Climbing and Walking Robots

Nowadays robotics is one of the most dynamic fields of scientific researches. The shift of robotics researches from manufacturing to services applications is clear. During the last decades interest in studying climbing and walking robots has been increased. This increasing interest has been in many areas that most important ones of them are: mechanics, electronics, medical engineering, cybernetics, controls, and computers. Today’s climbing and walking robots are a combination of manipulative, perceptive, communicative, and cognitive abilities and they are capable of performing many tasks in industrial and non- industrial environments. Surveillance, planetary exploration, emergence rescue operations, reconnaissance, petrochemical applications, construction, entertainment, personal services, intervention in severe environments, transportation, medical and etc are some applications from a very diverse application fields of climbing and walking robots. By great progress in this area of robotics it is anticipated that next generation climbing and walking robots will enhance lives and will change the way the human works, thinks and makes decisions. This book presents the state of the art achievments, recent developments, applications and future challenges of climbing and walking robots. These are presented in 24 chapters by authors throughtot the world The book serves as a reference especially for the researchers who are interested in mobile robots. It also is useful for industrial engineers and graduate students in advanced study

Cognitively Inspired Computational Memory Model with Applications in Robotics

У дисертацији је представљен нови рачунарски модел дуготрајне меморије, намењен за примене у конверзаци- оним роботским агентима. Предложени модел је симболи- чки, са методолошког аспекта, и инспирисан је изабраним когнитивним механизмима људског меморијског система, који укључују интеграцију менталних репрезентација, семантичку категоризацију, асоцијативно учење и контек- стно зависно селектовање информација. У основи модела се налази симболички приступ за аутоматско моделовање домена интеракције између човека и робота. Релевантни функциoнaлни aспeкт предложеног модела oднoси се нa прoблeме адекватног aктивирaњa делова дуготрајне мeмoриje, у складу са спољашњим стимулансима, истори- јом интеракције и тренутним контекстом интеракције. Ниво апстракције у спецификацији модела је довољан да омогући примену модела у широком спектру просторних, униформних домена који су карактеристични за интеракцију између човека и робота, а ниво детаља у спецификацији је довољан за рачунарску имплементацију модела.U disertaciji je predstavljen novi računarski model dugotrajne memorije, namenjen za primene u konverzaci- onim robotskim agentima. Predloženi model je simboli- čki, sa metodološkog aspekta, i inspirisan je izabranim kognitivnim mehanizmima ljudskog memorijskog sistema, koji uključuju integraciju mentalnih reprezentacija, semantičku kategorizaciju, asocijativno učenje i kontek- stno zavisno selektovanje informacija. U osnovi modela se nalazi simbolički pristup za automatsko modelovanje domena interakcije između čoveka i robota. Relevantni funkcionalni aspekt predloženog modela odnosi se na probleme adekvatnog aktiviranja delova dugotrajne memorije, u skladu sa spoljašnjim stimulansima, istori- jom interakcije i trenutnim kontekstom interakcije. Nivo apstrakcije u specifikaciji modela je dovoljan da omogući primenu modela u širokom spektru prostornih, uniformnih domena koji su karakteristični za interakciju između čoveka i robota, a nivo detalja u specifikaciji je dovoljan za računarsku implementaciju modela.This dissertation proposes a novel computational model of long-term memory intended for applications in conversational robotic agents. The proposed model is symbolic, from the methodological point of view, and cognitively-inspired by selected cognitive mechanisms of the human memory system, including integration of mental representations, semantic categorization, associative learning, and context-dependent information selection. In the core of the model there is a symbolic approach to automatic modeling of domains of human-robot interaction. The relevant functional aspect of the proposed model concerns the problems of context-dependent retrieval from long-term memory, in accordance with external stimuli, the interaction history, and the current context of interaction. The level of abstraction in the model is sufficient to enable generalization of the model over a range of spatial, uniform domains that are characterical for human-robot interaction, while the level of detail contained in the specification of the model is sufficient for a computational implementation

Legged locomotion over irregular terrains: State of the art of human and robot performance

