93 research outputs found
A Novel Real-Time Moving Target Tracking and Path Planning System for a Quadrotor UAV in Unknown Unstructured Outdoor Scenes
A quadrotor unmanned aerial vehicle (UAV) should have the ability to perform real-time target tracking and path planning simultaneously even when the target enters unstructured scenes, such as groves or forests. To accomplish this task, a novel system framework is designed and proposed to accomplish simultaneous moving target tracking and path planning by a quadrotor UAV with an onboard embedded computer, vision sensors, and a two-dimensional laser scanner. A support vector machine-based target screening algorithm is deployed to select the correct target from multiple candidates detected by single shot multibox detector. Furthermore, a new tracker named TLD-KCF is presented in this paper, in which a conditional scale adaptive algorithm is adopted to improve the tracking performance for a quadrotor UAV in cluttered outdoor environments. According to distance and position estimation for a moving target, our quadrotor UAV can acquire a control point to guide its fight. To reduce the computational burden, a fast path planning algorithm is proposed based on elliptical tangent model. A series of experiments are conducted on our quadrotor UAV platform DJI M100. Experimental video and comparison results among four kinds of target tracking algorithms are given to show the validity and practicality of the proposed approach
Optimal Trajectory Planning for Cinematography with Multiple Unmanned Aerial Vehicles
This paper presents a method for planning optimal trajectories with a team of
Unmanned Aerial Vehicles (UAVs) performing autonomous cinematography. The
method is able to plan trajectories online and in a distributed manner,
providing coordination between the UAVs. We propose a novel non-linear
formulation for this challenging problem of computing multi-UAV optimal
trajectories for cinematography; integrating UAVs dynamics and collision
avoidance constraints, together with cinematographic aspects like smoothness,
gimbal mechanical limits and mutual camera visibility. We integrate our method
within a hardware and software architecture for UAV cinematography that was
previously developed within the framework of the MultiDrone project; and
demonstrate its use with different types of shots filming a moving target
outdoors. We provide extensive experimental results both in simulation and
field experiments. We analyze the performance of the method and prove that it
is able to compute online smooth trajectories, reducing jerky movements and
complying with cinematography constraints.Comment: This paper has been published as: Optimal trajectory planning for
cinematography with multiple Unmanned Aerial Vehicles. Alfonso Alcantara and
Jesus Capitan and Rita Cunha and Anibal Ollero. Robotics and Autonomous
Systems. 103778 (2021) 10.1016/j.robot.2021.10377
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Path planning and control of a quadrotor UAV based on an improved APF using parallel search
Control and path planning are two essential and challenging issues in quadrotor unmanned aerial vehicle (UAV). In this paper, an approach for moving around the nearest obstacle is integrated into an artificial potential field (APF) to avoid the trap of local minimum of APF. The advantage of this approach is that it can help the UAV successfully escape from the local minimum without collision with any obstacles. Moreover, the UAV may encounter the problem of unreachable target when there are too many obstacles near its target. To address the problem, a parallel search algorithm is proposed, which requires UAV to simultaneously detect obstacles between current point and target point when it moves around the nearest obstacle to approach the target. Then, to achieve tracking of the planned path, the desired attitude states are calculated. Considering the external disturbance acting on the quadrotor, a nonlinear disturbance observer (NDO) is developed to guarantee observation error to exponentially converge to zero. Furthermore, a backstepping controller synthesized with the NDO is designed to eliminate tracking errors of attitude. Finally, comparative simulations are carried out to illustrate the effectiveness of the proposed path planning algorithm and controller
Real-time UAV Complex Missions Leveraging Self-Adaptive Controller with Elastic Structure
The expectation of unmanned air vehicles (UAVs) pushes the operation
environment to narrow spaces, where the systems may fly very close to an object
and perform an interaction. This phase brings the variation in UAV dynamics:
thrust and drag coefficient of the propellers might change under different
proximity. At the same time, UAVs may need to operate under external
disturbances to follow time-based trajectories. Under these challenging
conditions, a standard controller approach may not handle all missions with a
fixed structure, where there may be a need to adjust its parameters for each
different case. With these motivations, practical implementation and evaluation
of an autonomous controller applied to a quadrotor UAV are proposed in this
work. A self-adaptive controller based on a composite control scheme where a
combination of sliding mode control (SMC) and evolving neuro-fuzzy control is
used. The parameter vector of the neuro-fuzzy controller is updated adaptively
based on the sliding surface of the SMC. The autonomous controller possesses a
new elastic structure, where the number of fuzzy rules keeps growing or get
pruned based on bias and variance balance. The interaction of the UAV is
experimentally evaluated in real time considering the ground effect, ceiling
effect and flight through a strong fan-generated wind while following
time-based trajectories.Comment: 18 page
Optimal Multi-UAV Trajectory Planning for Filming Applications
Teams of multiple Unmanned Aerial Vehicles (UAVs) can be used to record large-scale
outdoor scenarios and complementary views of several action points as a promising
system for cinematic video recording. Generating the trajectories of the UAVs plays
a key role, as it should be ensured that they comply with requirements for system
dynamics, smoothness, and safety. The rise of numerical methods for nonlinear
optimization is finding a
ourishing field in optimization-based approaches to multi-
UAV trajectory planning. In particular, these methods are rather promising for
video recording applications, as they enable multiple constraints and objectives to
be formulated, such as trajectory smoothness, compliance with UAV and camera
dynamics, avoidance of obstacles and inter-UAV con
icts, and mutual UAV visibility.
The main objective of this thesis is to plan online trajectories for multi-UAV teams in
video applications, formulating novel optimization problems and solving them in real
time.
