8 research outputs found
A 10-gram Vision-based Flying Robot
We aim at developing ultralight autonomous microflyers capable of freely flying within houses or small built environments while avoiding collisions. Our latest prototype is a fixed-wing aircraft weighing a mere 10 g, flying around 1.5 m/s and carrying the necessary electronics for airspeed regulation and lateral collision avoidance. This microflyer is equipped with two tiny camera modules, two rate gyroscopes, an anemometer, a small microcontroller, and a Bluetooth radio module. Inflight tests are carried out in a new experimentation room specifically designed for easy changing of surrounding textures
Vision-based control of near-obstacle flight
Lightweight micro unmanned aerial vehicles (micro-UAVs) capable of autonomous flight in natural and urban environments have a large potential for civil and commercial applications, including environmental monitoring, forest fire monitoring, homeland security, traffic monitoring, aerial imagery, mapping and search and rescue. Smaller micro-UAVs capable of flying inside houses or small indoor environments have further applications in the domain of surveillance, search and rescue and entertainment. These applications require the capability to fly near to the ground and amongst obstacles. Existing UAVs rely on GPS and AHRS (attitude heading reference system) to control their flight and are unable to detect and avoid obstacles. Active distance sensors such as radars or laser range finders could be used to measure distances to obstacles, but are typically too heavy and power-consuming to be embedded on lightweight systems. In this thesis, we draw inspiration from biology and explore alternative approaches to flight control that allow aircraft to fly near obstacles. We show that optic flow can be used on flying platforms to estimate the proximity of obstacles and propose a novel control strategy, called optiPilot, for vision-based near-obstacle flight. Thanks to optiPilot, we demonstrate for the first time autonomous near-obstacle flight of micro-UAVs, both indoor and outdoor, without relying on an AHRS nor external beacons such as GPS. The control strategy only requires a small series of optic flow sensors, two rate gyroscopes and an airspeed sensor. It can run on a tiny embedded microcontroller in realtime. Despite its simplicity, optiPilot is able to fully control the aircraft, including altitude regulation, attitude stabilisation, obstacle avoidance, landing and take-off. This parsimony, inherited from the biology of flying insects, contrasts with the complexity of the systems used so far for flight control while offering more capabilities. The results presented in this thesis contribute to a better understanding of the minimal requirements, in terms of sensing and control architecture, that enable animals and artificial systems to fly and bring closer to reality the perspective of using lightweight and inexpensive micro-UAV for civilian purposes
Autonomous flight at low altitude using light sensors and little computational power
The ability to fly at low altitude while actively avoiding collisions with the terrain and objects such as trees and buildings is a great challenge for small unmanned aircraft. This paper builds on top of a control strategy called optiPilot whereby a series of optic-flow detectors pointed at divergent viewing directions around the aircraft main axis are linearly combined into roll and pitch commands using two sets of weights. This control strategy already proved successful at controlling flight and avoiding collisions in reactive navigation experiments. This paper describes how optiPilot can efficiently steer a flying platform during the critical phases of hand-launched take off and landing. It then shows how optiPilot can be coupled with a GPS in order to provide goal-directed, nap-of-the-earth flight control in presence of obstacles. Two fully autonomous flights of 25 minutes each are described where a 400-gram unmanned aircraft flies at approx. 10 m above ground in a circular path including two copses of trees requiring efficient collision avoidance actions
Obstacle avoidance based-visual navigation for micro aerial vehicles
This paper describes an obstacle avoidance system for low-cost Unmanned Aerial Vehicles (UAVs) using vision as the principal source of information through the monocular onboard camera. For detecting obstacles, the proposed system compares the image obtained in real time from the UAV with a database of obstacles that must be avoided. In our proposal, we include the feature point detector Speeded Up Robust Features (SURF) for fast obstacle detection and a control law to avoid them. Furthermore, our research includes a path recovery algorithm. Our method is attractive for compact MAVs in which other sensors will not be implemented. The system was tested in real time on a Micro Aerial Vehicle (MAV), to detect and avoid obstacles in an unknown controlled environment; we compared our approach with related works.Peer ReviewedPostprint (published version
Vision-Based navigation system for unmanned aerial vehicles
MenciĂłn Internacional en el tĂtulo de doctorThe main objective of this dissertation is to provide Unmanned Aerial Vehicles
(UAVs) with a robust navigation system; in order to allow the UAVs to perform
complex tasks autonomously and in real-time. The proposed algorithms deal with
solving the navigation problem for outdoor as well as indoor environments, mainly
based on visual information that is captured by monocular cameras. In addition,
this dissertation presents the advantages of using the visual sensors as the main
source of data, or complementing other sensors in providing useful information; in
order to improve the accuracy and the robustness of the sensing purposes.
The dissertation mainly covers several research topics based on computer vision
techniques: (I) Pose Estimation, to provide a solution for estimating the 6D pose of
the UAV. This algorithm is based on the combination of SIFT detector and FREAK
descriptor; which maintains the performance of the feature points matching and decreases
the computational time. Thereafter, the pose estimation problem is solved
based on the decomposition of the world-to-frame and frame-to-frame homographies.
