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Based on experiences from an autonomous mobile robot project called MOBOT -III, we found hard realtime-constraints for the operating-system-design. ALBATROSS is "A flexible multi-tasking and realtime network-operatingsystem-kernel", not limited to mobile- robot-projects only, but which might be useful also wherever you have to guarantee a high reliability of a realtime-system. The focus in this article is on a communication-scheme fulfilling the demanded (hard realtime-) assurances although not implying time-delays or jitters on the critical informationchannels. The central chapters discuss a locking-free shared buffer management, without the need for interrupts and a way to arrange the communication architecture in order to produce minimal protocol-overhead and short cycle-times. Most of the remaining communication-capacity (if there is any) is used for redundant transfers, increasing the reliability of the whole system. ALBATROSS is actually implemented on a multi-processor VMEbus-system.
This paper refers to the problem of adaptability over an infinite period of time, regarding dynamic networks. A never ending flow of examples have to be clustered, based on a distance measure. The developed model is based on the self-organizing feature maps of Kohonen [6], [7] and some adaptations by Fritzke [3]. The problem of dynamic surface classification is embedded in the SPIN project, where sub-symbolic abstractions, based on a 3-d scanned environment is being done.
The problem to be discussed here, is the usage of neural network clustering techniques on a mobile robot, in order to build qualitative topologic environment maps. This has to be done in realtime, i.e. the internal world model has to be adapted by the flow of sensor- samples without the possibility to stop this data-flow.Our experiments are done in a simulation environment as well as on a robot, called ALICE.
Based on the experiences from an autonomous mobile robot project called MOBOT-III, we found hard realtime-constraints for the operating- system-design. ALBATROSS is "A flexible multi-tasking and realtime network-operating-system-kernel". The focusin this article is on a communication-scheme fulfilling the previous demanded assurances. The centralchapters discuss the shared buffer management and the way to design the communication architecture.Some further aspects beside the strict realtime-requirements like the possibilities to control and watch a running system, are mentioned. ALBATROSS is actually implemented on a multi-processor VMEbus-system.
Based on the idea of using topologic feature-mapsinstead of geometric environment maps in practical mobile robot tasks, we show an applicable way tonavigate on such topologic maps. The main features regarding this kind of navigation are: handling of very inaccurate position (and orientation) information as well as implicit modelling of complex kinematics during an adaptation phase. Due to the lack of proper a-priori knowledge, a re-inforcement based model is used for the translation of navigator commands to motor actions. Instead of employing a backpropagation network for the cen-tral associative memory module (attaching actionprobabilities to sensor situations resp. navigatorcommands) a much faster dynamic cell structure system based on dynamic feature maps is shown. Standard graph-search heuristics like A* are applied in the planning phase.
SPIN-NFDS Learning and Preset Knowledge for Surface Fusion - A Neural Fuzzy Decision System -
(1993)
The problem to be discussed in this paper may be characterized in short by the question: "Are these two surface fragments belonging together (i.e. belonging to the same surface)?" The presented techniques try to benefit from some predefined knowledge as well as from the possibility to refine and adapt this knowledge according to a (changing) real environment, resulting in a combination of fuzzy-decision systems and neural networks. The results are encouraging (fast convergence speed, high accuracy), and the model might be used for a wide range of applications. The general frame surrounding the work in this paper is the SPIN- project, where emphasis is on sub-symbolic abstractions, based on a 3-d scanned environment.
This article will discuss a qualitative, topological and robust world-modelling technique with special regard to navigation-tasks for mobile robots operating in unknownenvironments. As a central aspect, the reliability regarding error-tolerance and stability will be emphasized. Benefits and problems involved in exploration, as well as in navigation tasks, are discussed. The proposed method demands very low constraints for the kind and quality of the employed sensors as well as for the kinematic precision of the utilized mobile platform. Hard real-time constraints can be handled due to the low computational complexity. The principal discussions are supported by real-world experiments with the mobile robot
Self-localization in unknown environments respectively correlation of current and former impressions of the world is an essential ability for most mobile robots. The method,proposed in this article is the construction of a qualitative, topological world model as a basis for self-localization. As a central aspect the reliability regarding error-tolerance and stability will be emphasized. The proposed techniques demand very low constraints for the kind and quality of the employed sensors as well as for the kinematic precisionof the utilized mobile platform. Hard real-time constraints can be handled due to the low computational complexity. The principal discussions are supported by real-world experiments with the mobile robot.
