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

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 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

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

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

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

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

Design, development and guidance of the Airborne’s quadrotor

The main goal of the Airborne project is to develop, at technology readiness level 8 (TRL8), a few selected robotic aerial technologies for quick localization of victims by avalanches by equipping drones with two forefront sensors used in SAR operations in case of avalanches, namely the ARVA and RECCO. This thesis focuses on the design, development, and guidance of the TRL8 quadrotor developed during the project. We present and describe the design method that allowed us to obtain an EMI shielded UAV capable of integrating both RECCO and ARVA sensors. Besides, is presented the avionics and power train design and building procedure in order to obtain a modular UAV frame that can be easily carried by rescuers and achieves all the performance benchmarks of the project. Additionally, in addition to the onboard algorithms, a multivariate regressive convolutional neural network whose goal is the localization of the ARVA signal is presented. On guidance, the automatic flight procedure is described, and the onboard waypoint generator algorithm is presented. The goal of this algorithm is the generation and execution of an automatic grid pattern without the need to know the map in advance and without the support of a control ground station (CGS). Moreover, we present an iterative trajectory planner that does not need pre-knowledge of the map and uses Bézier curves to address optimal, dynamically feasible, safe, and re-plannable trajectories. The goal is to develop a method that allows local and fast replannings in case of an obstacle pop up or if some waypoints change. This makes the novel planner suitable to be applied in SAR operations. The introduction of the final version of the quadrotor is supported by internal flight tests and field tests performed in real operative scenarios by the Club Alpino Italiano (CAI)

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically