3,817 research outputs found
Tutorial on UAV: A Blue Sky View on Wireless Communication
The growing use of Unmanned Aerial Vehicles (UAVs) for various applications
requires ubiquitous and reliable connectivity for safe control and data
exchange between these devices and ground terminals. Depending on the
application, UAV-mounted wireless equipment can either be an aerial user
equipment (AUE) that co-exists with the terrestrial users, or it can be a part
of wireless infrastructure providing a range of services to the ground users.
For instance, AUE can be used for real-time search and rescue and Aerial Base
Station (ABS) can enhance coverage, capacity and energy efficiency of wireless
networks. In both cases, UAV-based solutions are scalable, mobile, fast to
deploy. However, several technical challenges have to be addressed. In this
work, we present a tutorial on wireless communication with UAVs, taking into
account a wide range of potential applications. The main goal of this work is
to provide a complete overview of the main scenarios (AUE and ABS), channel and
performance models, compare them, and discuss open research points. This work
gives a comprehensive overview of the research done until now and depicts a
comprehensive picture to foster new ideas and solutions while avoiding
duplication of past work. We start by discussing the open challenges of
wireless communication with UAVs. To give answers to the posed questions, we
focus on the UAV communication basics, mainly providing the necessary channel
modeling background and giving guidelines on how various channel models should
be used. Next, theoretical, simulation- and measurement-based approaches, to
address the key challenges for AUE usage, are presented. Moreover, in this
work, we aim to provide a comprehensive overview on how UAV-mounted equipment
can be used as a part of a communication network. Based on the theoretical
analysis, we show how various network parameters can be optimized.Comment: 42 pages, 32 Figure
Recent Developments in Aerial Robotics: A Survey and Prototypes Overview
In recent years, research and development in aerial robotics (i.e., unmanned
aerial vehicles, UAVs) has been growing at an unprecedented speed, and there is
a need to summarize the background, latest developments, and trends of UAV
research. Along with a general overview on the definition, types, categories,
and topics of UAV, this work describes a systematic way to identify 1,318
high-quality UAV papers from more than thirty thousand that have been appeared
in the top journals and conferences. On top of that, we provide a bird's-eye
view of UAV research since 2001 by summarizing various statistical information,
such as the year, type, and topic distribution of the UAV papers. We make our
survey list public and believe that the list can not only help researchers
identify, study, and compare their work, but is also useful for understanding
research trends in the field. From our survey results, we find there are many
types of UAV, and to the best of our knowledge, no literature has attempted to
summarize all types in one place. With our survey list, we explain the types
within our survey and outline the recent progress of each. We believe this
summary can enhance readers' understanding on the UAVs and inspire researchers
to propose new methods and new applications.Comment: 14 pages, 16 figures, typos correcte
Distributed Obstacle and Multi-Robot Collision Avoidance in Uncertain Environments
This paper tackles the distributed leader-follower (L-F) control problem for
heterogeneous mobile robots in unknown environments requiring obstacle
avoidance, inter-robot collision avoidance, and reliable robot communications.
To prevent an inter-robot collision, we employ a virtual propulsive force
between robots. For obstacle avoidance, we present a novel distributed
Negative-Imaginary (NI) variant formation tracking control approach and a
dynamic network topology methodology which allows the formation to change its
shape and the robot to switch their roles. In the case of communication or
sensor loss, a UAV, controlled by a Strictly-Negative-Imaginary (SNI)
controller with good wind resistance characteristics, is utilized to track the
position of the UGV formation using its camera. Simulations and indoor
experiments have been conducted to validate the proposed methods
Parameterized Optimal Trajectory Generation for Target Localization
This paper presents an approach to near-optimal target localization for small and mi-cro uninhabited aerial vehicles using a family of pre-computed parameterized trajectories. These trajectories are pre-computed for a set of nominal target locations uniformly dis-tributed over the sensor field of view and stored off-line. Upon target detection the vehicle chooses the trajectory corresponding to the closest nominal target location. Adaptation is enabled with the ability to select new trajectories as the target state estimate is updated. Simulation results show the validity of this approach for both single target and sequential target localization missions. Further, results show that very coarse trajectory tables give the same or better target localization performance as finely discretized tables. I
A Novel Potential Field Controller for Use on Aerial Robots
Unmanned Aerial Vehicles (UAV), commonly known as drones, have many potential
uses in real world applications. Drones require advanced planning and
navigation algorithms to enable them to safely move through and interact with
the world around them. This paper presents an extended potential field
controller (ePFC) which enables an aerial robot, or drone, to safely track a
dynamic target location while simultaneously avoiding any obstacles in its
path. The ePFC outperforms a traditional potential field controller (PFC) with
smoother tracking paths and shorter settling times. The proposed ePFC's
stability is evaluated by Lyapunov approach, and its performance is simulated
in a Matlab environment. Finally, the controller is implemented on an
experimental platform in a laboratory environment which demonstrates the
effectiveness of the controller
UAV Visual Teach and Repeat Using Only Semantic Object Features
We demonstrate the use of semantic object detections as robust features for
Visual Teach and Repeat (VTR). Recent CNN-based object detectors are able to
reliably detect objects of tens or hundreds of categories in a video at frame
rates. We show that such detections are repeatable enough to use as landmarks
for VTR, without any low-level image features. Since object detections are
highly invariant to lighting and surface appearance changes, our VTR can cope
with global lighting changes and local movements of the landmark objects. In
