Modelling and Verification of Multiple UAV Mission Using SMV

Model checking has been used to verify the correctness of digital circuits, security protocols, communication protocols, as they can be modelled by means of finite state transition model. However, modelling the behaviour of hybrid systems like UAVs in a Kripke model is challenging. This work is aimed at capturing the behaviour of an UAV performing cooperative search mission into a Kripke model, so as to verify it against the temporal properties expressed in Computation Tree Logic (CTL). SMV model checker is used for the purpose of model checking

An efficient distributed area division method for cooperative monitoring applications with multiple uavs

This article addresses the area division problem in a distributed manner providing a solution for cooperative monitoring missions with multiple UAVs. Starting from a sub-optimal area division, a distributed online algorithm is presented to accelerate the convergence of the system to the optimal solution, following a frequency-based approach. Based on the “coordination variables” concept and on a strict neighborhood relation to share information (left, right, above and below neighbors), this technique defines a distributed division protocol to determine coherently the size and shape of the sub-area assigned to each UAV. Theoretically, the convergence time of the proposed solution depends linearly on the number of UAVs. Validation results, comparing the proposed approach with other distributed techniques, are provided to evaluate and analyze its performance following a convergence time criterion.European Union’s Horizon 2020 AERIAL-CORE Project Grant 871479CDTI (sPAIN) “Red Cervera” Programme iMOV3D Spanish R&D projec

Design of an UAV swarm

This master thesis tries to give an overview on the general aspects involved in the design of an UAV swarm. UAV swarms are continuoulsy gaining popularity amongst researchers and UAV manufacturers, since they allow greater success rates in task accomplishing with reduced times. Appart from this, multiple UAVs cooperating between them opens a new field of missions that can only be carried in this way. All the topics explained within this master thesis will explain all the agents involved in the design of an UAV swarm, from the communication protocols between them, navigation and trajectory analysis and task allocation

Unmanned vehicles formation control in 3D space and cooperative search

The first problem considered in this dissertation is the decentralized non-planar formation control of multiple unmanned vehicles using graph rigidity. The three-dimensional formation control problem consists of n vehicles operating in a plane Q and r vehicles that operate in an upper layer outside of the plane Q. This can be referred to as a layered formation control where the objective is for all vehicles to cooperatively acquire a predefined formation shape using a decentralized control law. The proposed control strategy is based on regulating the inter-vehicle distances and uses backstepping and Lyapunov approaches. Three different models, with increasing level of complexity are considered for the multi-vehicle system: the single integrator vehicle model, the double integrator vehicle model, and a model that represents the dynamics of a class of robotics vehicles including wheeled mobile robots, underwater vehicles with constant depth, aircraft with constant altitude, and marine vessels. A rigorous stability analysis is presented that guarantees convergence of the inter-vehicle distances to desired values. Additionally, a new Neural Network (NN)-based control algorithm that uses graph rigidity and relative positions of the vehicles is proposed to solve the formation control problem of unmanned vehicles in 3D space. The control law for each vehicle consists of a nonlinear component that is dependent on the closed-loop error dynamics plus a NN component that is linear in the output weights (a one-tunable layer NN is used). A Lyapunov analysis shows that the proposed distance-based control strategy achieves the uniformly ultimately bounded stability of the desired infinitesimally and minimally rigid formation and that NN weights remain bounded. Simulation results are included to demonstrate the performance of the proposed method. The second problem addressed in this dissertation is the cooperative unmanned vehicles search. In search and surveillance operations, deploying a team of unmanned vehicles provides a robust solution that has multiple advantages over using a single vehicle in efficiency and minimizing exploration time. The cooperative search problem addresses the challenge of identifying target(s) in a given environment when using a team of unmarried vehicles by proposing a novel method of mapping and movement of vehicle teams in a cooperative manner. The approach consists of two parts. First, the region is partitioned into a hexagonal beehive structure in order to provide equidistant movements in every direction and to allow for more natural and flexible environment mapping. Additionally, in search environments that are partitioned into hexagons, the vehicles have an efficient travel path while performing searches due to this partitioning approach. Second, a team of unmanned vehicles that move in a cooperative manner and utilize the Tabu Random algorithm is used to search for target(s). Due to the ever-increasing use of robotics and unmanned systems, the field of cooperative multi-vehicle search has developed many applications recently that would benefit from the use of the approach presented in this dissertation, including: search and rescue operations, surveillance, data collection, and border patrol. Simulation results are presented that show the performance of the Tabu Random search algorithm method in combination with hexagonal partitioning

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

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