Typically software engineers implement their software according to the design of the software
structure. Relations between classes and interfaces such as method-call relations and inheritance
relations are essential parts of a software structure. Accordingly, analyzing several types of
relations will benefit the static analysis process of the software structure. The tasks of this
analysis include but not limited to: understanding of (legacy) software, checking guidelines,
improving product lines, finding structure, or re-engineering of existing software. Graphs with
multi-type edges are possible representation for these relations considering them as edges, while
nodes represent classes and interfaces of software. Then, this multiple type edges graph can
be mapped to visualizations. However, the visualizations should deal with the multiplicity of
relations types and scalability, and they should enable the software engineers to recognize visual
patterns at the same time.
To advance the usage of visualizations for analyzing the static structure of software systems,
I tracked difierent development phases of the interactive multi-matrix visualization (IMMV)
showing an extended user study at the end. Visual structures were determined and classified
systematically using IMMV compared to PNLV in the extended user study as four categories:
High degree, Within-package edges, Cross-package edges, No edges. In addition to these structures
that were found in these handy tools, other structures that look interesting for software
engineers such as cycles and hierarchical structures need additional visualizations to display
them and to investigate them. Therefore, an extended approach for graph layout was presented
that improves the quality of the decomposition and the drawing of directed graphs
according to their topology based on rigorous definitions. The extension involves describing
and analyzing the algorithms for decomposition and drawing in detail giving polynomial time
complexity and space complexity. Finally, I handled visualizing graphs with multi-type edges
using small-multiples, where each tile is dedicated to one edge-type utilizing the topological
graph layout to highlight non-trivial cycles, trees, and DAGs for showing and analyzing the
static structure of software. Finally, I applied this approach to four software systems to show
Dealing with information in modern times involves users to cope with hundreds of thousands of documents, such as articles, emails, Web pages, or News feeds.
Above all information sources, the World Wide Web presents information seekers with great challenges.
It offers more text in natural language than one is capable to read.
The key idea for this research intends to provide users with adaptable filtering techniques, supporting them in filtering out the specific information items they need.
Its realization focuses on developing an Information Extraction system,
which adapts to a domain of concern, by interpreting the contained formalized knowledge.
Utilizing the Resource Description Framework (RDF), which is the Semantic Web's formal language for exchanging information,
allows extending information extractors to incorporate the given domain knowledge.
Because of this, formal information items from the RDF source can be recognized in the text.
The application of RDF allows a further investigation of operations on recognized information items, such as disambiguating and rating the relevance of these.
Switching between different RDF sources allows changing the application scope of the Information Extraction system from one domain of concern to another.
An RDF-based Information Extraction system can be triggered to extract specific kinds of information entities by providing it with formal RDF queries in terms of the SPARQL query language.
Representing extracted information in RDF extends the coverage of the Semantic Web's information degree and provides a formal view on a text from the perspective of the RDF source.
In detail, this work presents the extension of existing Information Extraction approaches by incorporating the graph-based nature of RDF.
Hereby, the pre-processing of RDF sources allows extracting statistical information models dedicated to support specific information extractors.
These information extractors refine standard extraction tasks, such as the Named Entity Recognition, by using the information provided by the pre-processed models.
The post-processing of extracted information items enables representing these results in RDF format or lists, which can now be ranked or filtered by relevance.
Post-processing also comprises the enrichment of originating natural language text sources with extracted information items by using annotations in RDFa format.
The results of this research extend the state-of-the-art of the Semantic Web.
This work contributes approaches for computing customizable and adaptable RDF views on the natural language content of Web pages.
Finally, due to the formal nature of RDF, machines can interpret these views allowing developers to process the contained information in a variety of applications.
This thesis presents a novel, generic framework for information segmentation in document images.
A document image contains different types of information, for instance, text (machine printed/handwritten), graphics, signatures, and stamps.
It is necessary to segment information in documents so that to process such segmented information only when required in automatic document processing workflows.