the teaching phase, we build a series of compact scene descriptors: a list of
detected object labels and their image-plane locations. In the repeating phase,
we use Seq-SLAM-like relocalization to identify the most similar learned scene,
then use a motion control algorithm based on the funnel lane theory to navigate
the robot along the previously piloted trajectory. We evaluate the method on a
commodity UAV, examining the robustness of the algorithm to new viewpoints,
lighting conditions, and movements of landmark objects. The results suggest
that semantic object features could be useful due to their invariance to
superficial appearance changes compared to low-level image features.Comment: 7 page
Real-Time Area Coverage and Target Localization using Receding-Horizon Ergodic Exploration
Although a number of solutions exist for the problems of coverage, search and
target localization---commonly addressed separately---whether there exists a
unified strategy that addresses these objectives in a coherent manner without
being application-specific remains a largely open research question. In this
paper, we develop a receding-horizon ergodic control approach, based on hybrid
systems theory, that has the potential to fill this gap. The nonlinear model
predictive control algorithm plans real-time motions that optimally improve
ergodicity with respect to a distribution defined by the expected information
density across the sensing domain. We establish a theoretical framework for
global stability guarantees with respect to a distribution. Moreover, the
approach is distributable across multiple agents, so that each agent can
independently compute its own control while sharing statistics of its coverage
across a communication network. We demonstrate the method in both simulation
and in experiment in the context of target localization, illustrating that the
algorithm is independent of the number of targets being tracked and can be run
in real-time on computationally limited hardware platforms.Comment: 18 page
Implementation of UAV Coordination Based on a Hierarchical Multi-UAV Simulation Platform
In this paper, a hierarchical multi-UAV simulation platform,called XTDrone,
is designed for UAV swarms, which is completely open-source 4 . There are six
layers in XTDrone: communication, simulator,low-level control, high-level
control, coordination, and human interac-tion layers. XTDrone has three
advantages. Firstly, the simulation speedcan be adjusted to match the computer
performance, based on the lock-step mode. Thus, the simulations can be
conducted on a work stationor on a personal laptop, for different purposes.
Secondly, a simplifiedsimulator is also developed which enables quick algorithm
designing sothat the approximated behavior of UAV swarms can be observed
inadvance. Thirdly, XTDrone is based on ROS, Gazebo, and PX4, andhence the
codes in simulations can be easily transplanted to embeddedsystems. Note that
XTDrone can support various types of multi-UAVmissions, and we provide two
important demos in this paper: one is aground-station-based multi-UAV
cooperative search, and the other is adistributed UAV formation flight,
including consensus-based formationcontrol, task assignment, and obstacle
avoidance.Comment: 12 pages, 10 figures. And for the, see
https://gitee.com/robin_shaun/XTDron
Stochastic Real-time Optimal Control for Bearing-only Trajectory Planning
A method is presented to simultaneously solve the optimal control problem and the optimal estimation problem for a bearing-only sensor. For bearing-only systems that require a minimum level of certainty in position relative to a source for mission accomplishment, some amount of maneuver is required to measure range. Traditional methods of trajectory optimization and optimal estimation minimize an information metric. This paper proposes constraining the final value of the information states with known time propagation dynamics relative to a given trajectory which allows for attainment of the required level of information with minimal deviation from a general performance index that can be tailored to a specific vehicle. The proposed method does not suffer from compression of the information metric into a scalar, and provides a route that will attain a particular target estimate quality while maneuvering to a desired relative point or set. An algorithm is created to apply the method in real-time, iteratively estimating target position with an Unscented Kalman Filter and updating the trajectory with an efficient pseudospectral method. Methods and tools required for hardware implementation are presented that apply to any real-time optimal control (RTOC) system. The algorithm is validated with both simulation and flight test, autonomously landing a quadrotor on a wire
SoK - Security and Privacy in the Age of Drones: Threats, Challenges, Solution Mechanisms, and Scientific Gaps
The evolution of drone technology in the past nine years since the first
commercial drone was introduced at CES 2010 has caused many individuals and
businesses to adopt drones for various purposes. We are currently living in an
era in which drones are being used for pizza delivery, the shipment of goods,
and filming, and they are likely to provide an alternative for transportation
in the near future. However, drones also pose a significant challenge in terms
of security and privacy within society (for both individuals and
organizations), and many drone related incidents are reported on a daily basis.
These incidents have called attention to the need to detect and disable drones
used for malicious purposes and opened up a new area of research and
development for academia and industry, with a market that is expected to reach
$1.85 billion by 2024. While some of the knowledge used to detect UAVs has been
adopted for drone detection, new methods have been suggested by industry and
academia alike to deal with the challenges associated with detecting the very
small and fast flying objects. In this paper, we describe new societal threats
to security and privacy created by drones, and present academic and industrial
methods used to detect and disable drones. We review methods targeted at areas
that restrict drone flights and analyze their effectiveness with regard to
various factors (e.g., weather, birds, ambient light, etc.). We present the
challenges arising in areas that allow drone flights, introduce the methods
that exist for dealing with these challenges, and discuss the scientific gaps
that exist in this area. Finally, we review methods used to disable drones,
analyze their effectiveness, and present their expected results. Finally, we
suggest future research directions
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