Autonomous Approach and Landing Algorithms for Unmanned Aerial Vehicles

In recent years, several research activities have been developed in order to increase the autonomy features in Unmanned Aerial Vehicles (UAVs), to substitute human pilots in dangerous missions or simply in order to execute specific tasks more efficiently and cheaply. In particular, a significant research effort has been devoted to achieve high automation in the landing phase, so as to allow the landing of an aircraft without human intervention, also in presence of severe environmental disturbances. The worldwide research community agrees with the opportunity of the dual use of UAVs (for both military and civil purposes), for this reason it is very important to make the UAVs and their autolanding systems compliant with the actual and future rules and with the procedures regarding autonomous flight in ATM (Air Traffic Management) airspace in addition to the typical military aims of minimizing fuel, space or other important parameters during each autonomous task. Developing autolanding systems with a desired level of reliability, accuracy and safety involves an evolution of all the subsystems related to the guide, navigation and control disciplines. The main drawbacks of the autolanding systems available at the state of art concern or the lack of adaptivity of the trajectory generation and tracking to unpredicted external events, such as varied environmental condition and unexpected threats to avoid, or the missed compliance with the guide lines imposed by certification authorities of the proposed technologies used to get the desired above mentioned adaptivity. During his PhD period the author contributed to the development of an autonomous approach and landing system considering all the indispensable functionalities like: mission automation logic, runway data managing, sensor fusion for optimal estimation of vehicle state, trajectory generation and tracking considering optimality criteria, health management algorithms. In particular the system addressed in this thesis is capable to perform a fully adaptive autonomous landing starting from any point of the three dimensional space. The main novel feature of this algorithm is that it generates on line, with a desired updating rate or at a specified event, the nominal trajectory for the aircraft, based on the actual state of the vehicle and on the desired state at touch down point. Main features of the autolanding system based on the implementation of the proposed algorithm are: on line trajectory re-planning in the landing phase, fully autonomy from remote pilot inputs, weakly instrumented landing runway (without ILS availability), ability to land starting from any point in the space and autonomous management of failures and/or adverse atmospheric conditions, decision-making logic evaluation for key-decisions regarding possible execution of altitude recovery manoeuvre based on the Differential GPS integrity signal and compatible with the functionalities made available by the future GNSS system. All the algorithms developed allow reducing computational tractability of trajectory generation and tracking problems so as to be suitable for real time implementation and to still obtain a feasible (for the vehicle) robust and adaptive trajectory for the UAV. All the activities related to the current study have been conducted at CIRA (Italian Aerospace Research Center) in the framework of the aeronautical TECVOL project whose aim is to develop innovative technologies for the autonomous flight. The autolanding system was developed by the TECVOL team and the author’s contribution to it will be outlined in the thesis. Effectiveness of proposed algorithms has been then evaluated in real flight experiments, using the aeronautical flying demonstrator available at CIRA

Data-driven control design for UAV autolanding : a pitch-only case study

This paper aims at exploring a new-type mode for autolanding control of fixed-wing unmanned aerial vehicles (UAVs). A discrete-time data-driven control scheme is tentatively proposed and developed with its pitch-only channel as a case study. Eventually, data-driven controllers inspired by attracting laws are introduced for a series of difference models. Numerical simulation for pitch-only dynamics is demonstrated to validate and compare performances of proposed DDC laws. Simulation results indicate that DDC laws can achieve the desired performance by altering data-driven models with different orders

First Approach to Coupling of Numerical Lifting-Line Theory and Linear Covariance Analysis for UAV State Uncertainty Propagation

Numerical lifting-line is a computationally efficient method for calculating aerodynamic forces and moments on aircraft. However, its potential has yet to be tapped for use in guidance, navigation, and control (GN&C). Linear covariance analysis is becoming a popular GN&C design tool and shows promise for pairing with numerical lifting-line. Pairing numerical lifting-line with linear covariance analysis allows for forward propagation of state uncertainty for real-time decision making. We demonstrate this for select state variables in a drone aerial recapture situation. Linear covariance analysis uses finite difference derivatives obtained from numerical lifting-line to calculate force and moment variances. These show agreement with Monte Carlo simulation results to within 10%, without the significant computational cost of Monte Carlo. These results show numerical lifting-line can be used in linear covariance analysis of an entire UAV GN&C solution. Not only does this allow for real-time uncertainty propagation, but also faster and more thorough multi-disciplinary design optimization