Legged robotic technologies have moved out of the lab to operate in real environments, characterized by a wide variety of unpredictable irregularities and disturbances, all this in close proximity with humans. Demonstrating the ability of current robots to move robustly and reliably in these conditions is becoming essential to prove their safe operation. Here, we report an in-depth literature review aimed at verifying the existence of common or agreed protocols and metrics to test the performance of legged system in realistic environments. We primarily focused on three types of robotic technologies, i.e., hexapods, quadrupeds and bipeds. We also included a comprehensive overview on human locomotion studies, being it often considered the gold standard for performance, and one of the most important sources of bioinspiration for legged machines. We discovered that very few papers have rigorously studied robotic locomotion under irregular terrain conditions. On the contrary, numerous studies have addressed this problem on human gait, being nonetheless of highly heterogeneous nature in terms of experimental design. This lack of agreed methodology makes it challenging for the community to properly assess, compare and predict the performance of existing legged systems in real environments. On the one hand, this work provides a library of methods, metrics and experimental protocols, with a critical analysis on the limitations of the current approaches and future promising directions. On the other hand, it demonstrates the existence of an important lack of benchmarks in the literature, and the possibility of bridging different disciplines, e.g., the human and robotic, towards the definition of standardized procedure that will boost not only the scientific development of better bioinspired solutions, but also their market uptake

Locomotion through morphology, evolution and learning for legged and limbless robots

Mención Internacional en el título de doctorRobot locomotion is concerned with providing autonomous locomotion capabilities to mobile robots. Most current day robots feature some form of locomotion for navigating in their environment. Modalities of robot locomotion includes: (i) aerial locomotion, (ii) terrestrial locomotion, and (iii) aquatic locomotion (on or under water). Three main forms of terrestrial locomotion are, legged locomotion, limbless locomotion and wheel-based locomotion. A Modular Robot (MR), on the other hand, is a robotic system composed of several independent unit modules, where, each module is a robot by itself. The objective in this thesis is to develop legged locomotion in a humanoid robot, as well as, limbless locomotion in modular robotic configurations. Taking inspiration from biology, robot locomotion from the perspective of robot’s morphology, through evolution, and through learning are investigated in this thesis. Locomotion is one of the key distinguishing characteristics of a zoological organism. Almost all animal species, and even some plant species, produce some form of locomotion. In the past few years, robots have been “moving out” of the factory floor and research labs, and are becoming increasingly common in everyday life. So, providing stable and agile locomotion capabilities for robots to navigate a wide range of environments becomes pivotal. Developing locomotion in robots through biologically inspired methods, also facilitates furthering our understanding on how biological processes may function. Connected modules in a configuration, exert force on each other as a result of interaction between each other and their environment. This phenomenon is studied and quantified, and then used as implicit communication between robot modules for producing locomotion coordination in MRs. Through this, a strong link between robot morphology and the gait that emerge in it is established. A variety of locomotion controller, some periodic-function based and some morphology based, are developed for MR locomotion and bipedal gait generation. A hybrid Evolutionary Algorithm (EA) is implemented for evolving gaits, both in simulation as well as in the real-world on a physical modular robotic configuration. Limbless gaits in MRs are also learnt by learning optimal control policies, through Reinforcement Learning (RL).En robótica, la locomoción trata de proporcionar capacidades de locomoción autónoma a robots móviles. La mayoría de los robots actuales tiene alguna forma de locomoción para navegar en su entorno. Los modos de locomoción robótica se pueden repartir entre: (i) locomoción aérea, (ii) locomoción terrestre, y (iii) locomoción acuática (sobre o bajo el agua). Las tres formas básicas de locomoción terrestre son la locomoción mediante piernas, la locomoción sin miembros, y la locomoción basada en ruedas. Un Robot Modular, por otra parte, es un sistema robótico compuesto por varios módulos independientes, donde cada módulo es un robot en sí mismo. El objetivo de esta tesis es el desarrollo de la locomoción mediante piernas para un robot humanoide, así como el de la locomoción sin miembros para varias configuraciones de robots modulares. Inspirándose en la biología, también se investiga en esta tesis el desarrollo de la locomoción del robot según su morfología, gracias a técnicas de evolución y de aprendizaje. La locomoción es una de las características distintivas de un organismo zoológico. Casi todas las especies animales, e incluso algunas especies de plantas, poseen algún tipo de locomoción. En los últimos años, los robots han “migrado” desde las fábricas y los laboratorios de investigación, y se están integrando cada vez más en nuestra vida diaria. Por estas razones, es crucial proporcionar capacidades de locomoción estables y ágiles a los robots para que puedan navegar por todo tipo de entornos. El uso de métodos de inspiración biológica para alcanzar esta meta también nos ayuda a entender mejor cómo pueden funcionar los procesos biológicos equivalentes. En una configuración de módulos conectados, puesto que cada uno interacciona con su entorno, los módulos ejercen fuerza los unos sobre los otros. Este fenómeno se ha estudiado y cuantificado, y luego se ha usado como comunicación implícita entre los módulos para producir la coordinación en la locomoción de este robot. De esta manera, se establece un fuerte vínculo entre la morfología de un robot y el modo de andar que este desarrolla. Se han desarrollado varios controladores de locomoción para robots modulares y robots bípedos, algunos basados en funciones periódicas, otros en la morfología del robot. Un algoritmo evolutivo híbrido se ha implementado para la evolución de locomociones, tanto en simulación como en el mundo real en una configuración física de robot modular. También se pueden generar locomociones sin miembros para robots modulares, determinando las políticas de control óptimo gracias a técnicas de aprendizaje por refuerzo. Se presenta en primer lugar en esta tesis el estado del arte de la robótica modular, enfocándose en la locomoción de robots modulares, los controladores, la locomoción bípeda y la computación morfológica. A continuación se describen cinco configuraciones diferentes de robot modular que se utilizan en esta tesis, seguido de cuatro controladores de locomoción. Estos controladores son el controlador heterogéneo, el controlador basado en funciones periódicas, el controlador homogéneo y el controlador basado en la morfología del robot. Se desarrolla como parte de este trabajo un controlador de locomoción lineal, periódico, basado en features, para la locomoción bípeda de robots humanoides. Los parámetros de control se ajustan primero a mano para reproducir un modelo cart-table, y el controlador se evalúa en un robot humanoide simulado. A continuación, gracias a un algoritmo evolutivo, la optimización de los parámetros de control permite desarrollar una locomoción sin modelo predeterminado. Se desarrolla como parte de esta tesis un enfoque sobre algoritmos de Embodied Evolución, en otras palabras el uso de robots modulares físicos en la fase de evolución. La implementación material, la configuración experimental, y el Algoritmo Evolutivo implementado para Embodied Evolución, se explican detalladamente. El trabajo también incluye una visión general de las técnicas de aprendizaje por refuerzo y de los Procesos de Decisión de Markov. A continuación se presenta un algoritmo popular de aprendizaje por refuerzo, llamado Q-Learning, y su adaptación para aprender locomociones de robots modulares. Se proporcionan una implementación del algoritmo de aprendizaje y la evaluación experimental de la locomoción generada.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Antonio Barrientos Cruz.- Secretario: Luis Santiago Garrido Bullón.- Vocal: Giuseppe Carbon

Climbing and Walking Robots

With the advancement of technology, new exciting approaches enable us to render mobile robotic systems more versatile, robust and cost-efficient. Some researchers combine climbing and walking techniques with a modular approach, a reconfigurable approach, or a swarm approach to realize novel prototypes as flexible mobile robotic platforms featuring all necessary locomotion capabilities. The purpose of this book is to provide an overview of the latest wide-range achievements in climbing and walking robotic technology to researchers, scientists, and engineers throughout the world. Different aspects including control simulation, locomotion realization, methodology, and system integration are presented from the scientific and from the technical point of view. This book consists of two main parts, one dealing with walking robots, the second with climbing robots. The content is also grouped by theoretical research and applicative realization. Every chapter offers a considerable amount of interesting and useful information

