934 research outputs found
Quadrotor control for persistent surveillance of dynamic environments
Thesis (M.S.)--Boston UniversityThe last decade has witnessed many advances in the field of small scale unmanned aerial vehicles (UAVs). In particular, the quadrotor has attracted significant attention. Due to its ability to perform vertical takeoff and landing, and to operate in cluttered spaces, the quadrotor is utilized in numerous practical applications, such as reconnaissance and information gathering in unsafe or otherwise unreachable environments.
This work considers the application of aerial surveillance over a city-like environment. The thesis presents a framework for automatic deployment of quadrotors to monitor and react to dynamically changing events. The framework has a hierarchical structure. At the top level, the UAVs perform complex behaviors that satisfy high- level mission specifications. At the bottom level, low-level controllers drive actuators on vehicles to perform the desired maneuvers.
In parallel with the development of controllers, this work covers the implementation of the system into an experimental testbed. The testbed emulates a city using physical objects to represent static features and projectors to display dynamic events occurring on the ground as seen by an aerial vehicle. The experimental platform features a motion capture system that provides position data for UAVs and physical features of the environment, allowing for precise, closed-loop control of the vehicles. Experimental runs in the testbed are used to validate the effectiveness of the developed control strategies
Modelling and Verification of Multiple UAV Mission Using SMV
Model checking has been used to verify the correctness of digital circuits,
security protocols, communication protocols, as they can be modelled by means
of finite state transition model. However, modelling the behaviour of hybrid
systems like UAVs in a Kripke model is challenging. This work is aimed at
capturing the behaviour of an UAV performing cooperative search mission into a
Kripke model, so as to verify it against the temporal properties expressed in
Computation Tree Logic (CTL). SMV model checker is used for the purpose of
model checking
Evolutionary strategies in swarm robotics controllers
Nowadays, Unmanned Vehicles (UV) are widespread around the world. Most of these
vehicles require a great level of human control, and mission success is reliant on this
dependency. Therefore, it is important to use machine learning techniques that will train the
robotic controllers to automate the control, making the process more efficient.
Evolutionary strategies may be the key to having robust and adaptive learning in robotic
systems. Many studies involving UV systems and evolutionary strategies have been
conducted in the last years, however, there are still research gaps that need to be addressed,
such as the reality gap. The reality gap occurs when controllers trained in simulated
environments fail to be transferred to real robots.
This work proposes an approach for solving robotic tasks using realistic simulation and
using evolutionary strategies to train controllers. The chosen setup is easily scalable for multirobot
systems or swarm robots.
In this thesis, the simulation architecture and setup are presented, including the drone
simulation model and software. The drone model chosen for the simulations is available in the
real world and widely used, such as the software and flight control unit. This relevant factor
makes the transition to reality smoother and easier. Controllers using behavior trees were
evolved using a developed evolutionary algorithm, and several experiments were conducted.
Results demonstrated that it is possible to evolve a robotic controller in realistic
simulation environments, using a simulated drone model that exists in the real world, and also
the same flight control unit and operating system that is generally used in real world
experiments.Atualmente os Veículos Não Tripulados (VNT) encontram-se difundidos por todo o Mundo.
A maioria destes veículos requerem um elevado controlo humano, e o sucesso das missões
está diretamente dependente deste fator. Assim, é importante utilizar técnicas de
aprendizagem automática que irão treinar os controladores dos VNT, de modo a automatizar o
controlo, tornando o processo mais eficiente.
As estratégias evolutivas podem ser a chave para uma aprendizagem robusta e adaptativa
em sistemas robóticos. Vários estudos têm sido realizados nos últimos anos, contudo, existem
lacunas que precisam de ser abordadas, tais como o reality gap. Este facto ocorre quando os
controladores treinados em ambientes simulados falham ao serem transferidos para VNT
reais.
Este trabalho propõe uma abordagem para a resolução de missões com VNT, utilizando
um simulador realista e estratégias evolutivas para treinar controladores. A arquitetura
escolhida é facilmente escalável para sistemas com múltiplos VNT.
