) The Biogeography Based Optimization (BBO) approach is exploited to generate an optimal trajectory to avoid multiple intruders’ threats in the landing phase. 3) By formulating the collision avoidance problem within a Markov Decision Process (MDP) framework, a desired trajectory is produced to avoid multiple intruders in the 2D plane. 4) MDP optimization method is extended to address the problem of optimal 3D conflict resolution involving multiple aircraft. 5) Considering that the safety of UAVs is directly related to the dynamic constraints, the differential flatness technique is developed to smoothen the optimal trajectory. 6) Energy based controller is designed such that the UAV is capable of following the generated trajectory
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