Legged Robots for Object Manipulation: A Review

Legged robots can have a unique role in manipulating objects in dynamic, human-centric, or otherwise inaccessible environments. Although most legged robotics research to date typically focuses on traversing these challenging environments, many legged platform demonstrations have also included "moving an object" as a way of doing tangible work. Legged robots can be designed to manipulate a particular type of object (e.g., a cardboard box, a soccer ball, or a larger piece of furniture), by themselves or collaboratively. The objective of this review is to collect and learn from these examples, to both organize the work done so far in the community and highlight interesting open avenues for future work. This review categorizes existing works into four main manipulation methods: object interactions without grasping, manipulation with walking legs, dedicated non-locomotive arms, and legged teams. Each method has different design and autonomy features, which are illustrated by available examples in the literature. Based on a few simplifying assumptions, we further provide quantitative comparisons for the range of possible relative sizes of the manipulated object with respect to the robot. Taken together, these examples suggest new directions for research in legged robot manipulation, such as multifunctional limbs, terrain modeling, or learning-based control, to support a number of new deployments in challenging indoor/outdoor scenarios in warehouses/construction sites, preserved natural areas, and especially for home robotics.Comment: Preprint of the paper submitted to Frontiers in Mechanical Engineerin

Virtual articulation and kinematic abstraction in robotics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 279-292).This thesis presents the theory, implementation, novel applications, and experimental validation of a general-purpose framework for applying virtual modifications to an articulated robot, or virtual articulations. These can homogenize various aspects of a robot and its task environment into a single unified model which is both qualitatively high-level and quantitatively functional. This is the first framework designed specifically for the mixed real/virtual case. It supports arbitrary topology spatial kinematics, a broad catalog of joints, on-line structure changes, interactive kinostatic simulation, and novel kinematic abstractions, where complex subsystems are simplified with virtual replacements in both space and time. Decomposition algorithms, including a novel method of hierarchical subdivision, enable scaling to large closed-chain mechanisms with 100s of joints. Novel applications are presented in two areas of current interest: operating high- DoF kinematic manipulation and inspection tasks, and analyzing reliable kinostatic locomotion strategies based on compliance and proprioception. In both areas virtual articulations homogeneously model the robot and its task environment, and abstractions structure complex models. For high-DoF operations the operator attaches virtual joints as a novel interface metaphor to define task motion and to constrain coordinated motion (by virtually closing kinematic chains); virtual links can represent task frames or serve as intermediate connections for virtual joints. For compliant locomotion, virtual articulations model relevant compliances and uncertainties, and temporal abstractions model contact state evolution.(cont.) Results are presented for experiments with two separate robotic systems in each area. For high-DoF operations, NASA/JPL's 36 DoF ATHLETE performs previously challenging coordinated manipulation/inspection moves, and a novel large-scale (100s of joints) simulated modular robot is conveniently operated using spatial abstractions. For compliant locomotion, two experiments are analyzed that each achieve high reliability in uncertain tasks using only compliance and proprioception: a novel vertical structure climbing robot that is 99.8% reliable in over 1000 motions, and a mini-humanoid that steps up an uncertain height with 90% reliability in 80 trials. In both cases virtual articulation models capture the essence of compliant/proprioceptive strategies at a higher level than basic physics, and enable quantitative analyses of the limits of tolerable uncertainty that compare well to experiment.by Marsette Arthur Vona, III.Ph.D

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Towards Fault Diagnosis in Reconfigurable Robot

東京電機大学201

Planning Hybrid Driving-Stepping Locomotion for Ground Robots in Challenging Environments

