27 research outputs found

    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

    Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms

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    This book is a reprint of the Special Issue “Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms”,which was published in Applied Sciences

    Autonomous Navigation of Quadrotor Swarms

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    RÉSUMÉ La mise sur le marché de composants toujours plus performants et compétitifs en termes de coût, ainsi que le développement rapide des technologies de commande et de navigation en robotique, nous ont amenés à envisager le contrôle d’un large essaim de quadrirotors. Di-verses solutions impliquant des drones existent déjà pour différentes applications: inventaire forestier, gestion du littoral, suivi du trafic, etc. Parmi celles-ci, la recherche et le sauvetage en situation d’urgence représentent à nos yeux la possibilité la plus intéressante et constitue, de fait, la première motivation de notre travail. Par conséquent, une large revue de littérature sur la question est fournie. Ce travail se concentre sur le contrôle de l’essaim lui-même, et non sur l’application finale. Tout d’abord, un modèle mathématique de la dynamique du quadrirotor est présenté et plusieurs lois de commande numérique sont synthétisées. Ces dernières implémentent les modes de fonctionnement nécessaires aux algorithmes de navigation, à savoir : commande en vitesse, commande en position et commande en suivi. Ensuite, deux solutions originales et complémentaires de contrôle d’essaim sont proposées. D’une part, un algorithme d’essaimage pour la navigation extérieure est développé. Contrairement à la plupart des travaux trouvés dans la littérature, la solution proposée ici gère non seulement le maintien, mais aussi l’initialisation de la formation. Plus spécifiquement, un modèle de formation hexagonale est introduit. Ensuite, les places en formation sont attribuées de façon optimale à l’aide de l’algorithme hongrois. Enfin, les agents se déplacent jusqu’à la place qui leur est assignée tout en évitant les autres agents avec un algorithme de navigation inspiré du Artificial Potential Field. De plus, cette solution tient compte de contraintes de conception réalistes et a été intégrée avec succès dans un logiciel embarqué de quadrotor déjà existant et opérationnel. Les résultats de simulations Software-In-The-Loop sont fournis. D’autre part, une solution d’essaimage pour la navigation intérieure est étudiée. L’algorithme proposé implémente un certain nombre de comportements individuels simples, de sorte qu’un grand essaim peut suivre un meneur dans des environnements encombrés en se fiant uniquement aux informations locales. Des simulations préliminaires sont effectuées et les résultats montrent qu’il serait possible de faire fonctionner, conformément au besoin étudié, un essaim de cent quadrirotors avec l’algorithme proposé. En particulier, l’essaim est capable de suivre le meneur, de maintenir la connectivité, d’éviter les collisions entre agents, d’éviter les obstacles et même de se faufiler dans des espaces étroits.----------ABSTRACT The ever-growing hardware capabilities and the rapid development of robotic control and navigation technologies have led us to consider the control of an entire swarm of quadrotors. Drone-based solutions have been developed for different applications: forest inventory, coastal management, traÿc monitoring, etc... Among these, the Search And Rescue application represents for us a very promising field of application and constitutes the first motivation of our work. As a result, a wide literature review on the matter is provided. Nevertheless, this work focuses on the swarm control itself, and not on the end user application. First, a mathematical model of the quadrotor dynamics is presented and several digital control laws are designed. The latter provide operating modes useful for the navigation algorithms, namely: velocity control, position control and tracking control. Then, two original and complimentary swarming solutions are proposed. On the one hand, a swarming algorithm for outdoor navigation is developed. Unlike most of the works reviewed in the literature, our solution handles not only the maintenance but also the initialization of the formation. More specifically, an hexagonal formation pattern is intro-duced. Then, positions are optimally assigned using the Hungarian algorithm. Finally, the agents move to their assigned position while avoiding collisions with the other fleet members thanks to a navigation algorithm inspired from Artificial Potential Field. In addition, this solution accounts for realistic design constraints and was successfully integrated into already existing quadrotor onboard software. Software-In-The-Loop simulation results are provided. On the other hand, a swarming solution for indoor navigation is investigated. The proposed algorithm enforces a certain set of expected individual simple behaviors such that a large swarm can follow a leader through cluttered environments relying only on local information. Preliminary simulations are run and the results show that it is possible to operate a swarm of a hundred quadrotors with the proposed algorithm. In particular, the swarm is able to follow the leader, maintain connectivity, avoid collisions with the other agents, avoid obstacles, and even squeeze to pass through narrow spaces