(II) Obstacle Detection and Collision Avoidance, in which, the UAV is able to
sense and detect the frontal obstacles that are situated in its path. The detection
algorithm mimics the human behaviors for detecting the approaching obstacles; by
analyzing the size changes of the detected feature points, combined with the expansion
ratios of the convex hull constructed around the detected feature points
from consecutive frames. Then, by comparing the area ratio of the obstacle and the
position of the UAV, the method decides if the detected obstacle may cause a collision.
Finally, the algorithm extracts the collision-free zones around the obstacle,
and combining with the tracked waypoints, the UAV performs the avoidance maneuver.
(III) Navigation Guidance, which generates the waypoints to determine
the flight path based on environment and the situated obstacles. Then provide
a strategy to follow the path segments and in an efficient way and perform the
flight maneuver smoothly. (IV) Visual Servoing, to offer different control solutions (Fuzzy Logic Control (FLC) and PID), based on the obtained visual information; in
order to achieve the flight stability as well as to perform the correct maneuver; to
avoid the possible collisions and track the waypoints.
All the proposed algorithms have been verified with real flights in both indoor
and outdoor environments, taking into consideration the visual conditions; such as
illumination and textures. The obtained results have been validated against other
systems; such as VICON motion capture system, DGPS in the case of pose estimate
algorithm. In addition, the proposed algorithms have been compared with several
previous works in the state of the art, and are results proves the improvement in
the accuracy and the robustness of the proposed algorithms.
Finally, this dissertation concludes that the visual sensors have the advantages
of lightweight and low consumption and provide reliable information, which is
considered as a powerful tool in the navigation systems to increase the autonomy
of the UAVs for real-world applications.El objetivo principal de esta tesis es proporcionar Vehiculos Aereos no Tripulados
(UAVs) con un sistema de navegacion robusto, para permitir a los UAVs realizar
tareas complejas de forma autonoma y en tiempo real. Los algoritmos propuestos
tratan de resolver problemas de la navegacion tanto en ambientes interiores como
al aire libre basandose principalmente en la informacion visual captada por las camaras
monoculares. Ademas, esta tesis doctoral presenta la ventaja de usar sensores
visuales bien como fuente principal de datos o complementando a otros sensores
en el suministro de informacion util, con el fin de mejorar la precision y la
robustez de los procesos de deteccion.
La tesis cubre, principalmente, varios temas de investigacion basados en tecnicas
de vision por computador: (I) Estimacion de la Posicion y la Orientacion
(Pose), para proporcionar una solucion a la estimacion de la posicion y orientacion
en 6D del UAV. Este algoritmo se basa en la combinacion del detector SIFT y el
descriptor FREAK, que mantiene el desempeno del a funcion de puntos de coincidencia
y disminuye el tiempo computacional. De esta manera, se soluciona el
problema de la estimacion de la posicion basandose en la descomposicion de las
homografias mundo a imagen e imagen a imagen. (II) Deteccion obstaculos y elusion
colisiones, donde el UAV es capaz de percibir y detectar los obstaculos frontales
que se encuentran en su camino. El algoritmo de deteccion imita comportamientos
humanos para detectar los obstaculos que se acercan, mediante el analisis de la
magnitud del cambio de los puntos caracteristicos detectados de referencia, combinado
con los ratios de expansion de los contornos convexos construidos alrededor
de los puntos caracteristicos detectados en frames consecutivos. A continuacion,
comparando la proporcion del area del obstaculo y la posicion del UAV, el metodo
decide si el obstaculo detectado puede provocar una colision. Por ultimo, el algoritmo
extrae las zonas libres de colision alrededor del obstaculo y combinandolo
con los puntos de referencia, elUAV realiza la maniobra de evasion. (III) Guiado de navegacion, que genera los puntos de referencia para determinar la trayectoria de
vuelo basada en el entorno y en los obstaculos detectados que encuentra. Proporciona
una estrategia para seguir los segmentos del trazado de una manera eficiente
y realizar la maniobra de vuelo con suavidad. (IV) Guiado por Vision, para ofrecer
soluciones de control diferentes (Control de Logica Fuzzy (FLC) y PID), basados en
la informacion visual obtenida con el fin de lograr la estabilidad de vuelo, asi como
realizar la maniobra correcta para evitar posibles colisiones y seguir los puntos de
referencia.
Todos los algoritmos propuestos han sido verificados con vuelos reales en ambientes
exteriores e interiores, tomando en consideracion condiciones visuales como
la iluminacion y las texturas. Los resultados obtenidos han sido validados con otros
sistemas: como el sistema de captura de movimiento VICON y DGPS en el caso del
algoritmo de estimacion de la posicion y orientacion. Ademas, los algoritmos propuestos
han sido comparados con trabajos anteriores recogidos en el estado del arte
con resultados que demuestran una mejora de la precision y la robustez de los algoritmos
propuestos.
Esta tesis doctoral concluye que los sensores visuales tienen las ventajes de tener
un peso ligero y un bajo consumo y, proporcionar informacion fiable, lo cual lo
hace una poderosa herramienta en los sistemas de navegacion para aumentar la
autonomia de los UAVs en aplicaciones del mundo real.Programa Oficial de Doctorado en IngenierĂa ElĂ©ctrica, ElectrĂłnica y AutomáticaPresidente: Carlo Regazzoni.- Secretario: Fernando GarcĂa Fernández.- Vocal: Pascual Campoy Cerver