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Künstliche Intelligenz 25(2) - Mai 2011

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  • Zeitschriftenartikel
    Autonomous Off-Road Navigation for MuCAR-3
    (KI - Künstliche Intelligenz: Vol. 25, No. 2, 2011) Himmelsbach, Michael; Luettel, Thorsten; Hecker, Falk; Hundelshausen, Felix; Wuensche, Hans-Joachim
    This report gives an overview of the autonomous navigation approach developed for the ground robot MuCAR-3, partly as a satellite project in the CoTeSys cluster of excellence. Safe robot navigation in general demands that the navigation approach can also cope with situations where GPS data is noisy or even absent and hence great care must be taken when using global map information. Choosing a safe action should be tightly coupled to the perception of the immediate surrounding in such situations. The tentacles approach developed earlier in the project efficiently deals with these issues by introducing integral structures for sensing and motion. This report presents the extensions and improvements made to the tentacles approach during the progress of the project and the results obtained at various challenging robot competitions.
  • Zeitschriftenartikel
    Behaviour-Based Off-Road Robot Navigation
    (KI - Künstliche Intelligenz: Vol. 25, No. 2, 2011) Armbrust, Christopher; Proetzsch, Martin; Berns, Karsten
    This paper describes concepts for the control of the autonomous off-road vehicle ravon. The complexity of the target environment comprising rough terrain as well as vegetation requires capabilities reaching from low-level safety aspects to high-level planning. It is shown how the modular implementation using the behaviour-based architecture iB2C allows for the realisation of complex behaviour networks based on a concept for the uniform representation of sensor data. The effectiveness of the presented approach is briefly shown in a real-world experiment.
  • Zeitschriftenartikel
    Cost-Efficient Global Robot Navigation in Rugged Off-Road Terrain
    (KI - Künstliche Intelligenz: Vol. 25, No. 2, 2011) Braun, T.
    The thesis “Cost-Efficient Global Robot Navigation in Rugged Off-Road Terrain” proposes a global robot navigation concept for rugged off-road terrain which is robust against inaccurate self-localization and scalable to large environments, but also cost-conscious, e.g. able to generate navigation paths which optimize a cost measure closely related to terrain traversability. The thesis contributes a set of new techniques to integrate cost-consciousness into a primarily topological navigation scheme. Also, novel methods to learn and optimize cost estimates from experience are developed.
  • Zeitschriftenartikel
    Intelligent Mobility
    (KI - Künstliche Intelligenz: Vol. 25, No. 2, 2011) Joyeux, Sylvain; Schwendner, Jakob; Kirchner, Frank; Babu, Ajish; Grimminger, Felix; Machowinski, Janosch; Paranhos, Patrick; Gaudig, Christopher
    Robotic systems for outdoor applications can play an important role in the future. Tasks like exploration, surveillance or search and rescue missions benefit greatly from increased autonomy of the available systems. Outdoor environments and their high complexity pose a special challenge for existing autonomous behaviour technologies in robots. Some of these challenges in the area of navigation, plan management and sensor integration are investigated in the Intelligent Mobility (iMoby) project at the DFKI. An introduction to the project goals and the current achievements is given. Further, an outlook towards the end of the project and beyond is provided.
  • Zeitschriftenartikel
    (KI - Künstliche Intelligenz: Vol. 25, No. 2, 2011)
  • Zeitschriftenartikel
    Sensor-Fusion Based Real-Time 3D Outdoor Scene Reconstruction and Analysis on a Moving Mobile Outdoor Robot
    (KI - Künstliche Intelligenz: Vol. 25, No. 2, 2011) Kuhnert, Lars; Kuhnert, