1,335 research outputs found
A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones
Fully-autonomous miniaturized robots (e.g., drones), with artificial
intelligence (AI) based visual navigation capabilities are extremely
challenging drivers of Internet-of-Things edge intelligence capabilities.
Visual navigation based on AI approaches, such as deep neural networks (DNNs)
are becoming pervasive for standard-size drones, but are considered out of
reach for nanodrones with size of a few cm. In this work, we
present the first (to the best of our knowledge) demonstration of a navigation
engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based
visual navigation. To achieve this goal we developed a complete methodology for
parallel execution of complex DNNs directly on-bard of resource-constrained
milliwatt-scale nodes. Our system is based on GAP8, a novel parallel
ultra-low-power computing platform, and a 27 g commercial, open-source
CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the
software mapping techniques that enable the state-of-the-art deep convolutional
neural network presented in [1] to be fully executed on-board within a strict 6
fps real-time constraint with no compromise in terms of flight results, while
all processing is done with only 64 mW on average. Our navigation engine is
flexible and can be used to span a wide performance range: at its peak
performance corner it achieves 18 fps while still consuming on average just
3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication
in the IEEE Internet of Things Journal (IEEE IOTJ
An antiwindup approach to power controller switching in an ambient healthcare network
This paper proposes a methodology for improved power controller switching in mobile Body Area Networks operating within the ambient healthcare environment. The work extends Anti-windup and Bumpless transfer results to provide a solution to the ambulatory networking problem that ensures sufficient biometric data can always be regenerated at the base station. The solution thereby guarantees satisfactory quality of service for healthcare providers. Compensation is provided for the nonlinear hardware constraints that are a typical feature of the type of network under consideration and graceful performance degradation in the face of hardware output power saturation is demonstrated, thus conserving network energy in an optimal fashion
Comparative Study of Indoor Navigation Systems for Autonomous Flight
Recently, Unmanned Aerial Vehicles (UAVs) have attracted the society and researchers due to the capability to perform in economic, scientific and emergency scenarios, and are being employed in large number of applications especially during the hostile environments. They can operate autonomously for both indoor and outdoor applications mainly including search and rescue, manufacturing, forest fire tracking, remote sensing etc. For both environments, precise localization plays a critical role in order to achieve high performance flight and interacting with the surrounding objects. However, for indoor areas with degraded or denied Global Navigation Satellite System (GNSS) situation, it becomes challenging to control UAV autonomously especially where obstacles are unidentified. A large number of techniques by using various technologies are proposed to get rid of these limits. This paper provides a comparison of such existing solutions and technologies available for this purpose with their strengths and limitations. Further, a summary of current research status with unresolved issues and opportunities is provided that would provide research directions to the researchers of the similar interests
Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations
As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the in-time aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance
Biologically inspired, self organizing communication networks.
PhDThe problem of energy-efficient, reliable, accurate and self-organized target tracking in
Wireless Sensor Networks (WSNs) is considered for sensor nodes with limited physical
resources and abrupt manoeuvring mobile targets. A biologically inspired, adaptive
multi-sensor scheme is proposed for collaborative Single Target Tracking (STT) and
Multi-Target Tracking (MTT). Behavioural data obtained while tracking the targets
including the targets’ previous locations is recorded as metadata to compute the target
sampling interval, target importance and local monitoring interval so that tracking
continuity and energy-efficiency are improved. The subsequent sensor groups that track
the targets are selected proactively according to the information associated with the
predicted target location probability such that the overall tracking performance is
optimized or nearly-optimized. One sensor node from each of the selected groups is
elected as a main node for management operations so that energy efficiency and load
balancing are improved. A decision algorithm is proposed to allow the “conflict” nodes
that are located in the sensing areas of more than one target at the same time to decide
their preferred target according to the target importance and the distance to the target. A
tracking recovery mechanism is developed to provide the tracking reliability in the
event of target loss.
The problem of task mapping and scheduling in WSNs is also considered. A
Biological Independent Task Allocation (BITA) algorithm and a Biological Task
Mapping and Scheduling (BTMS) algorithm are developed to execute an application
using a group of sensor nodes. BITA, BTMS and the functional specialization of the
sensor groups in target tracking are all inspired from biological behaviours of
differentiation in zygote formation.