The thesis begins by presenting a framework for carrying out autonomous cinematography
missions with a team of UAVs. This framework enables media directors
to design missions involving different types of shots with one or multiple cameras,
running sequentially or concurrently. Second, the thesis proposes a novel non-linear
formulation for the challenging problem of computing optimal multi-UAV trajectories
for cinematography, integrating UAV dynamics and collision avoidance constraints,
together with cinematographic aspects such as smoothness, gimbal mechanical limits,
and mutual camera visibility. Lastly, the thesis describes a method for autonomous
aerial recording with distributed lighting by a team of UAVs. The multi-UAV trajectory
optimization problem is decoupled into two steps in order to tackle non-linear cinematographic aspects and obstacle avoidance at separate stages. This allows the
trajectory planner to perform in real time and to react online to changes in dynamic
environments.
It is important to note that all the methods in the thesis have been validated
by means of extensive simulations and field experiments. Moreover, all the software
components have been developed as open source.Los equipos de vehículos aéreos no tripulados (UAV) son sistemas prometedores para grabar
eventos cinematográficos, en escenarios exteriores de grandes dimensiones difíciles de cubrir
o para tomar vistas complementarias de diferentes puntos de acción. La generación de
trayectorias para este tipo de vehículos desempeña un papel fundamental, ya que debe
garantizarse que se cumplan requisitos dinámicos, de suavidad y de seguridad.
Los enfoques basados en la optimización para la planificación de trayectorias de múltiples
UAVs se pueden ver beneficiados por el auge de los métodos numéricos para la resolución de
problemas de optimización no lineales. En particular, estos métodos son bastante
prometedores para las aplicaciones de grabación de vídeo, ya que permiten formular múltiples
restricciones y objetivos, como la suavidad de la trayectoria, el cumplimiento de la dinámica
del UAV y de la cámara, la evitación de obstáculos y de conflictos entre UAVs, y la visibilidad
mutua.
El objetivo principal de esta tesis es planificar trayectorias para equipos multi-UAV en
aplicaciones de vídeo, formulando novedosos problemas de optimización y resolviéndolos en
tiempo real.
La tesis comienza presentando un marco de trabajo para la realización de misiones
cinematográficas autónomas con un equipo de UAVs. Este marco permite a los directores de
medios de comunicación diseñar misiones que incluyan diferentes tipos de tomas con una o
varias cámaras, ejecutadas de forma secuencial o concurrente. En segundo lugar, la tesis
propone una novedosa formulación no lineal para el difícil problema de calcular las
trayectorias óptimas de los vehículos aéreos no tripulados en cinematografía, integrando en el
problema la dinámica de los UAVs y las restricciones para evitar colisiones, junto con aspectos
cinematográficos como la suavidad, los límites mecánicos del cardán y la visibilidad mutua de
las cámaras. Por último, la tesis describe un método de grabación aérea autónoma con
iluminación distribuida por un equipo de UAVs. El problema de optimización de trayectorias se
desacopla en dos pasos para abordar los aspectos cinematográficos no lineales y la evitación
de obstáculos en etapas separadas. Esto permite al planificador de trayectorias actuar en
tiempo real y reaccionar en línea a los cambios en los entornos dinámicos.
Es importante señalar que todos los métodos de la tesis han sido validados mediante extensas
simulaciones y experimentos de campo. Además, todos los componentes del software se han
desarrollado como código abierto
A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques
Search and Rescue (SAR) missions represent an important challenge in the robotics research field as they usually involve exceedingly variable-nature scenarios which require a high-level of autonomy and versatile decision-making capabilities. This challenge becomes even more relevant in the case of aerial robotic platforms owing to their limited payload and computational capabilities. In this paper, we present a fully-autonomous aerial robotic solution, for executing complex SAR missions in unstructured indoor environments. The proposed system is based on the combination of a complete hardware configuration and a flexible system architecture which allows the execution of high-level missions in a fully unsupervised manner (i.e. without human intervention). In order to obtain flexible and versatile behaviors from the proposed aerial robot, several learning-based capabilities have been integrated for target recognition and interaction. The target recognition capability includes a supervised learning classifier based on a computationally-efficient Convolutional Neural Network (CNN) model trained for target/background classification, while the capability to interact with the target for rescue operations introduces a novel Image-Based Visual Servoing (IBVS) algorithm which integrates a recent deep reinforcement learning method named Deep Deterministic Policy Gradients (DDPG). In order to train the aerial robot for performing IBVS tasks, a reinforcement learning framework has been developed, which integrates a deep reinforcement learning agent (e.g. DDPG) with a Gazebo-based simulator for aerial robotics. The proposed system has been validated in a wide range of simulation flights, using Gazebo and PX4 Software-In-The-Loop, and real flights in cluttered indoor environments, demonstrating the versatility of the proposed system in complex SAR missions
Survey of computer vision algorithms and applications for unmanned aerial vehicles
This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria.This research is supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2013-48314-C3-1-R)
Urban Drone Navigation: Autoencoder Learning Fusion for Aerodynamics
Drones are vital for urban emergency search and rescue (SAR) due to the
challenges of navigating dynamic environments with obstacles like buildings and
wind. This paper presents a method that combines multi-objective reinforcement
learning (MORL) with a convolutional autoencoder to improve drone navigation in
urban SAR. The approach uses MORL to achieve multiple goals and the autoencoder
for cost-effective wind simulations. By utilizing imagery data of urban
layouts, the drone can autonomously make navigation decisions, optimize paths,
and counteract wind effects without traditional sensors. Tested on a New York
City model, this method enhances drone SAR operations in complex urban
settings.Comment: 47 page
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