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    Autonomous Operation of a Reconfigurable Multi-Robot System for Planetary Space Missions
    Reconfigurable robots can physically merge and form new types of composite systems. This ability leads to additional degrees of freedom for robot operations especially when dynamically composed robotic systems offer capabilities that none of the individual systems have. Research in the area of reconfigurable multi-robot systems has mainly been focused on swarm-based robots and thereby to systems with a high degree of modularity but a heavily restricted set of capabilities. In contrast, this thesis deals with heterogeneous robot teams comprising individually capable robots which are also modular and reconfigurable. In particular, the autonomous application of such reconfigurable multi-robot systems to enhance robotic space exploration missions is investigated. Exploiting the flexibility of a reconfigurable multi-robot system requires an appropriate system model and reasoner. Hence, this thesis introduces a special organisation model. This model accounts for the key characteristics of reconfigurable robots which are constrained by the availability and compatibility of hardware interfaces. A newly introduced mapping function between resource structures and functional properties permits to characterise dynamically created agent compositions. Since a combinatorial challenge lies in the identification of feasible and functionally suitable agents, this thesis further suggests bounding strategies to reason efficiently with composite robotic systems. This thesis proposes a mission planning algorithm which permits to exploit the flexibility of reconfigurable multi-robot systems. The implemented planner builds upon the developed organisation model so that multi-robot missions can be specified by high-level functionality constraints which are resolved to suitable combinations of robots. Furthermore, the planner synchronises robot activities over time and characterises plans according to three objectives: efficacy, efficiency and safety. The plannera s evaluation demonstrates an optimization of an exemplary space mission. This research is based on the parallel development of theoretical concepts and practical solutions while working with three reconfigurable multi-robot teams. The operation of a reconfigurable robotic team comes with practical constraints. Therefore, this thesis composes and evaluates an operational infrastructure which can serve as reference implementation. The identification and combination of applicable state-of-the-art technologies result in a distributed and dynamically extensible communication infrastructure which can maintain the properties of reconfigurable multi-robot systems. Field tests covering semi-autonomous and autonomous operation have been performed to characterise multi-robot missions and validate the autonomous control approach for reconfigurable multi-robot systems. The practical evaluation identified critical constraints and design elements for a successful application of reconfigurable multi-robot systems. Furthermore, the experiments point to improvements for the organisation model. This thesis is a wholistic approach to automate reconfigurable multi-robot systems. It identifies theoretical as well as practical challenges and it suggests effective solutions which permit an exploitation of an increased level of flexibility in future robotics missions.
    Dissertation
      1624  698
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    Generalizing, Decoding, and Optimizing Support Vector Machine Classification
    The classification of complex data usually requires the composition of processing steps. Here, a major challenge is the selection of optimal algorithms for preprocessing and classification. Nowadays, parts of the optimization process are automized but expert knowledge and manual work are still required. We present three steps to face this process and ease the optimization. Namely, we take a theoretical view on classical classifiers, provide an approach to interpret the classifier together with the preprocessing, and integrate both into one framework which enables a semiautomatic optimization of the processing chain and which interfaces numerous algorithms.
    Dissertation
      924  162
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    Development, Control, and Empirical Evaluation of the Six-Legged Robot SpaceClimber Designed for Extraterrestrial Crater Exploration
    In the recent past, mobile robots played an important role in the field of extraterrestrial surface exploration. Unfortunately, the currently available space exploration rovers do not provide the necessary mobility to reach scientifically interesting places in rough and steep terrain like boulder fields and craters. Multi-legged robots have proven to be a good solution to provide high mobility in unstructured environments. However, space missions place high demands on the system design, control, and performance which are hard to fulfill with such kinematically complex systems. This thesis focuses on the development, control, and evaluation of a six-legged robot for the purpose of lunar crater exploration considering the requirements arising from the envisaged mission scenario. The performance of the developed system is evaluated and optimized based on empirical data acquired in significant and reproducible experiments performed in a laboratory environment in order to show thecapability of the system to perform such a task and to provide a basis for the comparability with other mobile robotic solutions.