The main contribution of this thesis is the conceptualization and implementation of an information segmentation framework that is based on part-based features.
The generic nature of the presented framework makes it applicable to a variety of documents (technical drawings, magazines, administrative, scientific, and academic documents) digitized using different methods (scanners, RGB cameras, and hyper-spectral imaging (HSI) devices).
A highlight of the presented framework is that it does not require large training sets, rather a few training samples (for instance, four pages) lead to high performance, i.e., better than previously existing methods.
In addition, the presented framework is simple and can be adapted quickly to new problem domains.
This thesis is divided into three major parts on the basis of document digitization method (scanned, hyper-spectral imaging, and camera captured) used.
In the area of scanned document images, three specific contributions have been realized.
The first of them is in the domain of signature segmentation in administrative documents.
In some workflows, it is very important to check the document authenticity before processing the actual content.
This can be done based on the available seal of authenticity, e.g., signatures.
However, signature verification systems expect pre-segmented signature image, while signatures are usually a part of document.
To use signature verification systems on document images, it is necessary to first segment signatures in documents.
This thesis shows that the presented framework can be used to segment signatures in administrative documents.
The system based on the presented framework is tested on a publicly available dataset where it outperforms the state-of-the-art methods and successfully segmented all signatures, while less than half of the found signatures are false positives.
This shows that it can be applied for practical use.
The second contribution in the area of scanned document images is segmentation of stamps in administrative documents.
A stamp also serves as a seal for documents authenticity.
However, the location of stamp on the document can be more arbitrary than a signature depending on the person sealing the document.
This thesis shows that a system based on our generic framework is able to extract stamps of any arbitrary shape and color.
The evaluation of the presented system on a publicly available dataset shows that it is also able to segment black stamps (that were not addressed in the past) with a recall and precision of 83% and 73%, respectively.
%Furthermore, to segment colored stamps, this thesis presents a novel feature set which is based on intensity gradient, is able to extract unseen, colored, arbitrary shaped, textual as well as graphical stamps, and outperforms the state-of-the-art methods.
The third contribution in the scanned document images is in the domain of information segmentation in technical drawings (architectural floorplans, maps, circuit diagrams, etc.) containing usually a large amount of graphics and comparatively less textual components. Further, as in technical drawings, text is overlapping with graphics.
Thus, automatic analysis of technical drawings uses text/graphics segmentation as a pre-processing step.
This thesis presents a method based on our generic information segmentation framework that is able to detect the text, which is touching graphical components in architectural floorplans and maps.
Evaluation of the method on a publicly available dataset of architectural floorplans shows that it is able to extract almost all touching text components with precision and recall of 71% and 95%, respectively.
This means that almost all of the touching text components are successfully extracted.
In the area of hyper-spectral document images, two contributions have been realized.
Unlike normal three channels RGB images, hyper-spectral images usually have multiple channels that range from ultraviolet to infrared regions including the visible region.
First, this thesis presents a novel automatic method for signature segmentation from hyper-spectral document images (240 spectral bands between 400 - 900 nm).
The presented method is based on a part-based key point detection technique, which does not use any structural information, but relies only on the spectral response of the document regardless of ink color and intensity.
The presented method is capable of segmenting (overlapping and non-overlapping) signatures from varying backgrounds like, printed text, tables, stamps, logos, etc.
Importantly, the presented method can extract signature pixels and not just the bounding boxes.
This is substantial when signatures are overlapping with text and/or other objects in image. Second, this thesis presents a new dataset comprising of 300 documents scanned using a high-resolution hyper-spectral scanner. Evaluation of the presented signature segmentation method on this hyper-spectral dataset shows that it is able to extract signature pixels with the precision and recall of 100% and 79%, respectively.
Further contributions have been made in the area of camera captured document images. A major problem in the development of Optical Character Recognition (OCR) systems for camera captured document images is the lack of labeled camera captured document images datasets. In the first place, this thesis presents a novel, generic, method for automatic ground truth generation/labeling of document images. The presented method builds large-scale (i.e., millions of images) datasets of labeled camera captured / scanned documents without any human intervention. The method is generic and can be used for automatic ground truth generation of (scanned and/or camera captured) documents in any language, e.g., English, Russian, Arabic, Urdu. The evaluation of the presented method, on two different datasets in English and Russian, shows that 99.98% of the images are correctly labeled in every case.
Another important contribution in the area of camera captured document images is the compilation of a large dataset comprising 1 million word images (10 million character images), captured in a real camera-based acquisition environment, along with the word and character level ground truth. The dataset can be used for training as well as testing of character recognition systems for camera-captured documents. Various benchmark tests are performed to analyze the behavior of different open source OCR systems on camera captured document images. Evaluation results show that the existing OCRs, which already get very high accuracies on scanned documents, fail on camera captured document images.
Using the presented camera-captured dataset, a novel character recognition system is developed which is based on a variant of recurrent neural networks, i.e., Long Short Term Memory (LSTM) that outperforms all of the existing OCR engines on camera captured document images with an accuracy of more than 95%.
Finally, this thesis provides details on various tasks that have been performed in the area closely related to information segmentation. This includes automatic analysis and sketch based retrieval of architectural floor plan images, a novel scheme for online signature verification, and a part-based approach for signature verification. With these contributions, it has been shown that part-based methods can be successfully applied to document image analysis.
Today, many publishers (e.g., websites, mobile application developers) commonly use third-party analytics services and social widgets. Unfortunately, this scheme allows these third parties to track individual users across the web, creating privacy concerns and leading to reactions to prevent tracking via blocking, legislation and standards. While improving user privacy, these efforts do not consider the functionality third-party tracking enables publishers to use: to obtain aggregate statistics about their users and increase their exposure to other users via online social networks. Simply preventing third-party tracking without replacing the functionality it provides cannot be a viable solution; leaving publishers without essential services will hurt the sustainability of the entire ecosystem.
The goal of this work is to develop statistical natural language models and processing techniques
based on Recurrent Neural Networks (RNN), especially the recently introduced Long Short-
Term Memory (LSTM). Due to their adapting and predicting abilities, these methods are more
robust, and easier to train than traditional methods, i.e., words list and rule-based models. They
improve the output of recognition systems and make them more accessible to users for browsing
and reading. These techniques are required, especially for historical books which might take
years of effort and huge costs to manually transcribe them.
The contributions of this thesis are several new methods which have high-performance computing and accuracy. First, an error model for improving recognition results is designed. As
a second contribution, a hyphenation model for difficult transcription for alignment purposes
is suggested. Third, a dehyphenation model is used to classify the hyphens in noisy transcription. The fourth contribution is using LSTM networks for normalizing historical orthography.
A size normalization alignment is implemented to equal the size of strings, before the training
phase. Using the LSTM networks as a language model to improve the recognition results is
the fifth contribution. Finally, the sixth contribution is a combination of Weighted Finite-State
Transducers (WFSTs), and LSTM applied on multiple recognition systems. These contributions
will be elaborated in more detail.
Context-dependent confusion rules is a new technique to build an error model for Optical
Character Recognition (OCR) corrections. The rules are extracted from the OCR confusions
which appear in the recognition outputs and are translated into edit operations, e.g., insertions,
deletions, and substitutions using the Levenshtein edit distance algorithm. The edit operations
are extracted in a form of rules with respect to the context of the incorrect string to build an
error model using WFSTs. The context-dependent rules assist the language model to find the
best candidate corrections. They avoid the calculations that occur in searching the language
model and they also make the language model able to correct incorrect words by using context-
dependent confusion rules. The context-dependent error model is applied on the university of
Washington (UWIII) dataset and the Nastaleeq script in Urdu dataset. It improves the OCR
results from an error rate of 1.14% to an error rate of 0.68%. It performs better than the
state-of-the-art single rule-based which returns an error rate of 1.0%.
