Visual-based SLAM configurations for cooperative multi-UAV systems with a lead agent: an observability-based approach

In this work, the problem of the cooperative visual-based SLAM for the class of multi-UA systems that integrates a lead agent has been addressed. In these kinds of systems, a team of aerial robots flying in formation must follow a dynamic lead agent, which can be another aerial robot, vehicle or even a human. A fundamental problem that must be addressed for these kinds of systems has to do with the estimation of the states of the aerial robots as well as the state of the lead agent. In this work, the use of a cooperative visual-based SLAM approach is studied in order to solve the above problem. In this case, three different system configurations are proposed and investigated by means of an intensive nonlinear observability analysis. In addition, a high-level control scheme is proposed that allows to control the formation of the UAVs with respect to the lead agent. In this work, several theoretical results are obtained, together with an extensive set of computer simulations which are presented in order to numerically validate the proposal and to show that it can perform well under different circumstances (e.g., GPS-challenging environments). That is, the proposed method is able to operate robustly under many conditions providing a good position estimation of the aerial vehicles and the lead agent as well.Peer ReviewedPostprint (published version

Leader-Follower Control and Distributed Communication based UAV Swarm Navigation in GPS-Denied Environment

Unmanned Aerial Vehicles (UAVs) have developed rapidly in recent years due to technological advances and UAV technology finds applications in a wide range of fields, including surveillance, search and rescue, and agriculture. The utilization of UAV swarms in these contexts offers numerous advantages, increasing their value across different industries. These advantages include increased efficiency in tasks, enhanced productivity, greater safety, and the higher data quality. The coordination of UAVs becomes particularly crucial during missions in these applications, especially when drones are flying in close proximity as part of a swarm. For instance, if a drone swarm is targeted or needs to navigate through a Global Positioning System (GPS)-denied environment, it may encounter challenges in obtaining the location information typically provided by GPS. This poses a new challenge for the UAV swarms to maintain a reliable formation and successfully complete a given mission. In this article, our objective is to minimize the number of sensors required on each UAV and reduce the amount of information exchanged between UAVs. This approach aims to ensure the reliable maintenance of UAV formations with minimal communication requirements among UAVs while they follow predetermined trajectories during swarm missions. In this paper, we introduce a concept that utilizes extended Kalman filter, leader-follower-based control and a distributed data-sharing scheme to ensure the reliable and safe maintenance of formations and navigation autonomously for UAV swarm missions in GPS-denied environments. The formation control approaches and control strategies for UAV swarms are also discussed

Communication-based UAV Swarm Missions

Unmanned aerial vehicles have developed rapidly in recent years due to technological advances. UAV technology can be applied to a wide range of applications in surveillance, rescue, agriculture and transport. The problems that can exist in these areas can be mitigated by combining clusters of drones with several technologies. For example, when a swarm of drones is under attack, it may not be able to obtain the position feedback provided by the Global Positioning System (GPS). This poses a new challenge for the UAV swarm to fulfill a specific mission. This thesis intends to use as few sensors as possible on the UAVs and to design the smallest possible information transfer between the UAVs to maintain the shape of the UAV formation in flight and to follow a predetermined trajectory. This thesis presents Extended Kalman Filter methods to navigate autonomously in a GPS-denied environment. The UAV formation control and distributed communication methods are also discussed and given in detail