    Connectivity Preservation in Multi-Agent Systems using Model Predictive Control

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    Flocking of multiagent systems is one of the basic behaviors in the field of control of multiagent systems and it is an essential element of many real-life applications. Such systems under various network structures and environment modes have been extensively studied in the past decades. Navigation of agents in a leader-follower structure while operating in environments with obstacles is particularly challenging. One of the main challenges in flocking of multiagent systems is to preserve connectivity. Gradient descent method is widely utilized to achieve this goal. But the main shortcoming of applying this method for the leader-follower structure is the need for continuous data transmission between agents and/or the preservation of a fixed connection topology. In this research, we propose an innovative model predictive controller based on a potential field that maintains the connectivity of a flock of agents in a leader-follower structure with dynamic topology. The agents navigate through an environment with obstacles that form a path leading to a certain target. Such a control technique avoids collisions of followers with each other without using any communication links while following their leader which navigates in the environment through potential functions for modelling the neighbors and obstacles. The potential field is dynamically updated by introducing weight variables in order to preserve connectivity among the followers as we assume only the leader knows the target position. The values of these weights are changed in real-time according to trajectories of the agents when the critical neighbors of each agent is determined. We compare the performance of our predictive-control based algorithm with other approaches. The results show that our algorithm causes the agents to reach the target in less time. However, our algorithm faces more deadlock cases when the agents go through relatively narrow paths. Due to the consideration of the input costs in our controller, the group of agents reaching the target faster does not necessarily result in the followers consuming more energy than the leader

    Real-Time Obstacle and Collision Avoidance System for Fixed-Wing Unmanned Aerial Systems

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    The motivation for the research presented in this dissertation is to provide a two-fold solution to the problem of non-cooperative reactive mid-air threat avoidance for fixed-wing unmanned aerial systems. The first phase is an offline UAS trajectory planning designed for an altitude-specific mission. The second phase leans on the results produced during the first phase to provide intelligent, real-time, reactive mid-air threat avoidance logic. That real-time operating logic provides a given fixed-wing UAS with local threat awareness so it can get a feel for the danger represented by a potential threat before using results produced during the first phase to require aircraft rerouting. The first original contribution of this research is the Advanced Mapping and Waypoint Generator (AMWG), a piece of software which processes publicly available elevation data in order to only retain the information necessary for a given altitude-specific flight mission. The AMWG is what makes systematic offline trajectory possible. The AMWG first creates altitude groups in order to discard elevations points which are not relevant to a specific mission because of the altitude flown at. Those groups referred to as altitude layers can in turn be reused if the original layer becomes unsafe for the altitude range in use, and the other layers are used for altitude re-scheduling in order to update the current altitude layer to a safer layer. Each layer is bounded by a lower and higher altitude, within which terrain contours are considered constant according to a conservative approach involving the principle of natural erosion. The AMWG then proceeds to obstacle contours extraction using threshold and edge detection vision algorithms. A simplification of those obstacle contours and their corresponding free space zones counterparts is performed using a fixed -tolerance Douglas-Peucker algorithm. This simplification allows free space zones to be described by vectors instead of point clouds, which enables UAS point location. The resulting geometry is then processed through a vertical trapezoidal decomposition where for each vertex defining a contour a vertical line is drawn, and the results of this decomposition is a set of trapezoidal cells. The cells corresponding to obstacle contours are then removed from the original trapezoidal decomposition in order to solely retain the obstacle-free trapezoidal cells. After decomposition, cells sharing part of a common edge are considered from a graph theory perspective so it becomes possible to list all acyclic paths between two cells by applying a depth first search (DFS) algorithm. The final product of the AWMG is a network of connected free space trapezoidal cells with embedded connectivity information referred to as the Synthetic Terrain Avoidance (STA network). The walls of the trapezoidal cells are then extruded as the AWMG essentially approximates a three-dimensional world by considering it as a stratification of two-dimensional layers, but the real-time phase needs 3D support. Using the graph conceptual view and the depth first search algorithm, all the connected cell sequences joining the departure to the arrival cell can be listed, a capability which is used during aircraft rerouting. By connecting two adjacent cells' centroids to their common midpoint located on the shared edge, the resulting flying legs remain within the two cells. The next step for paths between two cells is to be converted into flyable paths, and the conversion uses main and fallback methods to achieve that. The preferred method is the closed-form Dubins paths method involving the design of sequences of arc circle-straight line-arc circle (CLC) in order to account for the minimum radius turn constrain of the UAS. An additional geometric transformation is developed and applied to the initial waypoints used in the Dubins method so the flying leg directions are respected which is not possible by using the Dubins method alone. When consecutive waypoints are too close from one another, a condition called the Dubins condition cannot be respected, and the UAS trajectory design switches to the numerical integration of a system of ordinary differential equations accounting for the minimum turning constraint. Using the Dubins method and the ODE method makes it possible for the AWMG to design flyable offline trajectories accounting for the lateral dynamic of the fixed-wing UAS. The second original contribution of this research is the development and demonstration of the Double Dispersion reduction RRT (DDRRT), an algorithm which employs two new developed logic schemes respectively referred to as Punctual Dispersion Reduction (PDR), and Spatial Dispersion Reduction exploration (SDR). The DDRRT is employed during the real-time in-flight phase where it initially assumes a perfect terrain and no unpredictable threat, consequently following a 100% adaptive goal biasing toward the next waypoint in its list. When a threat such as an unpredicted obstacle is detected, the (PDR) acknowledges the fact that the DDRRT tree branches have met an obstacle and the its goal-biasing toward the next waypoint is decreased. If the PDR keeps decreasing, the DDRRT develops awareness of its surrounding obstacles by relaxing its PDR and switching to SDR which has the effect of increasing the dispersion of its branches, but keeping their extension bounded by the cell containing the last good position of the UAS, Csafe. If a number of branches reach a limit proportional to the Csafe and its relative area, then the STA network is queried for alternative rerouting. The two phases provide real-time reactive mid - air threat avoidance scenarios with the ability for a UAS to develop local and realistic threat awareness before considering intelligent rerouting. Either the local exploration of the DDRRT is successful before reaching a maximum number of points, or the STA Network is required to find another route