Synthesis and Hardware Implementation of an Unmanned Aerial Vehicle Automatic Landing System Utilizing Quantitative Feedback Theory

Approach and landing are among the most difficult flight regimes for automatic control of fixed-wing aircraft. Additional challenges are introduced when working with unmanned aerial vehicles, such as modelling uncertainty and limited gust tolerance. This thesis develops linear discrete-time automatic landing controllers using Quantitative Feedback Theory to ensure control robustness and adequate disturbance rejection. Controllers are developed in simulation and evaluated in flight tests of the low cost Easy Star remote-controlled platform. System identification of the larger Pegasus unmanned aerial vehicle is performed to identify dynamic models from flight data. A full set of controllers are subsequently developed and evaluated in simulation for the Pegasus. The extensive simulation and experimental testing with the Easy Star will reduce the time required to implement the Pegasus control laws, and will reduce the associated risk by validating the core experimental software. It is concluded that the control synthesis process using Quantitative Feedback Theory provides robust controllers with generally adequate performance, based on simulation and hardware results. The Quantitative Feedback Theory framework provides a good method for synthesizing the inner-loop controllers and satisfying performance requirements, but in many of the cases considered here it is found to be impractical for the outer loop designs. The primary recommendations of this work are: perform additional verification flights on the Easy Star; repeat Pegasus system identification for a landing configuration before flight testing the control laws; design and implement a rudder control loop on the Pegasus for control of the vehicle after touchdown

An Innovative Human Machine Interface for UAS Flight Management System

The thesis is relative to the development of an innovative Human Machine Interface for UAS Flight Management System. In particular, touchscreena have been selected as data entry interface. The thesis has been done together at Alenia Aermacch

Vision-based autonomous landing system for aerial vehicles

En este trabajo de fin de grado se tiene como fin el diseño de un sistema de aterrizaje autónomo, basando en odometría visual, para el vehículo aéreo no tripulado (UAV) Ardrone 2.0 de Parrot. Esta idea surge a partir de un proyecto del Departamento de Sistemas Inteligentes de la Universidad Carlos III de Madrid que está creando una aplicación que permita convertir este aparato en un sistema aéreo no tripulado (UAS), es decir, un drone con capacidad de realizar maniobras autónomas. Este proyecto se basa en crear un modulo de ampliación para esta aplicación, que permita al Ardrone aproximarse y aterrizar sobre una zona delimitada para tal efecto, basándose solo en la información obtenida a través de las cámaras integradas. Se utilizaran técnicas de visión por computador apoyadas en la plataforma Emgu CV, que a su vez está basada en las conocidas librerías de OpenCV. La navegación y aterrizaje se realizará con el envió de los comandos de navegación en tiempo real basados en la información obtenida al realizar el análisis de la imagen y la búsqueda del patrón predefinido.In this final degree project the objective is to design an autonomous landing system, basing on visual odometry, for an unmanned aerial vehicle (UAV) Parrot Ardrone 2.0. This idea comes from a project of the Department of Intelligent Systems in the University Carlos III de Madrid who are creating an application that allows converting this aircraft in an unmanned aerial system (UAS), in essence, a drone with the capacity to perform autonomous manoeuvres. This project builds on creating an expansion module for this application, allowing the Ardrone approaching and landing on a limited area for this purpose, based only on the information obtained from the integrated cameras. Vision by computer techniques are used supported in CV Emgu platform, which is based on the famous OpenCV library. Navigation and landing is performed with commands sent in real time navigation based on the information obtained from the image analysis and the search for predefined pattern.Ingeniería Electrónica Industrial y Automátic

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Autonomous UAS-Based Agriculture Applications: General Overview and Relevant European Case Studies