    Dissertation
      434  440
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    Design and development of a hominid robot with local control in its adaptable feet to enhance locomotion capabilities
    With increasing mechanization of our daily lives, the expectations and demands in robotic systems increase in the general public and in scientists alike. In recent events such as the Deepwater Horizon''-accident or the nuclear disaster at Fukushima, mobile robotic systems were used, e.g., to support local task forces by gaining visual material to allow an analysis of the situation. Especially the Fukushima example shows that the robotic systems not only have to face a variety of different tasks during operation but also have to deal with different demands regarding the robot's mobility characteristics. To be able to cope with future requirements, it seems necessary to develop kinematically complex systems that feature several different operating modes. That is where this thesis comes in: A robotic system is developed, whose morphology is oriented on chimpanzees and which has the possibility due to its electro-mechanical structure and the degrees of freedom in its arms and legs to walk with different gaits in different postures. For the proposed robot, the chimpanzee was chosen as a model, since these animals show a multitude of different gaits in nature. A quadrupedal gait like crawl allows the robot to traverse safely and stable over rough terrain. A change into the humanoid, bipedal posture enables the robot to move in man-made environments. The structures, which are necessary to ensure an effective and stable locomotion in these two poses, e.g., the feet, are presented in more detail within the thesis. This includes the biological model and an abstraction to allow a technical implementation. In addition, biological spines are analyzed and the development of an active, artificial spine for the robotic system is described. These additional degrees of freedom can increase the robot's locomotion and manipulation capabilities and even allow to show movements, which are not possible without a spine. Unfortunately, the benefits of using an artificial spine in robotic systems are nowadays still neglected, due to the increased complexity of system design and control. To be able to control such a kinematically complex system, a multitude of sensors is installed within the robot's structures. By placing evaluation electronics close by, a local and decentralized preprocessing is realized. Due to this preprocessing is it possible to realize behaviors on the lowest level of robot control: in this thesis it is exemplarily demonstrated by a local controller in the robot's lower leg. In addition to the development and evaluation of robot's structures, the functionality of the overall system is analyzed in different environments. This includes the presentation of detailed data to show the advantages and disadvantages of the local controller. The robot can change its posture independently from a quadrupedal into a bipedal stance and the other way around without external assistance. Once the robot stands upright, it is to investigate to what extent the quadrupedal walking pattern and control structures (like the local controller) have to be modified to contribute to the bipedal walking as well.
    Dissertation
      672  358
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    Constraint basierte Modellierung von Komponentennetzwerken am Beispiel eines autonomen Unterwasserfahrzeugs
    Moderne Robotersysteme werden zunehmend komplexer. Die Forschung im Bereich der Robotik sieht sich immer mehr mit dem Problem der Integration von Algorithmen in neue Plattformen konfrontiert. Es gibt zahlreiche innovative Verfahren und Algorithmen, die die Robotersysteme mit neuen Fähigkeiten ausstatten. Diese Erweiterungen lassen sich sowohl auf der Hardware- als auch der Softwareebene erkennen. Jedoch findet diese Vielzahl an neuen Entwicklungen nur selten Anwendung auf den aktuellen Ziel-Plattformen. Dies bremst weitere Entwicklungen, da mehr Zeit für die Integration als für die eigentliche Forschung aufgewendet wird. Mit einem Blick über die eigene Forschung hinaus oder durch die detailliertere Beschäftigung mit den eingesetzten Verfahren lässt sich erkennen, dass die Lösungen zumeist systemspezifisch sind oder sich nur mit großer Mühe auf andere oder gar größere komplexere Maßstäbe übertragen lassen. Das Ziel dieser Arbeit ist es daher zu identifizieren, wo die größten Herausforderun liegen. Im Anschluss soll das markanteste Problem gelöst werden. Die Modularisierung sowie die modellgetriebene Entwicklung sind dabei die größten Änderungen, die sich Abzeichen. Beide Trends haben verschiedene, teils gravierende Einflüsse auf die Art und Weise, wie Entwicklung stattfindet. Modularisierung erhöht die Wiederverwendbarkeit einzelner Module und führt zu kleineren Verbunden. Modellgetriebene Entwicklung versucht mögliche Fehler in Konzepten zu einem frühen Zeitpunkt zu erkennen. Konkret bedeuten beide Verfahren jedoch Mehrarbeit im Vergleich zu der klassischen monolithischen und reaktiven Systementwicklung. Die Komponenten lassen sich zwar oftmals wiederverwenden, aber die Konsistenz und Nebeneffekte der Selektion bzw. der Abhängigkeiten erfordern viel Zeit und Expertenwissen. Die modellgetriebene Softwareentwicklung verlangt von den Entwicklern von Algorithmen bereits im Voraus Informationen darüber, wie und unter welchen Bedingungen ihr Algorithmus operieren kann. Auf der anderen Seite, der Hardwareentwicklung, stehen immer kleinere und hoch integrierte Sensoren und Aktuatoren zur Verfügung, die wiederum die Konstruktion immer komplexerer Systeme erlauben. Die Systeme bieten oftmals ausreichend Redundanz, um Probleme auf unterschiedliche Art und Weise lösen zu können.