    Distributed approaches for coverage missions with multiple heterogeneous UAVs for coastal areas.

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    This Thesis focuses on a high-level framework proposal for heterogeneous aerial, fixed wing teams of robots, which operate in complex coastal areas. Recent advances in the computational capabilities of modern processors along with the decrement of small scale aerial platform manufacturing costs, have given researchers the opportunity to propose efficient and low-cost solutions to a wide variety of problems. Regarding marine sciences and more generally coastal or sea operations, the use of aerial robots brings forth a number of advantages, including information redundancy and operator safety. This Thesis initially deals with complex coastal decomposition in relation with a vehicles’ on-board sensor. This decomposition decreases the computational complexity of planning a flight path, while respecting various aerial or ground restrictions. The sensor-based area decomposition also facilitates a team-wide heterogeneous solution for any team of aerial vehicles. Then, it proposes a novel algorithmic approach of partitioning any given complex area, for an arbitrary number of Unmanned Aerial Vehicles (UAV). This partitioning schema, respects the relative flight autonomy capabilities of the robots, providing them a corresponding region of interest. In addition, a set of algorithms is proposed for obtaining coverage waypoint plans for those areas. These algorithms are designed to afford the non-holonomic nature of fixed-wing vehicles and the restrictions their dynamics impose. Moreover, this Thesis also proposes a variation of a well-known path tracking algorithm, in order to further reduce the flight error of waypoint following, by introducing intermediate waypoints and providing an autopilot parametrisation. Finally, a marine studies test case of buoy information extraction is presented, demonstrating in that manner the flexibility and modular nature of the proposed framework.Esta tesis se centra en la propuesta de un marco de alto nivel para equipos heterogéneos de robots de ala fija que operan en áreas costeras complejas. Los avances recientes en las capacidades computacionales de los procesadores modernos, junto con la disminución de los costes de fabricación de plataformas aéreas a pequeña escala, han brindado a los investigadores la oportunidad de proponer soluciones eficientes y de bajo coste para enfrentar un amplio abanico de cuestiones. Con respecto a las ciencias marinas y, en términos más generales, a las operaciones costeras o marítimas, el uso de robots aéreos conlleva una serie de ventajas, incluidas la redundancia de la información y la seguridad del operador. Esta tesis trata inicialmente con la descomposición de áreas costeras complejas en relación con el sensor a bordo de un vehículo. Esta descomposición disminuye la complejidad computacional de la planificación de una trayectoria de vuelo, al tiempo que respeta varias restricciones aéreas o terrestres. La descomposición del área basada en sensores también facilita una solución heterogénea para todo el equipo para cualquier equipo de vehículos aéreos. Luego, propone un novedoso enfoque algorítmico de partición de cualquier área compleja dada, para un número arbitrario de vehículos aéreos no tripulados (UAV). Este esquema de partición respeta las capacidades relativas de autonomía de vuelo de los robots, proporcionándoles una región de interés correspondiente. Además, se propone un conjunto de algoritmos para obtener planes de puntos de cobertura para esas áreas. Estos algoritmos están diseñados teniendo en cuenta la naturaleza no holonómica de los vehículos de ala fija y las restricciones que impone su dinámica. En ese sentido, esta Tesis también ofrece una variación de un algoritmo de seguimiento de rutas bien conocido, con el fin de reducir aún más el error de vuelo del siguiente punto de recorrido, introduciendo puntos intermedios y proporcionando una parametrización del piloto automático. Finalmente, se presenta un caso de prueba de estudios marinos de extracción de información de boyas, que demuestra de esa manera la flexibilidad y el carácter modular del marco propuesto

