UAVs mission planning with flight level constraint using Fast Marching Square Method

In the last decade, Unmanned Aerial Vehicles (UAVs) have been a research focus for many purposes. Many of these studies require a path planning to perform autonomous flights, as well as the maintenance of a fixed flight level with respect to the ground to capture videos or overlying images. This article presents an approach to plan a mission for UAVs keeping a fixed flight level constraint. The 3D environment where the planning is carried out is an open field with non-uniform terrain. The approach proposed is based on the Fast Marching Square (FM ) method, which generates a path free from obstacles. Our approach includes two adjustment parameters. Depending on the values of these parameters, the restriction of flight level can be modified, as well as the smoothness and safety of the generated paths. Simulated experiments carried out in this work demonstrate that the proposed approach generates trajectories respecting a fixed flight level over the ground with successful results.The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos, fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU.Publicad

Unmanned Aerial Vehicle (UAV) mission planning based on Fast Marching Square (FM²) planner and Differential Evolution (DE)

Nowadays, mission planning for Unmanned Aerial Vehicles (UAVs) is a very attractive research field. UAVs have been a research focus for many purposes. In military and civil fields, the UAVs are very used for different missions. Many of these studies require a path planning to perform autonomous flights. Several problems related to the physical limitations of the UAV arise when the planning is carried out, as well as the maintenance of a fixed flight level with respect to the ground to capture videos or overlying images. This work presents an approach to plan missions for UAVs keeping a fixed flight level constraint. An approach is proposed to solve these problems and to generate effective paths in terms of smoothness and safety distance in two different types of environments: 1) 3D urban environments and 2) open field with non-uniform terrain environments. Many proposed activities to be carried out by UAVs in whatever the environment require a control over the altitude for different purposes: energy saving and minimization of costs are some of these objectives. In general terms, the planning is required to avoid all obstacles encountered in the environment and to maintain a fixed flight level during the path execution. For this reason, a mission planning requires robust planning methods. The method used in this work as planner is the Fast Marching Square (FM2) method, which generates a path free of obstacles. As a novelty, the method proposed includes two adjustment parameters. Depending on the values of these parameters, the restriction of flight level can be modified, as well as the smoothness and safety margins from the obstacles of the generated paths. The Dubins airplane model is used to check if the path resulting from the FM2 is feasible according to the constraints of the UAV: its turning rate, climb rate and cruise speed. Besides, this research also presents a novel approach for missions of Coverage Path Planning (CPP) carried out by UAVs in 3D environments. These missions are focused on path planning to cover a certain area in an environment in order to carry out tracking, search or rescue tasks. The methodology followed uses an optimization process based on the Differential Evolution (DE) algorithm in combination with the FM2 planner. Finally, the UAVs formation problem is introduced and addressed in a first stage using the planner proposed in this thesis. A wide variety of simulated experiments have been carried out to illustrate the efficiency and robustness of the approaches presented, obtaining successful results in different urban and open field 3D environments.Hoy en día la planificación de misiones para vehículos aéreos no tripulados (UAV) es un campo de investigación muy atractivo. Los UAV son foco de investigación en numerosas aplicaciones, tanto en el campo civil como militar. Muchas de estas aplicaciones requieren de un sistema de planificación de ruta que permita realizar vuelos autónomos y afrontar problemas relacionados con las limitaciones físicas del UAV y con requerimientos como el nivel de vuelo sobre el suelo para, entre otras funciones, poder capturar videos o imágenes. Este trabajo presenta una propuesta de planificador para vehículos aéreos no tripulados que permite resolver los problemas citados previamente, incluyendo en la planificación las consideraciones cinemáticas del UAV y las restricciones de nivel de vuelo, generando rutas suaves, realizables y suficientemente seguras para dos tipos diferentes de entornos 3D: 1) entornos urbanos y 2) campos abiertos con terrenos no uniformes. El método utilizado en esta tesis como base para la planificación es el método Fast Marching Square (FM2), que genera un camino libre de obstáculos. Como novedad, el método propuesto incluye dos parámetros de ajuste. Dependiendo de los valores de estos parámetros, se puede modificar la restricción de nivel de vuelo, así como la suavidad y los márgenes de seguridad respecto a los obstáculos de las rutas generadas. El modelo cinemático de Dubins se utiliza para verificar si la ruta resultante de nuestro planificador es realizable de acuerdo con las restricciones del UAV: su velocidad de giro, velocidad de ascenso y velocidad de crucero. Además, esta tesis también presenta una propuesta novedosa para la planificación de misiones de Coverage Path Planning (CPP) en entornos 3D. Estas misiones se centran en la planificación de rutas para cubrir un área determinada de un entorno con el fin de llevar a cabo tareas de rastreo, búsqueda o rescate. La metodología seguida utiliza un proceso de optimización basado en el algoritmo Differential Evolution (DE) en combinación con nuestro planificador FM2. Como parte final de la tesis, el problema de formación de UAVs se introduce y aborda en una primera etapa utilizando el planificador FM2 propuesto.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Antonio Giménez Fernández.- Secretario: Luis Santiago Garrido Bullón.- Vocal: Raúl Suárez Feijó