Ground robots capable of navigating a wide range of terrains are needed in several domains such as disaster response or planetary exploration. Hybrid driving-stepping locomotion is promising since it combines the complementary strengths of the two locomotion modes. However, suitable platforms require complex kinematic capabilities which need to be considered in corresponding locomotion planning methods. High terrain complexities induce further challenges for the planning problem. We present a search-based hybrid driving-stepping locomotion planning approach for robots which possess a quadrupedal base with legs ending in steerable wheels allowing for omnidirectional driving and stepping. Driving is preferred on sufficiently flat terrain while stepping is considered in the vicinity of obstacles. Steps are handled in a hierarchical manner: while only the connection between suitable footholds is considered during planning, those steps in the resulting path are expanded to detailed motion sequences considering the robot stability. To enable precise locomotion in challenging terrain, the planner takes the individual robot footprint into account. The method is evaluated in simulation and in real-world applications with the robots Momaro and Centauro. The results indicate that the planner provides bounded sub-optimal paths in feasible time. However, the required fine resolution and high-dimensional robot representation result in too large state spaces for more complex scenarios exceeding computation time and memory constraints. To enable the planner to be applicable in those scenarios, the method is extended to incorporate three levels of representation. In the vicinity of the robot, the detailed representation is used to obtain reliable paths for the near future. With increasing distance from the robot, the resolution gets coarser and the degrees of freedom of the robot representation decrease. To compensate this loss of information, those representations are enriched with additional semantics increasing the scene understanding. We further present how the most abstract representation can be used to generate an informed heuristic. Evaluation shows that planning is accelerated by multiple orders of magnitude with comparable result quality. However, manually designing the additional representations and tuning the corresponding cost functions requires a high effort. Therefore, we present a method to support the generation of an abstract representation through a convolutional neural network (CNN). While a low-dimensional, coarse robot representation and corresponding action set can be easily defined, a CNN is trained on artificially generated data to represent the abstract cost function. Subsequently, the abstract representation can be used to generate a similar informed heuristic, as described above. The CNN evaluation on multiple data sets indicates that the learned cost function generalizes well to realworld scenes and that the abstraction quality outperforms the manually tuned approach. Applied to hybrid driving-stepping locomotion planning, the heuristic achieves similar performance while design and tuning efforts are minimized. Since a learning-based method turned out to be beneficial to support the search-based planner, we finally investigate if the whole planning problem can be solved by a learning-based approach. Value Iteration Networks (VINs) are known to show good generalizability and goal-directed behavior, while being limited to small state spaces. Inspired by the above-described results, we extend VINs to incorporate multiple levels of abstraction to represent larger planning problems with suitable state space sizes. Experiments in 2D grid worlds show that this extension enables VINs to solve significantly larger planning tasks. We further apply the method to omnidirectional driving of the Centauro robot in cluttered environments which indicates limitations but also emphasizes the future potential of learning-based planning methods.Planung von Hybrider Fahr-Lauf-Lokomotion für Bodenroboter in Anspruchsvollen Umgebungen Bodenroboter, welche eine Vielzahl von Untergründen überwinden können, werden in vielen Anwendungsgebieten benötigt. Beispielszenarien sind die Katastrophenhilfe oder Erkundungsmissionen auf fremden Planeten. In diesem Kontext ist hybride Fahr-/Lauf-Fortbewegung vielversprechend, da sie die sich ergänzenden Stärken der beiden Fortbewegungsarten miteinander vereint. Um dies zu realisieren benötigen entsprechende Roboter allerdings komplexe kinematische Fähigkeiten, welche auch in adäquaten Ansätzen für die Planung dieser Fortbewegung berücksichtigt werden müssen. Anspruchsvolle Umgebungen mit komplexen Untergründen erhöhen dabei zusätzlich die Anforderungen an die Bewegungsplanung. In dieser Arbeit wird