1,882 research outputs found
A Survey on Aerial Swarm Robotics
The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas
Implementation and validation of an event-based real-time nonlinear model predictive control framework with ROS interface for single and multi-robot systems.
This paper presents the implementation and experimental validation of a central control framework. The presented framework addresses the need for a controller, which provides high performance combined with a low-computational load while being on-line adaptable to changes in the control scenario. Examples for such scenarios are cooperative control, task-based control and fault-tolerant control, where the system's topology, dynamics, objectives and constraints are changing. The framework combines a fast Nonlinear Model Predictive Control (NMPC), a communication interface with the Robot Operating System (ROS) as well as a modularization that allows an event-based change of the NMPC scenario. To experimentally validate performance and event-based adaptability of the framework, this paper is using a cooperative control scenario of Unmanned Aerial Vehicles (UAVs)
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
Decentralized Control of Uncertain Multi-Agent Systems with Connectivity Maintenance and Collision Avoidance
This paper addresses the problem of navigation control of a general class of
uncertain nonlinear multi-agent systems in a bounded workspace of
with static obstacles. In particular, we propose a decentralized
control protocol such that each agent reaches a predefined position at the
workspace, while using only local information based on a limited sensing
radius. The proposed scheme guarantees that the initially connected agents
remain always connected. In addition, by introducing certain distance
constraints, we guarantee inter-agent collision avoidance, as well as,
collision avoidance with the obstacles and the boundary of the workspace. The
proposed controllers employ a class of Decentralized Nonlinear Model Predictive
Controllers (DNMPC) under the presence of disturbances and uncertainties.
Finally, simulation results verify the validity of the proposed framework.Comment: IEEE European Control Conference (ECC), Limassol, Cyprus, June 201
Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms
This book is a reprint of the Special Issue “Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms”,which was published in Applied Sciences
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