This thesis describes a new, simple, fast, and accurate system for generating correspondences
between real scanned historical books and their transcriptions. The alignment has many challenges, first, the transcriptions might have different modifications, and layout variations than the
original book. Second, the recognition of the historical books have misrecognition, and segmentation errors, which make the alignment more difficult especially the line breaks, and pages will
not have the same correspondences. Adapted WFSTs are designed to represent the transcription. The WFSTs process Fraktur ligatures and adapt the transcription with a hyphenations
model that allows the alignment with respect to the varieties of the hyphenated words in the line
breaks of the OCR documents. In this work, several approaches are implemented to be used for
the alignment such as: text-segments, page-wise, and book-wise approaches. The approaches
are evaluated on German calligraphic (Fraktur) script historical documents dataset from “Wan-
derungen durch die Mark Brandenburg” volumes (1862-1889). The text-segmentation approach
returns an error rate of 2.33% without using a hyphenation model and an error rate of 2.0%
using a hyphenation model. Dehyphenation methods are presented to remove the hyphen from
the transcription. They provide the transcription in a readable and reflowable format to be used
for alignment purposes. We consider the task as classification problem and classify the hyphens
from the given patterns as hyphens for line breaks, combined words, or noise. The methods are
applied on clean and noisy transcription for different languages. The Decision Trees classifier
returns better performance on UWIII dataset and returns an accuracy of 98%. It returns 97%
on Fraktur script.
A new method for normalizing historical OCRed text using LSTM is implemented for different texts, ranging from Early New High German 14th - 16th centuries to modern forms in New
High German applied on the Luther bible. It performed better than the rule-based word-list
approaches. It provides a transcription for various purposes such as part-of-speech tagging and
n-grams. Also two new techniques are presented for aligning the OCR results and normalize the
size by using adding Character-Epsilons or Appending-Epsilons. They allow deletion and insertion in the appropriate position in the string. In normalizing historical wordforms to modern
wordforms, the accuracy of LSTM on seen data is around 94%, while the state-of-the-art combined rule-based method returns 93%. On unseen data, LSTM returns 88% and the combined
rule-based method returns 76%. In normalizing modern wordforms to historical wordforms, the
LSTM delivers the best performance and returns 93.4% on seen data and 89.17% on unknown
In this thesis, a deep investigation has been done on constructing high-performance language
modeling for improving the recognition systems. A new method to construct a language model
using LSTM is designed to correct OCR results. The method is applied on UWIII and Urdu
script. The LSTM approach outperforms the state-of-the-art, especially for unseen tokens
during training. On the UWIII dataset, the LSTM returns reduction in OCR error rates from
1.14% to 0.48%. On the Nastaleeq script in Urdu dataset, the LSTM reduces the error rate
from 6.9% to 1.58%.
Finally, the integration of multiple recognition outputs can give higher performance than a
single recognition system. Therefore, a new method for combining the results of OCR systems is
explored using WFSTs and LSTM. It uses multiple OCR outputs and votes for the best output
to improve the OCR results. It performs better than the ISRI tool, Pairwise of Multiple Sequence and it helps to improve the OCR results. The purpose is to provide correct transcription
so that it can be used for digitizing books, linguistics purposes, N-grams, and part-of-speech
tagging. The method consists of two alignment steps. First, two recognition systems are aligned
using WFSTs. The transducers are designed to be more flexible and compatible with the different symbols in line and page breaks to avoid the segmentation and misrecognition errors.
The LSTM model then is used to vote the best candidate correction of the two systems and
improve the incorrect tokens which are produced during the first alignment. The approaches
are evaluated on OCRs output from the English UWIII and historical German Fraktur dataset
which are obtained from state-of-the-art OCR systems. The Experiments show that the error
rate of ISRI-Voting is 1.45%, the error rate of the Pairwise of Multiple Sequence is 1.32%, the
error rate of the Line-to-Page alignment is 1.26% and the error rate of the LSTM approach has
the best performance with 0.40%.
