Vetrella, Amedeo Rodi
(2017)
Cooperation and Autonomy for UAV Swarms.
[Tesi di dottorato]
| Item Type: |
Tesi di dottorato
|
| Lingua: |
English |
| Title: |
Cooperation and Autonomy for UAV Swarms |
| Creators: |
| Creators | Email |
|---|
| Vetrella, Amedeo Rodi | avetrella@gmail.com |
|
| Date: |
10 December 2017 |
| Number of Pages: |
177 |
| Institution: |
Università degli Studi di Napoli Federico II |
| Department: |
dep11 |
| Dottorato: |
phd046 |
| Ciclo di dottorato: |
30 |
| Coordinatore del Corso di dottorato: |
| nome | email |
|---|
| Grassi, Michele | michele.grassi@unina.it |
|
| Tutor: |
| nome | email |
|---|
| Accardo, Domenico | UNSPECIFIED | | Fasano, Giancarmine | UNSPECIFIED |
|
| Date: |
10 December 2017 |
| Number of Pages: |
177 |
| Uncontrolled Keywords: |
Cooperative Navigation; Unmanned Aerial Vehicle (UAV); Data Fusion; Kalman Filtering |
| Settori scientifico-disciplinari del MIUR: |
Area 09 - Ingegneria industriale e dell'informazione > ING-IND/05 - Impianti e sistemi aerospaziali |
[error in script]
[error in script]
| Date Deposited: |
06 Jan 2018 14:08 |
| Last Modified: |
26 Mar 2019 10:05 |
| URI: |
http://www.fedoa.unina.it/id/eprint/12137 |
Abstract
In the last few years, the level of autonomy of mini- and micro-Unmanned Aerial Vehicles (UAVs) has increased thanks to the miniaturization of flight control systems and payloads, and the availability of computationally affordable algorithms for autonomous Guidance Navigation and Control (GNC). However, despite the technological evolution, operations conducted by a single micro-UAV still present limits in terms of performance, coverage and reliability.
The scope of this thesis is to overcome single-UAV limits by developing new distributed GNC architectures and technologies where the cooperative nature of a UAV formation is exploited to obtain navigation information. Moreover, this thesis aims at increasing UAVs autonomy by developing a take-off and landing technique which permits to complete fully autonomous operations, also taking into account regulations and the required level of safety. Indeed, in addition to the typical performance limitations of micro-UAVs, this thesis takes into account also those applications where a multi-vehicle architecture can improve coverage and reliability, and allow real time data fusion. Furthermore, considering the low cost of micro-UAV systems with consumer grade avionics, having several UAVs can be more cost effective than equipping a single vehicle with high performance equipment.
Among several research challenges to be addressed in order to design and operate a distributed system of vehicles working together for real time applications, this thesis focuses on the following topics regarding cooperation and
autonomy:
Improvement of UAV navigation performance: This research topic aims at improving the navigation performance of an UAV flying cooperatively with one or more UAVs, considering that the only integration of low cost
inertial measurement units (IMUs), Global Navigation Satellite Systems
(GNSS) and magnetometers allows real time stabilization and flight control
but may not be suitable for applications requiring fine sensor pointing.
The focus is set on outdoor environments and it is assumed that all vehicles
of the formation are flying under nominal Global Positioning System
(GPS) coverage, hence, the main navigation improvement is in terms
of attitude estimation. In particular, the key concept is to exploit Differential
GPS (DGPS) among vehicles and vision-based tracking to build
a virtual additional navigation sensor whose information is then integrated
within a sensor fusion algorithm based on an Extended Kalman
Filter (EKF). Both numerical simulations and flight results show the potential
of sub-degree angular accuracy. In particular, proper formation
geometries, and even relatively small baselines, allow achieving a heading
uncertainty that can approach 0.1°, which represents a very important result
taking into account typical performance levels of IMUs onboard small
UAVs.
UAV navigation in GPS challenging environments: This research topic
aims at developing algorithms for improving navigation performance of
UAVs flying in GPS-challenging environments (e.g. natural or urban
canyons, or mixed outdoor-indoor settings), where GPS measurements
can be unavailable and/or unreliable. These algorithms exploit aiding
measurements from one or more cooperative UAVs flying under nominal
GPS coverage and are based on the concepts of relative sensing and information
sharing. The developed sensor fusion architecture is based on
a tightly coupled EKF that integrates measurements from onboard inertial
sensors and magnetometers, the available GPS pseudoranges, position
information from cooperative UAVs, and line-of-sight information derived
by visual sensors. In addition, if available, measurements coming from a
monocular pose estimation algorithm can be integrated within the developed
EKF in order to counteract the position error drift. Results show
that aiding measurements from a single cooperative UAV do not allow
eliminating position error drift. However, combining this approach with
a standalone visual-SLAM, integrating valid pseudoranges in the tightly
coupled filtering structure, or exploiting ad hoc commanded motion of
the cooperative vehicle under GPS coverage drastically reduces the position
error drift keeping meter-level positioning accuracy also in absence of
reliable GPS observables.
Autonomous take-off and landing: This research activity, conducted during
a 6 month Academic Guest period at ETH Zürich, focuses on increasing reliability,
versatility and flight time of UAVs, by developing an autonomous
take-off and landing technique. Often, the landing phase is the most critical
as it involves performing delicate maneuvers; e.g., landing on a station
for recharging or on a ground carrier for transportation. These procedures
are subject to constraints on time and space and must be robust to changes
in environmental conditions. These problems are addressed in this thesis,
where a guidance approach, based on the intrinsic Tau guidance theory, is
integrated within the end-to-end software developed at ETH Zürich. This
method has been validated both in simulations and through real platform
experiments by using rotary-wing UAVs to land on static platforms.
Results show that this method achieves smooth landings within 10 cm
accuracy, with easily adjustable trajectory parameters.
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