1,279 research outputs found
Roving vehicle motion control Quarterly report, 1 Mar. - 31 May 1967
System and subsystem requirements for remote control of roving space vehicle motio
Field Testing of a Stochastic Planner for ASV Navigation Using Satellite Images
We introduce a multi-sensor navigation system for autonomous surface vessels
(ASV) intended for water-quality monitoring in freshwater lakes. Our mission
planner uses satellite imagery as a prior map, formulating offline a
mission-level policy for global navigation of the ASV and enabling autonomous
online execution via local perception and local planning modules. A significant
challenge is posed by the inconsistencies in traversability estimation between
satellite images and real lakes, due to environmental effects such as wind,
aquatic vegetation, shallow waters, and fluctuating water levels. Hence, we
specifically modelled these traversability uncertainties as stochastic edges in
a graph and optimized for a mission-level policy that minimizes the expected
total travel distance. To execute the policy, we propose a modern local planner
architecture that processes sensor inputs and plans paths to execute the
high-level policy under uncertain traversability conditions. Our system was
tested on three km-scale missions on a Northern Ontario lake, demonstrating
that our GPS-, vision-, and sonar-enabled ASV system can effectively execute
the mission-level policy and disambiguate the traversability of stochastic
edges. Finally, we provide insights gained from practical field experience and
offer several future directions to enhance the overall reliability of ASV
navigation systems.Comment: 33 pages, 20 figures. Project website https://pcctp.github.io. arXiv
admin note: text overlap with arXiv:2209.1186
Intuitive 3D Maps for MAV Terrain Exploration and Obstacle Avoidance
Recent development showed that Micro Aerial Vehicles (MAVs) are nowadays capable of autonomously take off at one point and land at another using only one single camera as exteroceptive sensor. During the flight and landing phase the MAV and user have, however, little knowledge about the whole terrain and potential obstacles. In this paper we show a new solution for a real-time dense 3D terrain reconstruction. This can be used for efficient unmanned MAV terrain exploration and yields a solid base for standard autonomous obstacle avoidance algorithms and path planners. Our approach is based on a textured 3D mesh on sparse 3D point features of the scene. We use the same feature points to localize and control the vehicle in the 3D space as we do for building the 3D terrain reconstruction mesh. This enables us to reconstruct the terrain without significant additional cost and thus in real-time. Experiments show that the MAV is easily guided through an unknown, GPS denied environment. Obstacles are recognized in the iteratively built 3D terrain reconstruction and are thus well avoide
An Autonomous Surface Vehicle for Long Term Operations
Environmental monitoring of marine environments presents several challenges:
the harshness of the environment, the often remote location, and most
importantly, the vast area it covers. Manual operations are time consuming,
often dangerous, and labor intensive. Operations from oceanographic vessels are
costly and limited to open seas and generally deeper bodies of water. In
addition, with lake, river, and ocean shoreline being a finite resource,
waterfront property presents an ever increasing valued commodity, requiring
exploration and continued monitoring of remote waterways. In order to
efficiently explore and monitor currently known marine environments as well as
reach and explore remote areas of interest, we present a design of an
autonomous surface vehicle (ASV) with the power to cover large areas, the
payload capacity to carry sufficient power and sensor equipment, and enough
fuel to remain on task for extended periods. An analysis of the design and a
discussion on lessons learned during deployments is presented in this paper.Comment: In proceedings of MTS/IEEE OCEANS, 2018, Charlesto
Autonomous aerial robot for high-speed search and intercept applications
In recent years, high-speed navigation and environment interaction in the context of
aerial robotics has become a field of interest for several academic and industrial research studies. In
particular, Search and Intercept (SaI) applications for aerial robots pose a compelling research
area due to their potential usability in several environments. Nevertheless, SaI tasks involve a
challenging development regarding sensory weight, onboard computation resources, actuation design,
and algorithms for perception and control, among others. In this work, a fully autonomous aerial
robot for high-speed object grasping has been proposed. As an additional subtask, our system is able
to autonomously pierce balloons located in poles close to the surface. Our first contribution is the
design of the aerial robot at an actuation and sensory level consisting of a novel gripper design with
additional sensors enabling the robot to grasp objects at high speeds. The second contribution is
a complete software framework consisting of perception, state estimation, motion planning, motion
control, and mission control in order to rapidly and robustly perform the autonomous grasping
mission. Our approach has been validated in a challenging international competition and has shown
outstanding results, being able to autonomously search, follow, and grasp a moving object at 6 m/s
in an outdoor environment.Agencia Estatal de InvestigaciónKhalifa Universit
WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmark for Autonomous Driving on Water Surfaces
Autonomous driving on water surfaces plays an essential role in executing
hazardous and time-consuming missions, such as maritime surveillance, survivors
rescue, environmental monitoring, hydrography mapping and waste cleaning. This
work presents WaterScenes, the first multi-task 4D radar-camera fusion dataset
for autonomous driving on water surfaces. Equipped with a 4D radar and a
monocular camera, our Unmanned Surface Vehicle (USV) proffers all-weather
solutions for discerning object-related information, including color, shape,
texture, range, velocity, azimuth, and elevation. Focusing on typical static
and dynamic objects on water surfaces, we label the camera images and radar
point clouds at pixel-level and point-level, respectively. In addition to basic
perception tasks, such as object detection, instance segmentation and semantic
segmentation, we also provide annotations for free-space segmentation and
waterline segmentation. Leveraging the multi-task and multi-modal data, we
conduct numerous experiments on the single modality of radar and camera, as
well as the fused modalities. Results demonstrate that 4D radar-camera fusion
can considerably enhance the robustness of perception on water surfaces,
especially in adverse lighting and weather conditions. WaterScenes dataset is
public on https://waterscenes.github.io
Software Porting of a 3D Reconstruction Algorithm to Razorcam Embedded System on Chip
A method is presented to calculate depth information for a UAV navigation system from Keypoints in two consecutive image frames using a monocular camera sensor as input and the OpenCV library. This method was first implemented in software and run on a general-purpose Intel CPU, then ported to the RazorCam Embedded Smart-Camera System and run on an ARM CPU onboard the Xilinx Zynq-7000. The results of performance and accuracy testing of the software implementation are then shown and analyzed, demonstrating a successful port of the software to the RazorCam embedded system on chip that could potentially be used onboard a UAV with tight constraints of size, weight, and power. The potential impacts will be seen through the continuation of this research in the Smart ES lab at University of Arkansas
Vision-Based navigation system for unmanned aerial vehicles
Mención Internacional en el título 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 Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Carlo Regazzoni.- Secretario: Fernando García Fernández.- Vocal: Pascual Campoy Cerver
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