UAVs mission planning with imposition of flight level through fast marching square

Many proposed activities to be carried out by unmanned aerial vehicles (UAVs) in urban environments require a control over the altitude for different purposes. Energy saving and minimization of costs are some of these objectives. This work presents a method to impose a flight level in a mission planning carried out by a UAV in a 3D urban environment. The planning avoids all obstacles encountered in the environment and maintains a fixed flight level in the majority of the trajectory. The method used as planner is the Fast Marching Square (FM2) method, which includes two adjustment parameters. Depending on the values of these parameters, it is possible to introduce into the planning an altitude constraint, as well as to modify the smoothness of the trajectory and the safety margins from the obstacles. Several simulated experiments have been carried out in different situations obtaining very good results.The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos, fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

UAVs formation approach using fast marching square methods

This article presents a novel method for the management of UAVs formations. Based on the fast marching square (FM2) technique, the proposed method allows the generation of soft realizable paths for a formation in leader-followers configuration, keeping a desired geometry among its different agents. The solution presented here also allows the UAVs formation to adapt its shape so that the obstacles can be avoided, at the same time that a flight level can be fixed with respect to the ground. Simulation results will be presented in different environments to show the validity and robustness of the approach.This research was supported by RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos, Fase IV; S2018/NMT-4331), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

Path planning and collision risk management strategy for multi-UAV systems in 3D environments

This article belongs to the Special Issue Smooth Motion Planning for Autonomous VehiclesMulti-UAV systems are attracting, especially in the last decade, the attention of researchers and companies of very different fields due to the great interest in developing systems capable of operating in a coordinated manner in complex scenarios and to cover and speed up applications that can be dangerous or tedious for people: search and rescue tasks, inspection of facilities, delivery of goods, surveillance, etc. Inspired by these needs, this work aims to design, implement and analyze a trajectory planning and collision avoidance strategy for multi-UAV systems in 3D environments. For this purpose, a study of the existing techniques for both problems is carried out and an innovative strategy based on Fast Marching Square¿for the planning phase¿and a simple priority-based speed control¿as the method for conflict resolution¿is proposed, together with prevention measures designed to try to limit and reduce the greatest number of conflicting situations that may occur between vehicles while they carry out their missions in a simulated 3D urban environment. The performance of the algorithm is evaluated successfully on the basis of certain conveniently chosen statistical measures that are collected throughout the simulation runs.This research was funded by the EUROPEAN COMMISSION: Innovation and Networks Executive Agency (INEA), through the European H2020 LABYRINTH project. Grant agreement H2020-MG-2019-TwoStages-861696

Coverage mission for UAVs using differential evolution and fast marching square methods

This research presents a novel approach for missions of coverage path planning (CPP) carried out by unmanned aerial vehicles (UAVs) in a three-dimensional environment. These missions are focused on path planning to cover a certain area in an environment in order to carry out tracking, search, or rescue tasks. The methodology followed uses an optimization process based on the differential evolution (DE) algorithm in combination with the Fast Marching Square (FM2) planner. The DE algorithm evaluates a cost function to determine what the zigzag path with the minimum cost is, according to the steering angle of the zigzag bands (alfa). This optimization process allows achieving the most optimal zigzag path in terms of distance traveled by the UAV to cover the whole area. Then, the FM2 method is applied to generate the final path according to the steering angle of the zigzag bands resulting from the DE algorithm. The approach generates a feasible path free from obstacles, keeping a fixed altitude flight over the ground. The flight level, smoothness, and safety of the path can be modified by two adjustment parameters included in our approach. Simulated experiments carried out in this work demonstrate that the proposed approach generates the most optimal zigzag path in terms of distance, safety, and smoothness to cover a certain whole area, keeping a determined flight level with successful results.This work was supported by the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos, fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