The purpose of this thesis is to contribute methods providing correct transcriptions corresponding to the original book. This is considered to be the first step towards an accurate and
more effective use of the documents in digital libraries.
The Context and Its Importance: In safety and reliability analysis, the information generated by Minimal Cut Set (MCS) analysis is large.
The Top Level event (TLE) that is the root of the fault tree (FT) represents a hazardous state of the system being analyzed.
MCS analysis helps in analyzing the fault tree (FT) qualitatively-and quantitatively when accompanied with quantitative measures.
The information shows the bottlenecks in the fault tree design leading to identifying weaknesses of the system being examined.
Safety analysis (containing the MCS analysis) is especially important for critical systems, where harm can be done to the environment or human causing injuries, or even death during the system usage.
Minimal Cut Set (MCS) analysis is performed using computers and generating a lot of information.
This phase is called MCS analysis I in this thesis.
The information is then analyzed by the analysts to determine possible issues and to improve the design of the system regarding its safety as early as possible.
This phase is called MCS analysis II in this thesis.
The goal of my thesis was developing interactive visualizations to support MCS analysis II of one fault tree (FT).
The Methodology: As safety visualization-in this thesis, Minimal Cut Set analysis II visualization-is an emerging field and no complete checklist regarding Minimal Cut Set analysis II requirements and gaps were available from the perspective of visualization and interaction capabilities,
I have conducted multiple studies using different methods with different data sources (i.e., triangulation of methods and data) for determining these requirements and gaps before developing and evaluating visualizations and interactions supporting Minimal Cut Set analysis II.
Thus, the following approach was taken in my thesis:
1- First, a triangulation of mixed methods and data sources was conducted.
2- Then, four novel interactive visualizations and one novel interaction widget were developed.
3- Finally, these interactive visualizations were evaluated both objectively and subjectively (compared to multiple safety tools)
from the point of view of users and developers of the safety tools that perform MCS analysis I with respect to their degree in supporting MCS analysis II and from the point of non-domain people using empirical strategies.
The Spiral tool supports analysts with different visions, i.e., full vision, color deficiency protanopia, deuteranopia, and tritanopia. It supports 100 out of 103 (97%) requirements obtained from the triangulation and it fills 37 out of 39 (95%) gaps. Its usability was rated high (better than their best currently used tools) by the users of the safety and reliability tools (RiskSpectrum, ESSaRel, FaultTree+, and a self-developed tool) and at least similar to the best currently used tools from the point of view of the CAFTA tool developers. Its quality was higher regarding its degree of supporting MCS analysis II compared to the FaultTree+ tool. The time spent for discovering the critical MCSs from a problem size of 540 MCSs (with a worst case of all equal order) was less than a minute while achieving 99.5% accuracy. The scalability of the Spiral visualization was above 4000 MCSs for a comparison task. The Dynamic Slider reduces the interaction movements up to 85.71% of the previous sliders and solves the overlapping thumb issues by the sliders provides the 3D model view of the system being analyzed provides the ability to change the coloring of MCSs according to the color vision of the user provides selecting a BE (i.e., multi-selection of MCSs), thus, can observe the BEs' NoO and provides its quality provides two interaction speeds for panning and zooming in the MCS, BE, and model views provide a MCS, a BE, and a physical tab for supporting the analysis starting by the MCSs, the BEs, or the physical parts. It combines MCS analysis results and the model of an embedded system enabling the analysts to directly relate safety information with the corresponding parts of the system being analyzed and provides an interactive mapping between the textual information of the BEs and MCSs and the parts related to the BEs.
Verifications and Assessments: I have evaluated all visualizations and the interaction widget both objectively and subjectively, and finally evaluated the final Spiral visualization tool also both objectively and subjectively regarding its perceived quality and regarding its degree of supporting MCS analysis II.