Simulation results show that compared with other well-known schemes, the
proposed tracking, task mapping and scheduling schemes can provide a significant
improvement in energy-efficiency and computational time, whilst maintaining
acceptable accuracy and seamless tracking, even with abrupt manoeuvring targets.Queen Mary university of London full Scholarshi
Properties of a DTN Packet Forwarding Scheme Inspired By Themodynamics
In this thesis, we develop a discrete time model of a recently proposed algorithm, inspired by thermodynamics, for message routing in Disruption Tolerant Networks (DTNs). We model the evolution of the temperature at the nodes as a stochastic switched linear system and show that the temperatures converge in distribution to a unique stationary distribution that is independent of initial conditions. The proof of this result borrows tools from Iterated Random Maps (IRMs) and Queuing theory. Lastly, we simulate the proposed algorithm, using a variety of mobility models, in order to observe the performance of the algorithm under various conditions
Development of an active vision system for robot inspection of complex objects
Dissertação de mestrado integrado em Engenharia Mecânica (área de especialização em Sistemas Mecatrónicos)The dissertation presented here is in the scope of the IntVis4Insp project between University of Minho
and the company Neadvance. It focuses on the development of a 3D hand tracking system that must be
capable of extracting the hand position and orientation to prepare a manipulator for automatic inspection
of leather pieces.
This work starts with a literature review about the two main methods for collecting the necessary data to
perform 3D hand tracking. These divide into glove-based methods and vision-based methods. The first
ones work with some kind of support mounted on the hand that holds all the necessary sensors to
measure the desired parameters. While the second ones recur to one or more cameras to capture the
hands and through computer vision algorithms track their position and configuration. The selected
method for this work was the vision-based method Openpose. For each recorded image, this application
can locate 21 hand keypoints on each hand that together form a skeleton of the hands.
This application is used in the tracking system developed throughout this dissertation. Its information is
used in a more complete pipeline where the location of those hand keypoints is crucial to track the hands
in videos of the demonstrated movements. These videos were recorded with an RGB-D camera, the
Microsoft Kinect, which provides a depth value for every RGB pixel recorded. With the depth information
and the 2D location of the hand keypoints in the images, it was possible to obtain the 3D world coordinates
of these points considering the pinhole camera model.
To define the hand, position a point is selected among the 21 for each hand, but for the hand orientation,
it was necessary to develop an auxiliary method called “Iterative Pose Estimation Method” (ITP), which
estimates the complete 3D pose of the hands. This method recurs only to the 2D locations of every hand
keypoint, and the complete 3D world coordinates of the wrists to estimate the right 3D world coordinates
of all the remaining points on the hand. This solution solves the problems related to hand occlusions that
a prone to happen due to the use of only one camera to record the inspection videos. Once the world
location of all the points in the hands is accurately estimated, their orientation can be defined by selecting
three points forming a plane.A dissertação aqui apresentada insere-se no âmbito do projeto IntVis4Insp entre a Universidade do Minho
e a empresa Neadavance, e foca-se no desenvolvimento de um sistema para extração da posição e
orientação das mĂŁos no espaço para posterior auxĂlio na manipulação automática de peças de couro,
com recurso a manipuladores robĂłticos.
O trabalho inicia-se com uma revisão literária sobre os dois principais métodos existentes para efetuar a
recolha de dados necessária à monitorização da posição e orientação das mãos ao longo do tempo.
Estes dividem-se em métodos baseados em luvas ou visão. No caso dos primeiros, estes recorrem
normalmente a algum tipo de suporte montado na mĂŁo (ex.: luva em tecido), onde estĂŁo instalados todos
os sensores necessários para a medição dos parâmetros desejados. Relativamente a sistemas de visão
estes recorrem a uma câmara ou conjunto delas para capturar as mãos e por via de algoritmos de visão
por computador determinam a sua posição e configuração. Foi selecionado para este trabalho um
algoritmo de visĂŁo por computador denominado por Openpose. Este Ă© capaz de, em cada imagem
gravada e para cada mĂŁo, localizar 21 pontos pertencentes ao seu esqueleto.
Esta aplicação é inserida no sistema de monitorização desenvolvido, sendo utilizada a sua informação
numa arquitetura mais completa onde é efetuada a extração da localização dos pontos chave de cada
mĂŁo nos vĂdeos de demonstração dos movimentos de inspeção. A gravação destes vĂdeos Ă© efetuada
com uma câmara RGB-D, a Microsoft Kinect, que fornece um valor de profundidade para cada pixel RGB
gravado. Com os dados de profundidade e a localização dos pontos chave nas imagens foi possĂvel obter
as coordenadas 3D no mundo destes pontos considerando o modelo pinhole para a câmara. No caso da
posição da mão é selecionado um ponto de entre os 21 para a definir ao longo do tempo, no entanto,
para o cálculo da orientação foi desenvolvido um método auxiliar para estimação da pose tridimensional
da mão denominado por “Iterative Pose Estimation Method” (ITP). Este método recorre aos dados 2D
do Openpose e às coordenadas 3D do pulso de cada mão para efetuar a correta estimação das
coordenadas 3D dos restantes pontos da mĂŁo. Isto permite essencialmente resolver problemas com
oclusões da mĂŁo, muito frequentes com o uso de uma sĂł câmara na gravação dos vĂdeos. Uma vez
estimada corretamente a posição 3D no mundo dos vários pontos da mão, a sua orientação pode ser
definida com recurso a quaisquer trĂŞs pontos que definam um plano
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