    Dissertation
      349  223
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    Modular and Analytical Methods for Solving Kinematics and Dynamics of Series-Parallel Hybrid Robots
    While serial robots are known for their versatility in applications, larger workspace, simpler modeling and control, they have certain disadvantages like limited precision, lower stiffness and poor dynamic characteristics in general. A parallel robot can offer higher stiffness, speed, accuracy and payload capacity, at the downside of a reduced workspace and a more complex geometry that needs careful analysis and control. To bring the best of the two worlds, parallel submechanism modules can be connected in series to achieve a series-parallel hybrid robot with better dynamic characteristics and larger workspace. Such a design philosophy is being used in several robots not only at DFKI (for e.g., Mantis, Charlie, Recupera Exoskeleton, RH5 humanoid etc.) but also around the world, for e.g. Lola (TUM), Valkyrie (NASA), THOR (Virginia Tech.) etc.These robots inherit the complexity of both serial and parallel architectures. Hence, solving their kinematics and dynamics is challenging because they are subjected to additional geometric loop closure constraints. Most approaches in multi-body dynamics adopt numerical resolution of these constraints for the sake of generality but may suffer from inaccuracy and performance issues. They also do not exploit the modularity in robot design. Further, closed loop systems can have variable mobility, different assembly modes and can impose redundant constraints on the equations of motion which deteriorates the quality of many multi-body dynamics solvers. Very often only a local view to the system behavior is possible. Hence, it is interesting for geometers or kinematics researchers, to study the analytical solutions to geometric problems associated with a specific type of parallel mechanism and their importance over numerical solutions is irrefutable. Techniques such as screw theory, computational algebraic geometry, elimination and continuation methods are popular in this domain. But this domain specific knowledge is often underrepresented in the design of model based kinematics and dynamics software frameworks. The contributions of this thesis are two-fold. Firstly, a rigorous and comprehensive kinematic analysis is performed for the novel parallel mechanisms invented recently at DFKI-RIC such as RH5 ankle mechanism and Active Ankle using approaches from computational algebraic geometry and screw theory. Secondly, the general idea of a modular software framework called Hybrid Robot Dynamics (HyRoDyn) is presented which can be used to solve the geometry, kinematics and dynamics of series-parallel hybrid robotic systems with the help of a software database which stores the analytical solutions for parallel submechanism modules in a configurable and unit testable manner. HyRoDyn approach is suitable for both high fidelity simulations and real-time control of complex series-parallel hybrid robots. The results from this thesis has been applied to two robotic systems namely Recupera-Reha exoskeleton and RH5 humanoid. The aim of this software tool is to assist both designers and control engineers in developing complex robotic systems of the future. Efficient kinematic and dynamic modeling can lead to more compliant behavior, better whole body control, walking and manipulating capabilities etc. which are highly desired in the present day and future robotic applications.
    Dissertation
      1008  1195
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    Object Recognition and Localization : the Role of Tactile Sensors
    Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving object recognition and localization. This thesis presents two approaches for haptic object recognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Sequential Filter (BRICPSF) is based on an innovative combination of a sequential filter, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC) algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in simulation and using actual hardware. To our knowledge this is the first instance of haptic object recognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses BRICPSF for object part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments.
    Dissertation
      487  283
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    Learning the Structure of Continuous Markov Decision Processes
    There is growing interest in artificial, intelligent agents which can operate autonomously for an extended period of time in complex environments and fulfill a variety of different tasks. Such agents will face different problems during their lifetime which may not be foreseeable at the time of their deployment. Thus, the capacity for lifelong learning of new behaviors is an essential prerequisite for this kind of agents as it enables them to deal with unforeseen situations. However, learning every complex behavior anew from scratch would be cumbersome for the agent. It is more plausible to consider behavior to be modular and let the agent acquire a set of reusable building blocks for behavior, the so-called skills. These skills might, once acquired, facilitate fast learning and adaptation of behavior to new situations. This work focuses on computational approaches for skill acquisition, namely which kind of skills shall be acquired and how to acquire them. The former is commonly denoted as "skill discovery" and the latter as "skill learning". The main contribution of this thesis is a novel incremental skill acquisition approach which is suited for lifelong learning. In this approach, the agent learns incrementally a graph-based representation of a domain and exploits certain properties of this graph such as its bottlenecks for skill discovery. This thesis proposes a novel approach for learning a graph-based representation of continuous domains based on formalizing the problem as a probabilistic generative model. Furthermore, a new incremental agglomerative clustering approach for identifying bottlenecks of such graphs is presented. Thereupon, the thesis proposes a novel intrinsic motivation system which enables an agent to intelligently allocate time between skill discovery and skill learning in developmental settings, where the agent is not constrained by external tasks. The results of this thesis show that the resulting skill acquisition approach is suited for continuous domains and can deal with domain stochasticity and different explorative behavior of the agent. The acquired skills are reusable and versatile and can be used in multi-task and lifelong learning settings in high-dimensional problems.