Information Visualization (InfoVis) and Human-Computer Interaction (HCI) have strong ties with each other. Visualization supports the human cognitive system by providing interactive and meaningful images of the underlying data. On the other side, the HCI domain cares about the usability of the designed visualization from the human perspectives. Thus, designing a visualization system requires considering many factors in order to achieve the desired functionality and the system usability. Achieving these goals will help these people in understanding the inside behavior of complex data sets in less time.
Graphs are widely used data structures to represent the relations between the data elements in complex applications. Due to the diversity of this data type, graphs have been applied in numerous information visualization applications (e.g., state transition diagrams, social networks, etc.). Therefore, many graph layout algorithms have been proposed in the literature to help in visualizing this rich data type. Some of these algorithms are used to visualize large graphs, while others handle the medium sized graphs. Regardless of the graph size, the resulting layout should be understandable from the users’ perspective and at the same time it should fulfill a list of aesthetic criteria to increase the representation readability. Respecting these two principles leads to produce a resulting graph visualization that helps the users in understanding and exploring the complex behavior of critical systems.
In this thesis, we utilize the graph visualization techniques in modeling the structural and behavioral aspects of embedded systems. Furthermore, we focus on evaluating the resulting representations from the users’ perspectives.
The core contribution of this thesis is a framework, called ESSAVis (Embedded Systems Safety Aspect Visualizer). This framework visualizes not only some of the safety aspects (e.g. CFT models) of embedded systems, but also helps the engineers and experts in analyzing the system safety critical situations. For this, the framework provides a 2Dplus3D environment in which the 2D represents the graph representation of the abstract data about the safety aspects of the underlying embedded system while the 3D represents the underlying system 3D model. Both views are integrated smoothly together in the 3D world fashion. In order to check the effectiveness and feasibility of the framework and its sub-components, we conducted many studies with real end users as well as with general users. Results of the main study that targeted the overall ESSAVis framework show high acceptance ratio and higher accuracy with better performance using the provided visual support of the framework.
The ESSAVis framework has been designed to be compatible with different 3D technologies. This enabled us to use the 3D stereoscopic depth of such technologies to encode nodes attributes in node-link diagrams. In this regard, we conducted an evaluation study to measure the usability of the stereoscopic depth cue approach, called the stereoscopic highlighting technique, against other selected visual cues (i.e., color, shape, and sizes). Based on the results, the thesis proposes the Reflection Layer extension to the stereoscopic highlighting technique, which was also evaluated from the users’ perspectives. Additionally, we present a new technique, called ExpanD (Expand in Depth), that utilizes the depth cue to show the structural relations between different levels of details in node-link diagrams. Results of this part opens a promising direction of the research in which visualization designers can get benefits from the richness of the 3D technologies in visualizing abstract data in the information visualization domain.
Finally, this thesis proposes the application of the ESSAVis frame- work as a visual tool in the educational training process of engineers for understanding the complex concepts. In this regard, we conducted an evaluation study with computer engineering students in which we used the visual representations produced by ESSAVis to teach the principle of the fault detection and the failure scenarios in embedded systems. Our work opens the directions to investigate many challenges about the design of visualization for educational purposes.
A prime motivation for using XML to directly represent pieces of information is the ability of supporting ad-hoc or 'schema-later' settings. In such scenarios, modeling data under loose data constraints is essential. Of course, the flexibility of XML comes at a price: the absence of a rigid, regular, and homogeneous structure makes many aspects of data management more challenging. Such malleable data formats can also lead to severe information quality problems, because the risk of storing inconsistent and incorrect data is greatly increased. A prominent example of such problems is the appearance of the so-called fuzzy duplicates, i.e., multiple and non-identical representations of a real-world entity. Similarity joins correlating XML document fragments that are similar can be used as core operators to support the identification of fuzzy duplicates. However, similarity assessment is especially difficult on XML datasets because structure, besides textual information, may exhibit variations in document fragments representing the same real-world entity. Moreover, similarity computation is substantially more expensive for tree-structured objects and, thus, is a serious performance concern. This thesis describes the design and implementation of an effective, flexible, and high-performance XML-based similarity join framework. As main contributions, we present novel structure-conscious similarity functions for XML trees - either considering XML structure in isolation or combined with textual information -, mechanisms to support the selection of relevant information from XML trees and organization of this information into a suitable format for similarity calculation, and efficient algorithms for large-scale identification of similar, set-represented objects. Finally, we validate the applicability of our techniques by integrating our framework into a native XML database management system; in this context we address several issues around the integration of similarity operations into traditional database architectures.