Visual Search has been investigated by many researchers inspired by the biological fact, that the sensory elements on the mammal retina are not equably distributed. Therefore the focus of attention (the area of the retina with the highest density of sensory elements) has to be directed in a way to efficiently gather data according to certain criteria. The work discussed in this article concentrates on applying a laser range finder instead of a silicon retina. The laser range finder is maximal focused at any time, but therefore a low resolution total-scene-image, available with camera-like devices from scratch on, cannot be used here. By adapting a couple of algorithms, the edge-scanning module steering the laser range finder is able to trace a detected edge. Based on the data scanned so far , two questions have to be answered. First: "Should the actual (edge-) scanning be interrupted in order to give another area of interest a chance of being investigated?" and second: "Where to start a new edge-scanning, after being interrupted?". These two decision-problems might be solved by a range of decision systems. The correctness of the decisions depends widely on the actual environment and the underlying rules may not be well initialized with a-priori knowledge. So we will present a version of a reinforcement decision system together with an overall scheme for efficiently controlling highly focused devices.
World models for mobile robots as introduced in many projects, are mostly redundant regarding similar situations detected in different places. The present paper proposes a method for dynamic generation of a minimal world model based on these redundancies. The technique is an extention of the qualitative topologic world modelling methods. As a central aspect the reliability regarding errortolerance and stability will be emphasized. The proposed technique demands very low constraints on the kind and quality of the employed sensors as well as for the kinematic precision of the utilized mobile platform. Hard realtime constraints can be handled due to the low computational complexity. The principal discussions are supported by real-world experiments with the mobile robot "
ALICE
(1994)
Planar force or pressure is a fundamental physical aspect during any people-vs-people and people-vs-environment activities and interactions. It is as significant as the more established linear and angular acceleration (usually acquired by inertial measurement units). There have been several studies involving planar pressure in the discipline of activity recognition, as reviewed in the first chapter. These studies have shown that planar pressure is a promising sensing modality for activity recognition. However, they still take a niche part in the entire discipline, using ad hoc systems and data analysis methods. Mostly these studies were not followed by further elaborative works. The situation calls for a general framework that can help push planar pressure sensing into the mainstream.
This dissertation systematically investigates using planar pressure distribution sensing technology for ubiquitous and wearable activity recognition purposes. We propose a generic Textile Pressure Mapping (TPM) Framework, which encapsulates (1) design knowledge and guidelines, (2) a multi-layered tool including hardware, software and algorithms, and (3) an ensemble of empirical study examples. Through validation with various empirical studies, the unified TPM framework covers the full scope of application recognition, including the ambient, object, and wearable subspaces.
The hardware part constructs a general architecture and implementations in the large-scale and mobile directions separately. The software toolkit consists of four heterogeneous tiers: driver, data processing, machine learning, visualization/feedback. The algorithm chapter describes generic data processing techniques and a unified TPM feature set. The TPM framework offers a universal solution for other researchers and developers to evaluate TPM sensing modality in their application scenarios.
The significant findings from the empirical studies have shown that TPM is a versatile sensing modality. Specifically, in the ambient subspace, a sports mat or carpet with TPM sensors embedded underneath can distinguish different sports activities or different people's gait based on the dynamic change of body-print; a pressure sensitive tablecloth can detect various dining actions by the force propagated from the cutlery through the plates to the tabletop. In the object subspace, swirl office chairs with TPM sensors under the cover can be used to detect the seater's real-time posture; TPM can be used to detect emotion-related touch interactions for smart objects, toys or robots. In the wearable subspace, TPM sensors can be used to perform pressure-based mechanomyography to detect muscle and body movement; it can also be tailored to cover the surface of a soccer shoe to distinguish different kicking angles and intensities.
All the empirical evaluations have resulted in accuracies well-above the chance level of the corresponding number of classes, e.g., the `swirl chair' study has classification accuracy of 79.5% out of 10 posture classes and in the `soccer shoe' study the accuracy is 98.8% among 17 combinations of angle and intensity.