    Dissertation
      629  188
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    Evolutionary Legged Robotics
    Due to the technological advance, robotic systems become more and more interesting for industrial and home applications. Popular examples are given by robotic lawn mower, robot vacuum cleaner, and package drones. Beside the toy industry, legged robots are not as popular, although they have some clear advantages compared to wheeled systems. With their flexibility concerning the locomotion, they are able to adapt their walking pattern to different environments. For instance they can walk over obstacles and gaps or climb over rubble and stairs. Another possible advantage could be a redundancy for locomotion. A faulty motor in one limb could be compensated by other motors in the kinematic chain. As well, multiple failing legs can be compensated by an adapted walking pattern. Compared to this, the more complex mechatronic systems represent a major challenge to the construction and the control. This thesis is dedicated to the control of complex walking robots. Genetic algorithms are applied to generate walking patterns for different robots. The evolutionary development of walking patterns is done in a simulation software. Results of various approaches are transferred and tested on existing systems which have been developed at RIC/DFKI. Different robotic systems are used to evaluate the generality of the applied methods. Eventually, a method is developed that can be utilized, with a few system specific modifications, for a variety of legged robots. As basis for the development and investigation of several methods, software tools are designed to generalize the application of applying genetic algorithms to legged locomotion. These tools include a simulation environment, a behavior representation, a genetic algorithm and a learning and benchmark framework. The simulation environment is adapted to the behavior of real robotic systems via reference experiments. In addition, the simulation is extended by a foot contact model for loose surfaces. The evaluation of the genetic algorithm is done on several benchmark problems and compared to three existing algorithms. This thesis contributes to the state of the art in many areas. The developed methodology can easily be applied to several complex robotic systems due to its transferability. The genetic algorithm and the hierarchical behavior representation provide a new opportunity to control the generation of the offspring in an evolutionary process. In addition, the developed software tools are an important contribution for their respective research fields.
    Dissertation
      504  210
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    Adaptive Localization and Mapping for Planetary Rovers
    Future rovers will be equipped with substantial onboard autonomy as space agencies and industry proceed with missions studies and technology development in preparation for the next planetary exploration missions. Simultaneous Localization and Mapping (SLAM) is a fundamental part of autonomous capabilities and has close connections to robot perception, planning and control. SLAM positively affects rover operations and mission success. The SLAM community has made great progress in the last decade by enabling real world solutions in terrestrial applications and is nowadays addressing important challenges in robust performance, scalability, high-level understanding, resources awareness and domain adaptation. In this thesis, an adaptive SLAM system is proposed in order to improve rover navigation performance and demand. This research presents a novel localization and mapping solution following a bottom-up approach. It starts with an Attitude and Heading Reference System (AHRS), continues with a 3D odometry dead reckoning solution and builds up to a full graph optimization scheme which uses visual odometry and takes into account rover traction performance, bringing scalability to modern SLAM solutions. A design procedure is presented in order to incorporate inertial sensors into the AHRS. The procedure follows three steps: error characterization, model derivation and filter design. A complete kinematics model of the rover locomotion subsystem is developed in order to improve the wheel odometry solution. Consequently, the parametric model predicts delta poses by solving a system of equations with weighed least squares. In addition, an odometry error model is learned using Gaussian processes (GPs) in order to predict non-systematic errors induced by poor traction of the rover with the terrain. The odometry error model complements the parametric solution by adding an estimation of the error. The gained information serves to adapt the localization and mapping solution to the current navigation demands (domain adaptation). The adaptivity strategy is designed to adjust the visual odometry computational load (active perception) and to influence the optimization back-end by including highly informative keyframes in the graph (adaptive information gain). Following this strategy, the solution is adapted to the navigation demands, providing an adaptive SLAM system driven by the navigation performance and conditions of the interaction with the terrain. The proposed methodology is experimentally verified on a representative planetary rover under realistic field test scenarios. This thesis introduces a modern SLAM system which adapts the estimated pose and map to the predicted error. The system maintains accuracy with fewer nodes, taking the best of both wheel and visual methods in a consistent graph-based smoothing approach.
    Dissertation
      747  311