ein suchbasierter Ansatz für kombinierte Fahr-/Lauf-Fortbewegungsplanung vorgestellt. Die adressierten Zielplattformen sind vierbeinige Roboter, deren Beine in lenkbaren Rädern enden, so dass sie omnidirektional fahren und laufen können. Auf ausreichend ebenem Untergrund wird generell Fahren bevorzugt, während der Planer Laufmanöver in der Nähe von Hindernissen in Erwägung zieht. Schritte werden dabei in einer hierarchischen Art undWeise realisiert: Während des Planens werden nur Verbindungen zwischen geeigneten Auftrittsflächen gesucht. Nur solche Schritte, die im Ergebnispfad enthalten sind, werden anschließend zu detaillierten Bewegungsabläufen verfeinert, welche die Balance des Roboters sicherstellen. Um präzise Fortbewegung in anspruchsvollen Umgebungen zu ermöglichen, betrachtet der Planer die spezifischen Aufstandsflächen der vier Füße. Der Ansatz wurde sowohl in simulierten als auch in realen Tests mit den Robotern Momaro und Centauro evaluiert, wobei der Planer in der Lage war, Lösungspfade von ausreichender Qualität in zulässiger Zeit zu generieren. Allerdings ergeben die benötigte feine Planungsauflösung und die hochdimensionale Roboterrepräsentation große Zustandsräumen. Diese würden für komplexere oder größere Planungsprobleme die zulässige Rechenzeit und den verfügbaren Speicher überschreiten. Damit der Planer auch eben diese komplexeren oder größeren Planungsprobleme handhaben kann, wird eine Erweiterung des Ansatzes beschrieben, welche mehrere Repräsentationslevel mit einbezieht. In unmittelbarer Umgebung des Roboters wird die zuvor beschriebene detaillierte Repräsentation genutzt, um hochwertige Pfade für die nahe Zukunft zu erzeugen. Mit zunehmendem Abstand vom Roboter wird die Auflösung gröber und die Anzahl der Freiheitsgrade in der Roboterrepräsentation sinkt. Um den mit dieser Vergröberung einhergehenden Informationsverlust zu kompensieren, werden diese Repräsentationen mit zusätzlicher Semantik ausgestattet, welche das Szenenverständnis erhöht. Darüber hinaus wird beschrieben, wie die Repräsentation mit dem höchsten Abstraktionsgrad zur Berechnung einer effektiven Heuristik genutzt werden kann. Die Evaluation in Simulationsumgebungen zeigt, dass der Planungsprozess um mehrere Größenordnungen beschleunigt werden kann, während die Ergebnisqualität vergleichbar bleibt. Allerdings sind das manuelle Gestalten der zusätzlichen Repräsentationen und das dazugehörige Parametrisieren der Kostenfunktionen sehr arbeitsintensiv. Um diesen Aufwand zu reduzieren, wird daher eine Methode beschrieben, welche die Gestaltung einer abstrakten Repräsentation durch ein Convolutional Neural Network (CNN) unterstützt. Während eine grobe, niedrigdimensionale Roboterrepräsentation und ein dazugehöriges Aktionsset einfach definiert werden können, wird ein CNN auf künstlich erzeugten Daten trainiert, um die abstrakte Kostenfunktion zu lernen. Anschließend kann die so erzeugte abstrakte Repräsentation genutzt werden, um die bereits zuvor erwähnte effektive Heuristik zu berechnen. In der Evaluation des CNNs auf verschiedenen Datensätzen zeigt sich, dass die gelernte Kostenfunktion auch mit Daten aus realen Umgebungen funktioniert und dass die generelle Ergebnisqualität oberhalb der Ergebnisse mit manuell erzeugten Repräsentationen liegt. Die Anwendnung der Methode zur Planung hybrider Fahr-/Lauf-Fortbewegung zeigt, dass die so erzeugte Heuristik gleichwertige Ergebnisse wie die Heuristik auf Basis manuell erzeugter Repräsentation liefert, während der Aufwand zur Gestaltung und Parametrisierung deutlich verringert wurde. Da sich gezeigt hat, dass eine lernbasierte Methode den klassischen suchbasierten Ansatz effektiv unterstützen kann, wird in dieser Arbeit abschließend untersucht, ob das gesamte Planungsproblem durch eine lernbasierte Methode gelöst werden kann. Value Iteration Networks (VINs) sind in diesem Zusammenhang ein vielversprechender Ansatz, da sie bekanntlich ein gutes zielorientiertes Planungsverhalten lernen und das Gelernte auf unbekannte Situationen verallgemeinern können. Allerdings ist ihre bisherige Anwendung auf kleine Zustandsräume begrenzt. Durch die zuvor beschriebenen Ergebnisse motiviert, wird eine Erweiterung von VINs beschrieben, so dass diese auf verschiedenen Abstraktionsleveln planen, um größere Planungsprobleme in Zustandsräumen entsprechender Größe darzustellen. Experimente in 2D-Rasterumgebungen zeigen, dass die beschriebene Methode VINs in die Lage versetzt, deutlich größere Planungsprobleme zu lösen. Darüber hinaus wird die beschriebene Methode benutzt, um omnidirektionale Fahrmanöver für den Centauro-Roboter in anspruchsvollen Umgebungen zu planen. Gleichzeitig werden hier aber auch die momentanen, hardware-bedingten Grenzen rein lernbasierter Ansätze sowie ihr zukünftiges Potential aufgezeigt