Nowadays, accounting, charging and billing users' network resource consumption are commonly used for the purpose of facilitating reasonable network usage, controlling congestion, allocating cost, gaining revenue, etc. In traditional IP traffic accounting systems, IP addresses are used to identify the corresponding consumers of the network resources. However, there are some situations in which IP addresses cannot be used to identify users uniquely, for example, in multi-user systems. In these cases, network resource consumption can only be ascribed to the owners of these hosts instead of corresponding real users who have consumed the network resources. Therefore, accurate accountability in these systems is practically impossible. This is a flaw of the traditional IP address based IP traffic accounting technique. This dissertation proposes a user based IP traffic accounting model which can facilitate collecting network resource usage information on the basis of users. With user based IP traffic accounting, IP traffic can be distinguished not only by IP addresses but also by users. In this dissertation, three different schemes, which can achieve the user based IP traffic accounting mechanism, are discussed in detail. The inband scheme utilizes the IP header to convey the user information of the corresponding IP packet. The Accounting Agent residing in the measured host intercepts IP packets passing through it. Then it identifies the users of these IP packets and inserts user information into the IP packets. With this mechanism, a meter located in a key position of the network can intercept the IP packets tagged with user information, extract not only statistic information, but also IP addresses and user information from the IP packets to generate accounting records with user information. The out-of-band scheme is a contrast scheme to the in-band scheme. It also uses an Accounting Agent to intercept IP packets and identify the users of IP traffic. However, the user information is transferred through a separated channel, which is different from the corresponding IP packets' transmission. The Multi-IP scheme provides a different solution for identifying users of IP traffic. It assigns each user in a measured host a unique IP address. Through that, an IP address can be used to identify a user uniquely without ambiguity. This way, traditional IP address based accounting techniques can be applied to achieve the goal of user based IP traffic accounting. In this dissertation, a user based IP traffic accounting prototype system developed according to the out-of-band scheme is also introduced. The application of user based IP traffic accounting model in the distributed computing environment is also discussed.
Conditional Compilation (CC) is frequently used as a variation mechanism in software product lines (SPLs). However, as a SPL evolves the variable code realized by CC erodes in the sense that it becomes overly complex and difficult to understand and maintain. As a result, the SPL productivity goes down and puts expected advantages more and more at risk. To investigate the variability erosion and keep the productivity above a sufficiently good level, in this paper we 1) investigate several erosion symptoms in an industrial SPL; 2) present a variability improvement process that includes two major improvement strategies. While one strategy is to optimize variable code within the scope of CC, the other strategy is to transition CC to a new variation mechanism called Parameterized Inclusion. Both of these two improvement strategies can be conducted automatically, and the result of CC optimization is provided. Related issues such as applicability and cost of the improvement are also discussed.
As a Software Product Line (SPL) evolves with increasing number of features and feature values, the feature correlations become extremely intricate, and the specifications of these correlations tend to be either incomplete or inconsistent with their realizations, causing misconfigurations in practice. In order to guide product configuration processes, we present a solution framework to recover complex feature correlations from existing product configurations. These correlations are further pruned automatically and validated by domain experts. During implementation, we use association mining techniques to automatically extract strong association rules as potential feature correlations. This approach is evaluated using a large-scale industrial SPL in the embedded system domain, and finally we identify a large number of complex feature correlations.
Automata theory has given rise to a variety of automata models that consist
of a finite-state control and an infinite-state storage mechanism. The aim
of this work is to provide insights into how the structure of the storage
mechanism influences the expressiveness and the analyzability of the
resulting model. To this end, it presents generalizations of results about
individual storage mechanisms to larger classes. These generalizations
characterize those storage mechanisms for which the given result remains
true and for which it fails.
In order to speak of classes of storage mechanisms, we need an overarching
framework that accommodates each of the concrete storage mechanisms we wish
to address. Such a framework is provided by the model of valence automata,
in which the storage mechanism is represented by a monoid. Since the monoid
serves as a parameter to specifying the storage mechanism, our aim
translates into the question: For which monoids does the given
(automata-theoretic) result hold?
As a first result, we present an algebraic characterization of those monoids
over which valence automata accept only regular languages. In addition, it
turns out that for each monoid, this is the case if and only if valence
grammars, an analogous grammar model, can generate only context-free
languages.
Furthermore, we are concerned with closure properties: We study which
monoids result in a Boolean closed language class. For every language class
that is closed under rational transductions (in particular, those induced by
valence automata), we show: If the class is Boolean closed and contains any
non-regular language, then it already includes the whole arithmetical
hierarchy.
This work also introduces the class of graph monoids, which are defined by
finite graphs. By choosing appropriate graphs, one can realize a number of
prominent storage mechanisms, but also combinations and variants thereof.