A Solution for the Efficient Takeoff and Flight Coordination of UAV Swarms

[ES] En la última década, hemos asistido a un gran aumento del uso de los VANTs, debido principalmente a los avances en tecnología y materiales. Hoy en día, los VANTs ya no son solo juguetes para el entretenimiento, sino también importantes activos para muchas empresas. Los VANTs son muy versátiles y, por ello, existen muchas y variadas aplicaciones: misiones de búsqueda y rescate, vigilancia de fronteras, inspección térmica de tuberías, cinematografía y agricultura de precisión, solo por nombrar algunas. En estos momentos en que las industrias están incorporando soluciones basadas en VANTs, es crucial que la investigación avance. El cambio más destacado (con respecto a los VANTs) que presenciaremos en esta década, es el despliegue de grupos de VANTs trabajando en colaboración para cumplir un objetivo superior. Estos grupos, también llamados enjambres de drones, permiten realizar tareas más complejas, de forma más eficiente, o con mayor redundancia. Sin embargo, existen retos inherentes al funcionamiento de un enjambre de VANTs. Debe existir una buena comunicación entre los VANTs, deben evitarse las colisiones y los VANTs individuales deben utilizarse de forma inteligente para aumentar la eficiencia global. En este trabajo fin de master se da solución a algunos de los principales problemas relativos a los enjambres de vehículos aéreos no tripulados. En primer lugar, diseñamos varios patrones de formación de enjambres ´útiles. A continuación, incorporamos esas formaciones en dos procedimientos de despegue - una heurística y un algoritmo ya existente (KMA) - los cuales se prueban ampliamente para decidir cual es el más adecuado para despegar un enjambre de VANTs de la manera más eficiente. Una vez que somos capaces de despegar de forma sincronizada y segura un enjambre completo, continuamos nuestra investigación proporcionando una solución para mantener ese enjambre organizado, y estable durante una misión pre-planificada. Nuestra solución incorpora mecanismos para proporcionar resiliencia al enjambre, de tal manera que todos y cada uno de los VANTs pueden abandonar el enjambre (en pleno vuelo), sin perturbar a los demás en su misión.[EN] In the last decade, we have seen a great increase in the use of Unmanned Aerial Vehicles (UAVs). This is mainly due to advances in technology and materials. Nowadays, UAVs are no longer only toys for entertainment, but also important assets for many enterprises. UAVs are versatile, and thus many diverse applications exist: search and rescue missions, border surveillance, thermal pipeline inspection, cinematography, and precision agriculture, just to name a few. Now that the industry is incorporating UAVs based solutions, it is crucial that research advances. The most prominent change (with respect to UAVs) that we will witness in this decade, is the deployment of groups of UAVs working collaboratively to fulfill a higher goal. Those groups, also called swarms, allow us to perform more complex tasks, more efficiently, or with more redundancy. However, there are inherent challenges while operating a swarm of UAVs: there must be a good communication channel between the UAVs, collisions must be avoided, and the individual UAVs should be used intelligently in order to increase the overall efficiency. In this master thesis, a solution is given for some of the main problems concerning Unmanned Aerial Vehicle (UAV) swarms. First, we lay out various useful swarm formation patterns. Then we incorporate those formations in two takeoff procedures - an heuristic and an existing algorithm (KuhnMunkres algorithm (KMA)) - which are extensively tested to decide which one is the most appropriate for the takeoff of a swarm of UAVs in the most efficient manner. Once we are able to take off an entire swarm, we continue our research by providing a solution to keep that swarm organized and stable during a pre-planned mission. Such solution incorporates mechanisms to provide resilience to the swarm in such a manner that any number of UAVs can be removed from the swarm (mid-flight) without disturbing the others in their mission.Wubben, J. (2021). A Solution for the Efficient Takeoff and Flight Coordination of UAV Swarms. Universitat Politècnica de València. http://hdl.handle.net/10251/172620TFG

AR.Drone UAV control parameters tuning based on particle swarm optimization algorithm

In this paper, a proposed particle swarm optimization called multi-objective particle swarm optimization (MOPSO) with an accelerated update methodology is employed to tune Proportional-Integral-Derivative (PID) controller for an AR.Drone quadrotor. The proposed approach is to modify the velocity formula of the general PSO systems in order for improving the searching efficiency and actual execution time. Three PID control parameters, i.e., the proportional gain K-p, integral gain K-i and derivative gain K-d are required to form a parameter vector which is considered as a particle of PSO. To derive the optimal PID parameters for the Ar.Drone, the modified update method is employed to move the positions of all particles in the population. In the meanwhile, multi-objective functions defined for PID controller optimization problems are minimized. The results verify that the proposed MOPSO is able to perform appropriately in Ar.Drone control system