Nesta tese, é apresentada a arquitetura e configuração do ambiente de simulação,
incluindo o modelo e software de simulação do VNT. O modelo de VNT escolhido para as
simulações é um modelo real e amplamente utilizado, assim como o software e a unidade de
controlo de voo. Este fator é relevante e torna a transição para a realidade mais suave. É
desenvolvido um algoritmo evolucionário para treinar um controlador, que utiliza behavior
trees, e realizados diversos testes.
Os resultados demonstram que é possível evoluir um controlador em ambientes de
simulação realistas, utilizando um VNT simulado mas real, assim como utilizando as mesmas
unidades de controlo de voo e software que são amplamente utilizados em ambiente real
A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles
Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning for UAVs, including the most relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions
Self-management Framework for Mobile Autonomous Systems
The advent of mobile and ubiquitous systems has enabled the development of autonomous
systems such as wireless-sensors for environmental data collection and teams of collaborating Unmanned Autonomous Vehicles (UAVs) used in missions unsuitable for humans. However, with these range of new application domains comes a new challenge – enabling self-management in mobile autonomous systems. The primary challenge in using autonomous systems for real-life missions is shifting the burden of management from humans to these systems themselves without loss of the ability to adapt to failures, changes in context, and changing user requirements.
Autonomous systems have to be able to manage themselves individually as well as to form self-managing teams that are able to recover or adapt to failures, protect themselves from attacks and optimise performance.
This thesis proposes a novel distributed policy-based framework that enables autonomous systems to perform self management individually and as a team. The
framework allows missions to be specified in terms of roles in an adaptable and reusable way, enables dynamic and secure team formation with a utility-based approach
for optimal role assignment, caters for communication link maintenance among team members and recovery from failure. Adaptive management is achieved by employing an architecture that uses policy-based techniques to allow dynamic modification of the management strategy relating to resources, role behaviour, team and communications management, without reloading the basic software within the system.
Evaluation of the framework shows that it is scalable with respect to the number of roles, and consequently the number of autonomous systems participating in the
mission. It is also shown to be optimal with respect to role assignments, and robust
to intermittent communication link disconnections and permanent team-member
failures. The prototype implementation was tested on mobile robots as a proof-ofconcept
demonstration
Autonomous aerial robot for high-speed search and intercept applications
In recent years, high-speed navigation and environment interaction in the context of
aerial robotics has become a field of interest for several academic and industrial research studies. In
particular, Search and Intercept (SaI) applications for aerial robots pose a compelling research
area due to their potential usability in several environments. Nevertheless, SaI tasks involve a
challenging development regarding sensory weight, onboard computation resources, actuation design,
and algorithms for perception and control, among others. In this work, a fully autonomous aerial
robot for high-speed object grasping has been proposed. As an additional subtask, our system is able
to autonomously pierce balloons located in poles close to the surface. Our first contribution is the
design of the aerial robot at an actuation and sensory level consisting of a novel gripper design with
additional sensors enabling the robot to grasp objects at high speeds. The second contribution is
a complete software framework consisting of perception, state estimation, motion planning, motion
control, and mission control in order to rapidly and robustly perform the autonomous grasping
mission. Our approach has been validated in a challenging international competition and has shown
outstanding results, being able to autonomously search, follow, and grasp a moving object at 6 m/s
in an outdoor environment.Agencia Estatal de InvestigaciónKhalifa Universit
System Architectures for Cooperative Teams of Unmanned Aerial Vehicles Interacting Physically with the Environment
Unmanned Aerial Vehicles (UAVs) have become quite a useful tool for a wide range of
applications, from inspection & maintenance to search & rescue, among others. The
capabilities of a single UAV can be extended or complemented by the deployment
of more UAVs, so multi-UAV cooperative teams are becoming a trend. In that case,
as di erent autopilots, heterogeneous platforms, and application-dependent software
components have to be integrated, multi-UAV system architectures that are fexible
and can adapt to the team's needs are required.
In this thesis, we develop system architectures for cooperative teams of UAVs,
paying special attention to applications that require physical interaction with the
environment, which is typically unstructured. First, we implement some layers to
abstract the high-level components from the hardware speci cs. Then we propose
increasingly advanced architectures, from a single-UAV hierarchical navigation architecture
to an architecture for a cooperative team of heterogeneous UAVs. All
this work has been thoroughly tested in both simulation and eld experiments in
di erent challenging scenarios through research projects and robotics competitions.