Since their invention in the 1980s, behaviour-based systems have become very popular among roboticists. Their component-based nature facilitates the distributed implementation of systems, fosters reuse, and allows for early testing and integration. However, the distributed approach necessitates the interconnection of many components into a network in order to realise complex functionalities. This network is crucial to the correct operation of the robotic system. There are few sound design techniques for behaviour networks, especially if the systems shall realise task sequences. Therefore, the quality of the resulting behaviour-based systems is often highly dependant on the experience of their developers.
This dissertation presents a novel integrated concept for the design and verification of behaviour-based systems that realise task sequences. Part of this concept is a technique for encoding task sequences in behaviour networks. Furthermore, the concept provides guidance to developers of such networks. Based on a thorough analysis of methods for defining sequences, Moore machines have been selected for representing complex tasks. With the help of the structured workflow proposed in this work and the developed accompanying tool support, Moore machines defining task sequences can be transferred automatically into corresponding behaviour networks, resulting in less work for the developer and a lower risk of failure.
Due to the common integration of automatically and manually created behaviour-based components, a formal analysis of the final behaviour network is reasonable. For this purpose, the dissertation at hand presents two verification techniques and justifies the selection of model checking. A novel concept for applying model checking to behaviour-based systems is proposed according to which behaviour networks are modelled as synchronised automata. Based on such automata, properties of behaviour networks that realise task sequences can be verified or falsified. Extensive graphical tool support has been developed in order to assist the developer during the verification process.
Several examples are provided in order to illustrate the soundness of the presented design and verification techniques. The applicability of the integrated overall concept to real-world tasks is demonstrated using the control system of an autonomous bucket excavator. It can be shown that the proposed design concept is suitable for developing complex sophisticated behaviour networks and that the presented verification technique allows for verifying real-world behaviour-based systems.
When designing autonomous mobile robotic systems, there usually is a trade-off between the three opposing goals of safety, low-cost and performance.
If one of these design goals is approached further, it usually leads to a recession of one or even both of the other goals.
If for example the performance of a mobile robot is increased by making use of higher vehicle speeds, then the safety of the system is usually decreased, as, under the same circumstances, faster robots are often also more dangerous robots.
This decrease of safety can be mitigated by installing better sensors on the robot, which ensure the safety of the system, even at high speeds.
However, this solution is accompanied by an increase of system cost.
In parallel to mobile robotics, there is a growing amount of ambient and aware technology installations in today's environments - no matter whether in private homes, offices or factory environments.
Part of this technology are sensors that are suitable to assess the state of an environment.
For example, motion detectors that are used to automate lighting can be used to detect the presence of people.
This work constitutes a meeting point between the two fields of robotics and aware environment research.
It shows how data from aware environments can be used to approach the abovementioned goal of establishing safe, performant and additionally low-cost robotic systems.
Sensor data from aware technology, which is often unreliable due to its low-cost nature, is fed to probabilistic methods for estimating the environment's state.
Together with models, these methods cope with the uncertainty and unreliability associated with the sensor data, gathered from an aware environment.
The estimated state includes positions of people in the environment and is used as an input to the local and global path planners of a mobile robot, enabling safe, cost-efficient and performant mobile robot navigation during local obstacle avoidance as well as on a global scale, when planning paths between different locations.
The probabilistic algorithms enable graceful degradation of the whole system.
Even if, in the extreme case, all aware technology fails, the robots will continue to operate, by sacrificing performance while maintaining safety.
All the presented methods of this work have been validated using simulation experiments as well as using experiments with real hardware.