Angle-Encoded Swarm Optimization for UAV Formation Path Planning

© 2018 IEEE. This paper presents a novel and feasible path planning technique for a group of unmanned aerial vehicles (DAVs) conducting surface inspection of infrastructure. The ultimate goal is to minimise the travel distance of DAVs while simultaneously avoid obstacles, and maintain altitude constraints as well as the shape of the UAV formation. A multiple-objective optimisation algorithm, called the Angle-encoded Particle Swarm Optimization (θ- PSO) algorithm, is proposed to accelerate the swarm convergence with angular velocity and position being used for the location of particles. The whole formation is modelled as a virtual rigid body and controlled to maintain a desired geometric shape among the paths created while the centroid of the group follows a pre-determined trajectory. Based on the testbed of 3DR Solo drones equipped with a proprietary Mission Planner, and the Internet-of- Things (loT) for multi-directional transmission and reception of data between the DAV s, extensive experiments have been conducted for triangular formation maintenance along a monorail bridge. The results obtained confirm the feasibility and effectiveness of the proposed approach

Gaussian Processes for Machine Learning in Robotics

Mención Internacional en el título de doctorNowadays, machine learning is widely used in robotics for a variety of tasks such as perception, control, planning, and decision making. Machine learning involves learning, reasoning, and acting based on the data. This is achieved by constructing computer programs that process the data, extract useful information or features, make predictions to infer unknown properties, and suggest actions to take or decisions to make. This computer program corresponds to a mathematical model of the data that describes the relationship between the variables that represent the observed data and properties of interest. The aforementioned model is learned based on the available training data, which is accomplished using a learning algorithm capable of automatically adjusting the parameters of the model to agree with the data. Therefore, the architecture of the model needs to be selected accordingly, which is not a trivial task and usually depends on the machine-learning engineer’s insights and past experience. The number of parameters to be tuned varies significantly with the selected machine learning model, ranging from two or three parameters for Gaussian processes (GP) to hundreds of thousands for artificial neural networks. However, as more complex and novel robotic applications emerge, data complexity increases and prior experience may be insufficient to define adequate mathematical models. In addition, traditional machine learning methods are prone to problems such as overfitting, which can lead to inaccurate predictions and catastrophic failures in critical applications. These methods provide probabilistic distributions as model outputs, allowing for estimating the uncertainty associated with predictions and making more informed decisions. That is, they provide a mean and variance for the model responses. This thesis focuses on the application of machine learning solutions based on Gaussian processes to various problems in robotics, with the aim of improving current methods and providing a new perspective. Key areas such as trajectory planning for unmanned aerial vehicles (UAVs), motion planning for robotic manipulators and model identification of nonlinear systems are addressed. In the field of path planning for UAVs, algorithms based on Gaussian processes that allow for more efficient planning and energy savings in exploration missions have been developed. These algorithms are compared with traditional analytical approaches, demonstrating their superiority in terms of efficiency when using machine learning. Area coverage and linear coverage algorithms with UAV formations are presented, as well as a sea surface search algorithm. Finally, these algorithms are compared with a new method that uses Gaussian processes to perform probabilistic predictions and optimise trajectory planning, resulting in improved performance and reduced energy consumption. Regarding motion planning for robotic manipulators, an approach based on Gaussian process models that provides a significant reduction in computational times is proposed. A Gaussian process model is used to approximate the configuration space of a robot, which provides valuable information to avoid collisions and improve safety in dynamic environments. This approach is compared to conventional collision checking methods and its effectiveness in terms of computational time and accuracy is demonstrated. In this application, the variance provides information about dangerous zones for the manipulator. In terms of creating models of non-linear systems, Gaussian processes also offer significant advantages. This approach is applied to a soft robotic arm system and UAV energy consumption models, where experimental data is used to train Gaussian process models that capture the relationships between system inputs and outputs. The results show accurate identification of system parameters and the ability to make reliable future predictions. In summary, this thesis presents a variety of applications of Gaussian processes in robotics, from trajectory and motion planning to model identification. These machine learning-based solutions provide probabilistic predictions and improve the ability of robots to perform tasks safely and efficiently. Gaussian processes are positioned as a powerful tool to address current challenges in robotics and open up new possibilities in the field.El aprendizaje automático ha revolucionado el campo de la robótica al ofrecer una amplia gama de aplicaciones en áreas como la percepción, el control, la planificación