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PhD Thesis

 

"Vision for UAVs: tracking, pose estimation and control"
by Carol V. Martinez
International honored PhD thesis which received a unanimous Summa Cum Laude from the panel in July 15th 2013

 

This thesis deals with the problem of developing robust vision algorithms that will solve comun vision problems such as tracking, pose estimation and vision-based control using a UAV as testbed.

These algorithms should be able to overcome problems such as UAV vibrations, changes in illumination due to outdoors operations, or large image displacements, among others. In this thesis two different sources of information will be explored, one using external vision system (trinocular system), and the other one using an on board vision system.

             Landing Platform for UAVs                         Robust Visual Tracking

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"Soft-Computing Based Visual Control for Unmanned Vehicles"
by Miguel A. Olivares
International honored PhD thesis which received a unanimous Summa Cum Laude from the panel in March 20th 2013


This Thesis exploits the use of Soft Computing to control unmanned vehicles using vision. This works goes beyond the typical control systems used in highly controlled environments, by demonstrating the power
of the Fuzzy Logic Controllers (FLCs) to command aerial and ground vehicles in a sort of different tasks. A huge amount of real tests are presented in which the implemented Fuzzy controllers manage a visual pan and tilt platform, a helicopter, a commercial car and two different types of quadcopters. The use of the Cross-Entropy method to optimize the behavior of these controllers is also shown.

All the visual servoing controllers presented in this Thesis were implemented using the self-developed software tool called MOFS (Miguel Olivares’ Fuzzy Software). Different visual algorithms were used to acquire the information of the surrounding environment of the vehicles. The CamShift, homography decomposition, and augmented reality mark detection among others. This visual information was used as input of the Fuzzy controllers to manage the vehicle to do different autonomous tasks.
The steering wheel of a commercial car was controlled to implement a driverless vehicle for inner-city tests. Long distance of more than 6 km was covered without driver in a close circuit using a vision line following algorithm. The limited field of view (5030 cm) of the system was not an impediment to reach a top speed of 48km/h and guide the vehicle inside low radius curves.

Static and moving objects like cars were tracking from an unmanned helicopter controlling an on board pan and tilt visual platform. A full control of altitude, lateral and forward movements was implemented for an auto-landing task of a helicopter. An implementation of pitch and heading controllers were used to command a quadrotor for object following task. The heading was also controlled for See and Avoid task with this type of UAVs. The Cross-Entropy optimization method is not wide used for control in the literature. This Thesis presents the way to optimize the gains, membership function sets’ position and size and the rules’ weight to improve the behavior of a Fuzzy controller. This optimization process was done using ROS and Matlab Simulink to obtain better results for See and Avoid tests for UAVs.

This Thesis demonstrates that the Fuzzy Logic Controllers are widely capable to command free-model systems in high disturbance environments with a low cost sensor. The noisy effects of illumination changes and the high uncertain of the visual detection were manage in a gentle way by this Soft Computing technique to approach different tasks with different aerial and ground vehicles.


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"Onboard Visual Control Algorithms for Unmanned Aerial Vehicles"

by Iván Fernando Mondragón Bernal

European honored PhD thesis which received a unanimous Summa Cum Laude from the panel in November 14th 2011

This thesis provide solutions to the control of UVA based mainly on visual information that increases the capabilities of this kind of vehicles. Some of the R&D areas involves, visual tracking and servoing, 3D pose and attitude estimation, image enhancement, video stabilization, mosaic building, among others. This thesis also involves the use of catadioptric systems for omnidirectional vision, useful for attitude estimation and "see and avoid" techniques. 
 
The thesis presents novel contributions on the fields os 3D pose and attitude estimation, as well as visual servoing on UAVs for applications on precision hovering, autonomous landing, object following and See&Avoid methods, based only on visual processing
.

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Attitude Estimation Using Omnidirectional Vision

IBVS for Object Following on Quadrotors 

PBVS based on 3D pose estimation using robust homographies for Autonomous landing

Video enhancement:

See&Avoid

Visual Tracking