Most of the applications required physical interaction with the environment, mainly
in unstructured outdoors scenarios. All the know-how and lessons learned throughout
the process are shared in this thesis, and all relevant code is publicly available.Los vehículos aéreos no tripulados (UAVs, del inglés Unmanned Aerial Vehicles) se han
convertido en herramientas muy valiosas para un amplio espectro de aplicaciones, como
inspección y mantenimiento, u operaciones de rescate, entre otras. Las capacidades de un
único UAV pueden verse extendidas o complementadas al utilizar varios de estos vehículos
simultáneamente, por lo que la tendencia actual es el uso de equipos cooperativos con
múltiples UAVs. Para ello, es fundamental la integración de diferentes autopilotos,
plataformas heterogéneas, y componentes software -que dependen de la aplicación-, por lo
que se requieren arquitecturas multi-UAV que sean flexibles y adaptables a las necesidades
del equipo.
En esta tesis, se desarrollan arquitecturas para equipos cooperativos de UAVs, prestando
una especial atención a aplicaciones que requieran de interacción física con el entorno,
cuya naturaleza es típicamente no estructurada. Primero se proponen capas para abstraer a
los componentes de alto nivel de las particularidades del hardware. Luego se desarrollan
arquitecturas cada vez más avanzadas, desde una arquitectura de navegación para un
único UAV, hasta una para un equipo cooperativo de UAVs heterogéneos. Todo el trabajo ha
sido minuciosamente probado, tanto en simulación como en experimentos reales, en
diferentes y complejos escenarios motivados por proyectos de investigación y
competiciones de robótica. En la mayoría de las aplicaciones se requería de interacción
física con el entorno, que es normalmente un escenario en exteriores no estructurado. A lo
largo de la tesis, se comparten todo el conocimiento adquirido y las lecciones aprendidas en
el proceso, y el código relevante está publicado como open-source
A particle swarm optimization approach using adaptive entropy-based fitness quantification of expert knowledge for high-level, real-time cognitive robotic control
Abstract: High-level, real-time mission control of semi-autonomous robots, deployed in remote and dynamic environments, remains a challenge. Control models, learnt from a knowledgebase, quickly become obsolete when the environment or the knowledgebase changes. This research study introduces a cognitive reasoning process, to select the optimal action, using the most relevant knowledge from the knowledgebase, subject to observed evidence. The approach in this study introduces an adaptive entropy-based set-based particle swarm algorithm (AE-SPSO) and a novel, adaptive entropy-based fitness quantification (AEFQ) algorithm for evidence-based optimization of the knowledge. The performance of the AE-SPSO and AEFQ algorithms are experimentally evaluated with two unmanned aerial vehicle (UAV) benchmark missions: (1) relocating the UAV to a charging station and (2) collecting and delivering a package. Performance is measured by inspecting the success and completeness of the mission and the accuracy of autonomous flight control. The results show that the AE-SPSO/AEFQ approach successfully finds the optimal state-transition for each mission task and that autonomous flight control is successfully achieved
Command and Control Systems for Search and Rescue Robots
The novel application of unmanned systems in the domain of humanitarian Search and Rescue (SAR) operations has created a need to develop specific multi-Robot Command and Control (RC2) systems. This societal application of robotics requires human-robot interfaces for controlling a large fleet of heterogeneous robots deployed in multiple domains of operation (ground, aerial and marine). This chapter provides an overview of the Command, Control and Intelligence (C2I) system developed within the scope of Integrated Components for Assisted Rescue and Unmanned Search operations (ICARUS). The life cycle of the system begins with a description of use cases and the deployment scenarios in collaboration with SAR teams as end-users. This is followed by an illustration of the system design and architecture, core technologies used in implementing the C2I, iterative integration phases with field deployments for evaluating and improving the system. The main subcomponents consist of a central Mission Planning and Coordination System (MPCS), field Robot Command and Control (RC2) subsystems with a portable force-feedback exoskeleton interface for robot arm tele-manipulation and field mobile devices. The distribution of these C2I subsystems with their communication links for unmanned SAR operations is described in detail. Field demonstrations of the C2I system with SAR personnel assisted by unmanned systems provide an outlook for implementing such systems into mainstream SAR operations in the future
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