Autonomous Quadrocopter for Search, Count and Localization of Objects

This chapter describes and evaluates the design and implementation of a new fully autonomous quadrocopter, which is capable of self‐reliant search, count and localization of a predefined object on the ground inside a room

Autonomous Quadrocopter for Indoor Exploration, AQopterI8, Research and Development

Diese Forschungsarbeit beschreibt alle Aspekte der Entwicklung eines neuartigen, autonomen Quadrokopters, genannt AQopterI8, zur Innenraumerkundung. Dank seiner einzigartigen modularen Komposition von Soft- und Hardware ist der AQopterI8 in der Lage auch unter widrigen Umweltbedingungen autonom zu agieren und unterschiedliche Anforderungen zu erfüllen. Die Arbeit behandelt sowohl theoretische Fragestellungen unter dem Schwerpunkt der einfachen Realisierbarkeit als auch Aspekte der praktischen Umsetzung, womit sie Themen aus den Gebieten Signalverarbeitung, Regelungstechnik, Elektrotechnik, Modellbau, Robotik und Informatik behandelt. Kernaspekt der Arbeit sind Lösungen zur Autonomie, Hinderniserkennung und Kollisionsvermeidung. Das System verwendet IMUs (Inertial Measurement Unit, inertiale Messeinheit) zur Orientierungsbestimmung und Lageregelung und kann unterschiedliche Sensormodelle automatisch detektieren. Ultraschall-, Infrarot- und Luftdrucksensoren in Kombination mit der IMU werden zur Höhenbestimmung und Höhenregelung eingesetzt. Darüber hinaus werden bildgebende Sensoren (Videokamera, PMD), ein Laser-Scanner sowie Ultraschall- und Infrarotsensoren zur Hindernis-erkennung und Kollisionsvermeidung (Abstandsregelung) verwendet. Mit Hilfe optischer Sensoren kann der Quadrokopter basierend auf Prinzipien der Bildverarbeitung Objekte erkennen sowie seine Position im Raum bestimmen. Die genannten Subsysteme im Zusammenspiel erlauben es dem AQopterI8 ein Objekt in einem unbekannten Raum autonom, d.h. völlig ohne jedes externe Hilfsmittel, zu suchen und dessen Position auf einer Karte anzugeben. Das System kann Kollisionen mit Wänden vermeiden und Personen autonom ausweichen. Dabei verwendet der AQopterI8 Hardware, die deutlich günstiger und Dank der Redundanz gleichzeitig erheblich verlässlicher ist als vergleichbare Mono-Sensor-Systeme (z.B. Kamera- oder Laser-Scanner-basierte Systeme). Neben dem Zweck als Forschungsarbeit (Dissertation) dient die vorliegende Arbeit auch als Dokumentation des Gesamtprojektes AQopterI8, dessen Ziel die Erforschung und Entwicklung neuartiger autonomer Quadrokopter zur Innenraumerkundung ist. Darüber hinaus wird das System zum Zweck der Lehre und Forschung an der Universität Würzburg, der Fachhochschule Brandenburg sowie der Fachhochschule Würzburg-Schweinfurt eingesetzt. Darunter fallen Laborübungen und 31 vom Autor dieser Arbeit betreute studentische Bachelor- und Masterarbeiten. Das Projekt wurde ausgezeichnet vom Universitätsbund und der IHK Würzburg-Mainfranken mit dem Universitätsförderpreis der Mainfränkischen Wirtschaft und wird gefördert unter den Bezeichnungen „Lebensretter mit Propellern“ und „Rettungshelfer mit Propellern“. Außerdem wurde die Arbeit für den Gips-Schüle-Preis nominiert. Absicht dieser Projekte ist die Entwicklung einer Rettungsdrohne. In den Medien Zeitung, Fernsehen und Radio wurde über den AQopterI8 schon mehrfach berichtet. Die Evaluierung zeigt, dass das System in der Lage ist, voll autonom in Innenräumen zu fliegen, Kollisionen mit Objekten zu vermeiden (Abstandsregelung), eine Suche durchzuführen, Objekte zu erkennen, zu lokalisieren und zu zählen. Da nur wenige Forschungsarbeiten diesen Grad an Autonomie erreichen, gleichzeitig aber keine Arbeit die gestellten Anforderungen vergleichbar erfüllt, erweitert die Arbeit den Stand der Forschung.The following scientific work describes entirely the development of an innovative autonomous quadrotor, called AQopterI8, for indoor exploration and object search describing solutions in the fields of signal processing, control theory, software and hardware. The main topics are autonomy, collision avoidance and obstacle detection. The AQopterI8 uses an IMU (inertial measurement unit) for attitude control and together with ultrasonic, infrared and pressure sensors for height control. Furthermore ultrasonic, infrared, stereo vision, pmd (photonic mixing device) and laser sensors enable the quadrotor to fly autonomously, detect obstacles and avoid collisions (distance control). All ressources are on-Board and the quadrotor requires no external system such as GPS or OTS (optical tracking system) cameras. The evaluation shows, that the AQopterI8 can control its distance to moving obstacles such as persons, can fly through corridors and search, localize and count autonmously target objects. The work was marked with the university promotion prize from the chamber of commerce (IHK Mainfranken) and has been many times in the media such as newspaper, radio and television