A cooperative navigation system with distributed architecture for multiple unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have been widely used in many applications due to, among other features, their versatility, reduced operating cost, and small size. These applications increasingly demand that features related to autonomous navigation be employed, such as mapping. However, the reduced capacity of resources such as, for example, battery and hardware (memory and processing units) can hinder the development of these applications in UAVs. Thus, the collaborative use of multiple UAVs for mapping can be used as an alternative to solve this problem, with a cooperative navigation system. This system requires that individual local maps be transmitted and merged into a global map in a distributed manner. In this scenario, there are two main problems to be addressed: the transmission of maps among the UAVs and the merging of the local maps in each UAV. In this context, this work describes the design, development, and evaluation of a cooperative navigation system with distributed architecture to be used by multiple UAVs. This system uses proposed structures to store the 3D occupancy grid maps. Furthermore, maps are compressed and transmitted between UAVs using algorithms specially proposed for these purposes. Then the local 3D maps are merged in each UAV. In this map merging system, maps are processed before and merged in pairs using suitable algorithms to make them compatible with the 3D occupancy grid map data. In addition, keypoints orientation properties are obtained from potential field gradients. Some proposed filters are used to improve the parameters of the transformations among maps. To validate the proposed solution, simulations were performed in six different environments, outdoors and indoors, and with different layout characteristics. The obtained results demonstrate the effectiveness of thesystemin the construction, sharing, and merging of maps. Still, from the obtained results, the extreme complexity of map merging systems is highlighted.Os veículos aéreos não tripulados (VANTs) têm sidoamplamenteutilizados em muitas aplicações devido, entre outrosrecursos,à sua versatilidade, custo de operação e tamanho reduzidos. Essas aplicações exigem cadavez mais que recursos relacionados à navegaçãoautônoma sejam empregados,como o mapeamento. No entanto, acapacidade reduzida de recursos como, por exemplo, bateria e hardware (memória e capacidade de processamento) podem atrapalhar o desenvolvimento dessas aplicações em VANTs.Assim, o uso colaborativo de múltiplosVANTs para mapeamento pode ser utilizado como uma alternativa para resolvereste problema, criando um sistema de navegaçãocooperativo. Estesistema requer que mapas locais individuais sejam transmitidos efundidos em um mapa global de forma distribuída.Nesse cenário, há doisproblemas principais aserem abordados:a transmissão dosmapas entre os VANTs e afusão dos mapas locais em cada VANT. Nestecontexto, estatese apresentao projeto, desenvolvimento e avaliaçãode um sistema de navegação cooperativo com arquitetura distribuída para ser utilizado pormúltiplos VANTs. Este sistemausa estruturas propostas para armazenaros mapasdegradedeocupação 3D. Além disso, os mapas são compactados e transmitidos entre os VANTs usando os algoritmos propostos. Em seguida, os mapas 3D locais são fundidos em cada VANT. Neste sistemade fusão de mapas, os mapas são processados antes e juntados em pares usando algunsalgoritmos adequados para torná-los compatíveiscom os dados dos mapas da grade de ocupação 3D. Além disso, as propriedadesde orientação dos pontoschave são obtidas a partir de gradientes de campos potenciais. Alguns filtros propostos são utilizadospara melhorar as indicações dos parâmetros dastransformações entre mapas. Paravalidar a aplicação proposta, foram realizadas simulações em seis ambientes distintos, externos e internos, e com características construtivas distintas. Os resultados apresentados demonstram a efetividade do sistema na construção, compartilhamento e fusão dos mapas. Ainda, a partir dos resultados obtidos, destaca-se a extrema complexidade dos sistemas de fusão de mapas

Adaptive Learning Terrain Estimation for Unmanned Aerial Vehicle Applications

For the past decade, terrain mapping research has focused on ground robots using occupancy grids and tree-like data structures, like Octomap and Quadtrees. Since flight vehicles have different constraints, ground-based terrain mapping research may not be directly applicable to the aerospace industry. To address this issue, Adaptive Learning Terrain Estimation algorithms have been developed with an aim towards aerospace applications. This thesis develops and tests Adaptive Learning Terrain Estimation algorithms using a custom test benchmark on representative aerospace cases: autonomous UAV landing and UAV flight through 3D urban environments. The fundamental objective of this thesis is to investigate the use of Adaptive Learning Terrain Estimation algorithms for aerospace applications and compare their performance to commonly used mapping techniques such as Quadtree and Octomap. To test the algorithms, point clouds were collected and registered in simulation and real environments. Then, the Adaptive Learning, Quadtree, and Octomap algorithms were applied to the data sets, both in real-time and offline. Finally, metrics of map size, accuracy, and running time were developed and implemented to quantify and compare the performance of the algorithms. The results show that Quadtree yields the computationally lightest maps, but it is not suitable for real-time implementation due to its lack of recursiveness. Adaptive Learning maps are computationally efficient due to the use of multiresolution grids. Octomap yields the most detailed maps, but it produces a high computational load. The results of the research show that Adaptive Learning algorithms have significant potential for real-time implementation in aerospace applications. Their low memory load and variable-sized grids make them viable candidates for future research and development

Navigation and Guidance for Autonomous Quadcopter Drones Using Deep Learning on Indoor Corridors

Autonomous drones require accurate navigation and localization algorithms to carry out their duties. Outdoors drones can utilize GPS for navigation and localization systems. However, GPS is often unreliable or not available at all indoors. Therefore, in this research, an autonomous indoor drone navigation model was created using a deep learning algorithm, to assist drone navigation automatically, especially in indoor corridor areas. In this research, only the Caddx Ratel 2 FPV camera mounted on the drone was used as an input for the deep learning model to navigate the drone forward without a collision with the wall in the corridor. This research produces two deep learning models, namely, a rotational model to overcome a drone's orientation deviations with a loss of 0.0010 and a mean squared error of 0.0009, and a translation model to overcome a drone's translation deviation with a loss of 0.0140 and a mean squared error of 0.011. The implementation of the two models on autonomous drones reaches an NCR value of 0.2. The conclusion from the results obtained in this research is that the difference in resolution and FOV value in the actual image captured by the FPV camera on the drone with the image used for training the deep learning model results in a discrepancy in the output value during the implementation of the deep learning model on autonomous drones and produces low NCR implementation values

TRAJECTORY GENERATION BASED GUIDANCE AND CONTROL OF ROTORCRAFT UNMANNED AERIAL VEHICLES

Ph.DDOCTOR OF PHILOSOPH