Emerging precision agriculture techniques rely on the frequent collection of high-quality data which can be acquired efficiently by unmanned aerial systems (UAS). The main obstacle for wider adoption of this technology is related to UAS operational costs. The path forward requires a high degree of autonomy and integration of the UAS and other cyber physical systems on the farm into a common Farm Management System (FMS) to facilitate the use of big data and artificial intelligence (AI) techniques for decision support. Such a solution has been implemented in the EU project AFarCloud (Aggregated Farming in the Cloud). The regulation of UAS operations is another important factor that impacts the adoption rate of agricultural UAS. An analysis of the new European UAS regulations relevant for autonomous operation is included. Autonomous UAS operation through the AFarCloud FMS solution has been demonstrated at several test farms in multiple European countries. Novel applications have been developed, such as the retrieval of data from remote field sensors using UAS and in situ measurements using dedicated UAS payloads designed for physical contact with the environment. The main findings include that (1) autonomous UAS operation in the agricultural sector is feasible once the regulations allow this; (2) the UAS should be integrated with the FMS and include autonomous data processing and charging functionality to offer a practical solution; and (3) several applications beyond just asset monitoring are relevant for the UAS and will help to justify the cost of this equipment.publishedVersio

Synthesis and Hardware Implementation of an Unmanned Aerial Vehicle Automatic Landing System Utilizing Quantitative Feedback Theory

Approach and landing are among the most difficult flight regimes for automatic control of fixed-wing aircraft. Additional challenges are introduced when working with unmanned aerial vehicles, such as modelling uncertainty and limited gust tolerance. This thesis develops linear discrete-time automatic landing controllers using Quantitative Feedback Theory to ensure control robustness and adequate disturbance rejection. Controllers are developed in simulation and evaluated in flight tests of the low cost Easy Star remote-controlled platform. System identification of the larger Pegasus unmanned aerial vehicle is performed to identify dynamic models from flight data. A full set of controllers are subsequently developed and evaluated in simulation for the Pegasus. The extensive simulation and experimental testing with the Easy Star will reduce the time required to implement the Pegasus control laws, and will reduce the associated risk by validating the core experimental software. It is concluded that the control synthesis process using Quantitative Feedback Theory provides robust controllers with generally adequate performance, based on simulation and hardware results. The Quantitative Feedback Theory framework provides a good method for synthesizing the inner-loop controllers and satisfying performance requirements, but in many of the cases considered here it is found to be impractical for the outer loop designs. The primary recommendations of this work are: perform additional verification flights on the Easy Star; repeat Pegasus system identification for a landing configuration before flight testing the control laws; design and implement a rudder control loop on the Pegasus for control of the vehicle after touchdown

Automated taxiing for unmanned aircraft systems

Over the last few years, the concept of civil Unmanned Aircraft System(s) (UAS) has been realised, with small UASs commonly used in industries such as law enforcement, agriculture and mapping. With increased development in other areas, such as logistics and advertisement, the size and range of civil UAS is likely to grow. Taken to the logical conclusion, it is likely that large scale UAS will be operating in civil airspace within the next decade. Although the airborne operations of civil UAS have already gathered much research attention, work is also required to determine how UAS will function when on the ground. Motivated by the assumption that large UAS will share ground facilities with manned aircraft, this thesis describes the preliminary development of an Automated Taxiing System(ATS) for UAS operating at civil aerodromes. To allow the ATS to function on the majority of UAS without the need for additional hardware, a visual sensing approach has been chosen, with the majority of work focusing on monocular image processing techniques. The purpose of the computer vision system is to provide direct sensor data which can be used to validate the vehicle s position, in addition to detecting potential collision risks. As aerospace regulations require the most robust and reliable algorithms for control, any methods which are not fully definable or explainable will not be suitable for real-world use. Therefore, non-deterministic methods and algorithms with hidden components (such as Artificial Neural Network (ANN)) have not been used. Instead, the visual sensing is achieved through a semantic segmentation, with separate segmentation and classification stages. Segmentation is performed using superpixels and reachability clustering to divide the image into single content clusters. Each cluster is then classified using multiple types of image data, probabilistically fused within a Bayesian network. The data set for testing has been provided by BAE Systems, allowing the system to be trained and tested on real-world aerodrome data. The system has demonstrated good performance on this limited dataset, accurately detecting both collision risks and terrain features for use in navigation