A review on Distributed Control of Cooperating MINI UAVs

Mini Unmanned Aerial Vehicles (MUAVs) are becoming popular research platform and drawing considerable attention, particularly during the last decade due to their multi-dimensional applications in almost every walk of life. MUAVs range from simple toys found at electronic supermarkets for entertainment purpose to highly sophisticated commercial platforms performing novel assignments like offshore wind power station inspection and 3D modelling of buildings. This paper presents an overview of the main aspects in the domain of distributed control of cooperating MUAVs to facilitate the potential users in this fascinating field. Furthermore it gives an overview on state of the art in MUAV technologies e.g. Photonic Mixer Devices (PMD) camera, distributed control methods and on-going work and challenges, which is the motivation for many researchers all over the world to work in this field

An Autonomous UAV with an Optical Flow Sensor for Positioning and Navigation

A procedure to control all six DOF (degrees of freedom) of a UAV (unmanned aerial vehicle) without an external reference system and to enable fully autonomous flight is presented here. For 2D positioning the principle of optical flow is used. Together with the output of height estimation, fusing ultrasonic, infrared and inertial and pressure sensor data, the 3D position of the UAV can be computed, controlled and steered. All data processing is done on the UAV. An external computer with a pathway planning interface is for commanding purposes only. The presented system is part of the AQopterI8 project, which aims to develop an autonomous flying quadrocopter for indoor application. The focus of this paper is 2D positioning using an optical flow sensor. As a result of the performed evaluation, it can be concluded that for position hold, the standard deviation of the position error is 10cm and after landing the position error is about 30cm

Waypoint flight parameter comparison of an autonomous UAV

The present paper compares the effect of different waypoint parameters on the flight performance of a special autonomous indoor UAV (unmanned aerial vehicle) fusing ultrasonic, inertial, pressure and optical sensors for 3D positioning and controlling. The investigated parameters are the acceptance threshold for reaching a waypoint as well as the maximal waypoint step size or block size. The effect of these parameters on the flight time and accuracy of the flight path is investigated. Therefore the paper addresses how the acceptance threshold and step size influence the speed and accuracy of the autonomous flight and thus influence the performance of the presented autonomous quadrocopter under real indoor navigation circumstances. Furthermore the paper demonstrates a drawback of the standard potential field method for navigation of such autonomous quadrocopters and points to an improvement

Complementary Vision based Data Fusion for Robust Positioning and Directed Flight of an Autonomous Quadrocopter

The present paper describes an improved 4 DOF (x/y/z/yaw) vision based positioning solution for fully 6 DOF autonomous UAVs, optimised in terms of computation and development costs as well as robustness and performance. The positioning system combines Fourier transform-based image registration (Fourier Tracking) and differential optical flow computation to overcome the drawbacks of a single approach. The first method is capable of recognizing movement in four degree of freedom under variable lighting conditions, but suffers from low sample rate and high computational costs. Differential optical flow computation, on the other hand, enables a very high sample rate to gain control robustness. This method, however, is limited to translational movement only and performs poor in bad lighting conditions. A reliable positioning system for autonomous flights with free heading is obtained by fusing both techniques. Although the vision system can measure the variable altitude during flight, infrared and ultrasonic sensors are used for robustness. This work is part of the AQopterI8 project, which aims to develop an autonomous flying quadrocopter for indoor application and makes autonomous directed flight possible

Obstacle Detection and Collision Avoidance for a UAV with Complementary Low-Cost Sensors

This paper demonstrates an innovative and simple solution for obstacle detection and collision avoidance of unmanned aerial vehicles (UAVs) optimized for and evaluated with quadrotors. The sensors exploited in this paper are low-cost ultrasonic and infrared range finders, which are much cheaper though noisier than more expensive sensors such as laser scanners. This needs to be taken into consideration for the design, implementation, and parametrization of the signal processing and control algorithm for such a system, which is the topic of this paper. For improved data fusion, inertial and optical flow sensors are used as a distance derivative for reference. As a result, a UAV is capable of distance controlled collision avoidance, which is more complex and powerful than comparable simple solutions. At the same time, the solution remains simple with a low computational burden. Thus, memory and time-consuming simultaneous localization and mapping is not required for collision avoidance