424 research outputs found
What Makes a Place? Building Bespoke Place Dependent Object Detectors for Robotics
This paper is about enabling robots to improve their perceptual performance
through repeated use in their operating environment, creating local expert
detectors fitted to the places through which a robot moves. We leverage the
concept of 'experiences' in visual perception for robotics, accounting for bias
in the data a robot sees by fitting object detector models to a particular
place. The key question we seek to answer in this paper is simply: how do we
define a place? We build bespoke pedestrian detector models for autonomous
driving, highlighting the necessary trade off between generalisation and model
capacity as we vary the extent of the place we fit to. We demonstrate a
sizeable performance gain over a current state-of-the-art detector when using
computationally lightweight bespoke place-fitted detector models.Comment: IROS 201
User modelling for robotic companions using stochastic context-free grammars
Creating models about others is a sophisticated human ability that robotic companions need to develop in order to have successful interactions. This thesis proposes user modelling frameworks to personalise the interaction between a robot and its user and devises novel scenarios where robotic companions may apply these user modelling techniques.
We tackle the creation of user models in a hierarchical manner, using a streamlined version of the Hierarchical Attentive Multiple-Models for Execution and Recognition (HAMMER) architecture to detect low-level user actions and taking advantage of Stochastic Context-Free Grammars (SCFGs) to instantiate higher-level models which recognise uncertain and recursive sequences of low-level actions.
We discuss a couple of distinct scenarios for robotic companions: a humanoid sidekick for power-wheelchair users and a companion of hospital patients. Next, we address the limitations of the previous scenarios by applying our user modelling techniques and designing two further scenarios that fully take advantage of the user model. These scenarios are: a wheelchair driving tutor which models the user abilities, and the musical collaborator which learns the preferences of its users.
The methodology produced interesting results in all scenarios: users preferred the actual robot over a simulator as a wheelchair sidekick. Hospital patients rated positively their interactions with the companion independently of their age. Moreover, most users agreed that the music collaborator had become a better accompanist with our framework. Finally, we observed that users' driving performance improved when the robotic tutor instructed them to repeat a task.
As our workforce ages and the care requirements in our society grow, robots will need to play a role in helping us lead better lives. This thesis shows that, through the use of SCFGs, adaptive user models may be generated which then can be used by robots to assist their users.Open Acces
Low computational SLAM for an autonomous indoor aerial inspection vehicle
The past decade has seen an increase in the capability of small scale Unmanned
Aerial Vehicle (UAV) systems, made possible through technological advancements
in battery, computing and sensor miniaturisation technology. This has opened a new
and rapidly growing branch of robotic research and has sparked the imagination of
industry leading to new UAV based services, from the inspection of power-lines to
remote police surveillance.
Miniaturisation of UAVs have also made them small enough to be practically flown
indoors. For example, the inspection of elevated areas in hazardous or damaged
structures where the use of conventional ground-based robots are unsuitable. Sellafield
Ltd, a nuclear reprocessing facility in the U.K. has many buildings that require
frequent safety inspections. UAV inspections eliminate the current risk to personnel
of radiation exposure and other hazards in tall structures where scaffolding or hoists
are required.
This project focused on the development of a UAV for the novel application of
semi-autonomously navigating and inspecting these structures without the need for
personnel to enter the building. Development exposed a significant gap in knowledge
concerning indoor localisation, specifically Simultaneous Localisation and Mapping
(SLAM) for use on-board UAVs. To lower the on-board processing requirements
of SLAM, other UAV research groups have employed techniques such as off-board
processing, reduced dimensionality or prior knowledge of the structure, techniques
not suitable to this application given the unknown nature of the structures and the
risk of radio-shadows.
In this thesis a novel localisation algorithm, which enables real-time and threedimensional
SLAM running solely on-board a computationally constrained UAV in
heavily cluttered and unknown environments is proposed. The algorithm, based
on the Iterative Closest Point (ICP) method utilising approximate nearest neighbour
searches and point-cloud decimation to reduce the processing requirements has
successfully been tested in environments similar to that specified by Sellafield Ltd
On flexibly integrating machine vision inspection systems in PCB manufacture
The objective of this research is to advance computer vision techniques
and their applications in the electronics manufacturing industry. The research has
been carried out with specific reference to the design of automatic optical inspection
(AOI) systems and their role in the manufacture of printed circuit boards (PCBs).
To achieve this objective, application areas of AOI systems in PCB manufacture
have been examined. As a result, a requirement for enhanced performance
characteristics has been identified and novel approaches and image processing algorithms
have been evolved which can be used within next generation of AOI systems.
The approaches are based on gaining an understanding of ways in which
manufacturing information can be used to support AOI operations. Through providing
information support, an AOI system has access to product models and associated
information which can be used to enhance the execution of visual inspection
tasks. Manufacturing systems integration, or more accurately controlled access to
electronic information, is the key to the approaches. Also in the thesis methods are
proposed to achieve the flexible integration of AOI systems (and computer vision
systems in general) within their host PCB manufacturing environment. Furthermore,
potential applications of information supported AOI systems at various stages of
PCB manufacturing have been studied.
It is envisaged that more efficient and cost-effective applications of AOI
can be attained through adopting the flexible integration methods proposed, since
AOI-generated information can now be accessed and utilized by other processes
A Helping Hand for Europe: The Competitive Outlook for the EU Robotics Industry
This report is one of a series resulting from a project entitled ¿Competitiveness by Leveraging Emerging Technologies Economically¿ (COMPLETE), carried out by JRC-IPTS.
Each of the COMPLETE studies illustrates in its own right that European companies are active on many fronts of emerging and disruptive ICT technologies and are supplying the market with relevant products and services. Nevertheless, the studies also show that the creation and growth of high tech companies is still very complex and difficult in Europe, and too many economic opportunities seem to escape European initiatives and ownership. COMPLETE helps to illustrate some of the difficulties experienced in different segments of the ICT industry and by growing potential global players. Hopefully, COMPLETE will contribute to a better understanding of the opportunities and help shape better market conditions (financial, labour and product markets) to sustain European competitiveness and economic growth.
This report deals with robotics applications in general, and in two specific areas selected because of potential market and EU capability in these areas: robotics applications in SMEs, and robotics safety. It starts by introducing the state of the art in robotics, their applications, market size, value chains and disruptive potential of emerging robotics technologies. For each of the two specific areas, the report describes the EU landscape, potential market, benefits, difficulties, and how these might be overcome. The last chapter draws together the findings of the study, to consider EU competitiveness in robotics, opportunities and policy implications. The work is based on desk research and targeted interviews with industry experts in Europe and beyond. The results were reviewed by experts and in a dedicated workshop.JRC.DDG.J.4-Information Societ
Long-Term Visual Localization Revisited
Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing conditions, including day-night changes, as well as weather and seasonal variations, while providing highly accurate six degree-of-freedom (6DOF) camera pose estimates. In this paper, we extend three publicly available datasets containing images captured under a wide variety of viewing conditions, but lacking camera pose information, with ground truth pose information, making evaluation of the impact of various factors on 6DOF camera pose estimation accuracy possible. We also discuss the performance of state-of-the-art localization approaches on these datasets. Additionally, we release around half of the poses for all conditions, and keep the remaining half private as a test set, in the hopes that this will stimulate research on long-term visual localization, learned local image features, and related research areas. Our datasets are available at visuallocalization.net, where we are also hosting a benchmarking server for automatic evaluation of results on the test set. The presented state-of-the-art results are to a large degree based on submissions to our server
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
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