Examples are pushdowns, counters, and Turing tapes. We can therefore relate
the structure of the graphs to computational properties of the resulting
storage mechanisms.
In the case of graph monoids, we study (i) the decidability of the emptiness
problem, (ii) which storage mechanisms guarantee semilinear Parikh images,
(iii) when silent transitions (i.e. those that read no input) can be
avoided, and (iv) which storage mechanisms permit the computation of
downward closures.
Today, information systems are often distributed to achieve high availability and low latency.
These systems can be realized by building on a highly available database to manage the distribution of data.
However, it is well known that high availability and low latency are not compatible with strong consistency guarantees.
For application developers, the lack of strong consistency on the database layer can make it difficult to reason about their programs and ensure that applications work as intended.
We address this problem from the perspective of formal verification.
We present a specification technique, which allows specifying functional properties of the application.
In addition to data invariants, we support history properties.
These let us express relations between events, including invocations of the application API and operations on the database.
To address the verification problem, we have developed a proof technique that handles concurrency using invariants and thereby reduces the problem to sequential verification.
The underlying system semantics, technique and its soundness proof are all formalized in the interactive theorem prover Isabelle/HOL.
Additionally, we have developed a tool named Repliss which uses the proof technique to enable partially automated verification and testing of applications.
For verification, Repliss generates verification conditions via symbolic execution and then uses an SMT solver to discharge them.
Wireless LANs operating within unlicensed frequency bands require random access schemes such as CSMA/ CA, so that wireless networks from different administrative domains (for example wireless community networks) may co-exist without central coordination, even when they happen to operate on the same radio channel. Yet, it is evident that this Jack of coordination leads to an inevitable loss in efficiency due to contention on the MAC layer. The interesting question is, which efficiency may be gained by adding coordination to existing, unrelated wireless networks, for example by self-organization. In this paper, we present a methodology based on a mathematical programming formulation to determine the
parameters (assignment of stations to access points, signal strengths and channel assignment of both access points and stations) for a scenario of co-existing CSMA/ CA-based wireless networks, such that the contention between these networks is minimized. We demonstrate how it is possible to solve this discrete, non-linear optimization problem exactly for small
problems. For larger scenarios, we present a genetic algorithm specifically tuned for finding near-optimal solutions, and compare its results to theoretical lower bounds. Overall, we provide a benchmark on the minimum contention problem for coordination mechanisms in CSMA/CA-based wireless networks.
With the technological advancement in the field of robotics, it is now quite practical to acknowledge the actuality of social robots being a part of human's daily life in the next decades. Concerning HRI, the basic expectations from a social robot are to perceive words, emotions, and behaviours, in order to draw several conclusions and adapt its behaviour to realize natural HRI. Henceforth, assessment of human personality traits is essential to bring a sense of appeal and acceptance towards the robot during interaction.
Knowledge of human personality is highly relevant as far as natural and efficient HRI is concerned. The idea is taken from human behaviourism, with humans behaving differently based on the personality trait of the communicating partners. This thesis contributes to the development of personality trait assessment system for intelligent human-robot interaction.
The personality trait assessment system is organized in three separate levels. The first level, known as perceptual level, is responsible for enabling the robot to perceive, recognize and understand human actions in the surrounding environment in order to make sense of the situation. Using psychological concepts and theories, several percepts have been extracted. A study has been conducted to validate the significance of these percepts towards personality traits.
The second level, known as affective level, helps the robot to connect the knowledge acquired in the first level to make higher order evaluations such as assessment of human personality traits. The affective system of the robot is responsible for analysing human personality traits. To the best of our knowledge, this thesis is the first work in the field of human-robot interaction that presents an automatic assessment of human personality traits in real-time using visual information. Using psychology and cognitive studies, many theories has been studied. Two theories have been been used to build the personality trait assessment system: Big Five personality traits assessment and temperament framework for personality traits assessment.
By using the information from the perceptual and affective level, the last level, known as behavioural level, enables the robot to synthesize an appropriate behaviour adapted to human personality traits. Multiple experiments have been conducted with different scenarios. It has been shown that the robot, ROBIN, assesses personality traits correctly during interaction and uses the similarity-attraction principle to behave with similar personality type. For example, if the person is found out to be extrovert, the robot also behaves like an extrovert. However, it also uses the complementary attraction theory to adapt its behaviour and complement the personality of the interaction partner. For example, if the person is found out to be self-centred, the robot behaves like an agreeable in order to flourish human-robot interaction.