    Multi-Robot Persistent Coverage in Complex Environments

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    Los recientes avances en robótica móvil y un creciente desarrollo de robots móviles asequibles han impulsado numerosas investigaciones en sistemas multi-robot. La complejidad de estos sistemas reside en el diseño de estrategias de comunicación, coordinación y controlpara llevar a cabo tareas complejas que un único robot no puede realizar. Una tarea particularmente interesante es la cobertura persistente, que pretende mantener cubierto en el tiempo un entorno con un equipo de robots moviles. Este problema tiene muchas aplicaciones como aspiración o limpieza de lugares en los que la suciedad se acumula constantemente, corte de césped o monitorización ambiental. Además, la aparición de vehículos aéreos no tripulados amplía estas aplicaciones con otras como la vigilancia o el rescate.Esta tesis se centra en el problema de cubrir persistentemente entornos progresivamente mas complejos. En primer lugar, proponemos una solución óptima para un entorno convexo con un sistema centralizado, utilizando programación dinámica en un horizonte temporalnito. Posteriormente nos centramos en soluciones distribuidas, que son más robustas, escalables y eficientes. Para solventar la falta de información global, presentamos un algoritmo de estimación distribuido con comunicaciones reducidas. Éste permite a los robots teneruna estimación precisa de la cobertura incluso cuando no intercambian información con todos los miembros del equipo. Usando esta estimación, proponemos dos soluciones diferentes basadas en objetivos de cobertura, que son los puntos del entorno en los que más se puedemejorar dicha cobertura. El primer método es un controlador del movimiento que combina un término de gradiente con un término que dirige a los robots hacia sus objetivos. Este método funciona bien en entornos convexos. Para entornos con algunos obstáculos, el segundométodo planifica trayectorias abiertas hasta los objetivos, que son óptimas en términos de cobertura. Finalmente, para entornos complejos no convexos, presentamos un algoritmo capaz de encontrar particiones equitativas para los robots. En dichas regiones, cada robotplanifica trayectorias de longitud finita a través de un grafo de caminos de tipo barrido.La parte final de la tesis se centra en entornos discretos, en los que únicamente un conjunto finito de puntos debe que ser cubierto. Proponemos una estrategia que reduce la complejidad del problema separándolo en tres subproblemas: planificación de trayectoriascerradas, cálculo de tiempos y acciones de cobertura y generación de un plan de equipo sin colisiones. Estos subproblemas más pequeños se resuelven de manera óptima. Esta solución se utiliza en último lugar para una novedosa aplicación como es el calentamiento por inducción doméstico con inductores móviles. En concreto, la adaptamos a las particularidades de una cocina de inducción y mostramos su buen funcionamiento en un prototipo real.Recent advances in mobile robotics and an increasing development of aordable autonomous mobile robots have motivated an extensive research in multi-robot systems. The complexity of these systems resides in the design of communication, coordination and control strategies to perform complex tasks that a single robot can not. A particularly interesting task is that of persistent coverage, that aims to maintain covered over time a given environment with a team of robotic agents. This problem is of interest in many applications such as vacuuming, cleaning a place where dust is continuously settling, lawn mowing or environmental monitoring. More recently, the apparition of useful unmanned aerial vehicles (UAVs) has encouraged the application of the coverage problem to surveillance and monitoring. This thesis focuses on the problem of persistently covering a continuous environment in increasingly more dicult settings. At rst, we propose a receding-horizon optimal solution for a centralized system in a convex environment using dynamic programming. Then we look for distributed solutions, which are more robust, scalable and ecient. To deal with the lack of global information, we present a communication-eective distributed estimation algorithm that allows the robots to have an accurate estimate of the coverage of the environment even when they can not exchange information with all the members of the team. Using this estimation, we propose two dierent solutions based on coverage goals, which are the points of the environment in which the coverage can be improved the most. The rst method is a motion controller, that combines a gradient term with a term that drives the robots to the goals, and which performs well in convex environments. For environments with some obstacles, the second method plans open paths to the goals that are optimal in terms of coverage. Finally, for complex, non-convex environments we propose a distributed algorithm to nd equitable partitions for the robots, i.e., with an amount of work proportional to their capabilities. To cover this region, each robot plans optimal, nite-horizon paths through a graph of sweep-like paths. The nal part of the thesis is devoted to discrete environment, in which only a nite set of points has to be covered. We propose a divide-and-conquer strategy to separate the problem to reduce its complexity into three smaller subproblem, which can be optimally solved. We rst plan closed paths through the points, then calculate the optimal coverage times and actions to periodically satisfy the coverage required by the points, and nally join together the individual plans of the robots into a collision-free team plan that minimizes simultaneous motions. This solution is eventually used for a novel application that is domestic induction heating with mobile inductors. We adapt it to the particular setting of a domestic hob and demonstrate that it performs really well in a real prototype.<br /
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