y la toma de decisiones. Este enfoque implica desarrollar programas informáticos que pueden procesar datos, extraer información valiosa, realizar predicciones y ofrecer recomendaciones o sugerencias de acciones. Estos programas se basan en modelos matemáticos que capturan las relaciones entre las variables que representan los datos observados y las propiedades que se desean analizar. Los modelos se entrenan utilizando algoritmos de optimización que ajustan automáticamente los parámetros para lograr un rendimiento óptimo. Sin embargo, a medida que surgen aplicaciones robóticas más complejas y novedosas, la complejidad de los datos aumenta y la experiencia previa puede resultar insuficiente para definir modelos matemáticos adecuados. Además, los métodos de aprendizaje automático tradicionales son propensos a problemas como el sobreajuste, lo que puede llevar a predicciones inexactas y fallos catastróficos en aplicaciones críticas. Para superar estos desafíos, los métodos probabilísticos de aprendizaje automático, como los procesos gaussianos, han ganado popularidad. Estos métodos ofrecen distribuciones probabilísticas como salidas del modelo, lo que permite estimar la incertidumbre asociada a las predicciones y tomar decisiones más informadas. Esto es, proporcionan una media y una varianza para las respuestas del modelo. Esta tesis se centra en la aplicación de soluciones de aprendizaje automático basadas en procesos gaussianos a diversos problemas en robótica, con el objetivo de mejorar los métodos actuales y proporcionar una nueva perspectiva. Se abordan áreas clave como la planificación de trayectorias para vehículos aéreos no tripulados (UAVs), la planificación de movimientos para manipuladores robóticos y la identificación de modelos de sistemas no lineales. En el campo de la planificación de trayectorias para UAVs, se han desarrollado algoritmos basados en procesos gaussianos que permiten una planificación más eficiente y un ahorro de energía en misiones de exploración. Estos algoritmos se comparan con los enfoques analíticos tradicionales, demostrando su superioridad en términos de eficiencia al utilizar el aprendizaje automático. Se presentan algoritmos de recubrimiento de áreas y recubrimiento lineal con formaciones de UAVs, así como un algoritmo de búsqueda en superficies marinas. Finalmente, estos algoritmos se comparan con un nuevo método que utiliza procesos gaussianos para realizar predicciones probabilísticas y optimizar la planificación de trayectorias, lo que resulta en un rendimiento mejorado y una reducción del consumo de energía. En cuanto a la planificación de movimientos para manipuladores robóticos, se propone un enfoque basado en modelos gaussianos que permite una reducción significativa en los tiempos de cálculo. Se utiliza un modelo de procesos gaussianos para aproximar el espacio de configuraciones de un robot, lo que proporciona información valiosa para evitar colisiones y mejorar la seguridad en entornos dinámicos. Este enfoque se compara con los métodos convencionales de planificación de movimientos y se demuestra su eficacia en términos de tiempo de cálculo y precisión de los movimientos. En esta aplicación, la varianza proporciona información sobre zonas peligrosas para el manipulador. En cuanto a la identificación de modelos de sistemas no lineales, los procesos gaussianos también ofrecen ventajas significativas. Este enfoque se aplica a un sistema de brazo robótico blando y a modelos de consumo energético de UAVs, donde se utilizan datos experimentales para entrenar un modelo de proceso gaussiano que captura las relaciones entre las entradas y las salidas del sistema. Los resultados muestran una identificación precisa de los parámetros del sistema y la capacidad de realizar predicciones futuras confiables. En resumen, esta tesis presenta una variedad de aplicaciones de procesos gaussianos en robótica, desde la planificación de trayectorias y movimientos hasta la identificación de modelos. Estas soluciones basadas en aprendizaje automático ofrecen predicciones probabilísticas y mejoran la capacidad de los robots para realizar tareas de manera segura y eficiente. Los procesos gaussianos se posicionan como una herramienta poderosa para abordar los desafíos actuales en robótica y abrir nuevas posibilidades en el campo.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Juan Jesús Romero Cardalda.- Secretaria: María Dolores Blanco Rojas.- Vocal: Giuseppe Carbon

Autonomous navigation for UAVs managing motion and sensing uncertainty

We present a motion planner for the autonomous navigation of UAVs that manages motion and sensing uncertainty at planning time. By doing so, optimal paths in terms of probability of collision, traversal time and uncertainty are obtained. Moreover, our approach takes into account the real dimensions of the UAV in order to reliably estimate the probability of collision from the predicted uncertainty. The motion planner relies on a graduated fidelity state lattice and a novel multi-resolution heuristic which adapt to the obstacles in the map. This allows managing the uncertainty at planning time and yet obtaining solutions fast enough to control the UAV in real time. Experimental results show the reliability and the efficiency of our approach in different real environments and with different motion models. Finally, we also report planning results for the reconstruction of 3D scenarios, showing that with our approach the UAV can obtain a precise 3D model autonomouslyThis research was funded by the Spanish Ministry for Science, Innovation, Spain and Universities (grant TIN2017-84796-C2-1-R) and the Galician Ministry of Education, University and Professional Training, Spain (grants ED431C 2018/29 and “accreditation 2016–2019, ED431G/08”). These grants were co-funded by the European Regional Development Fund (ERDF/FEDER program)S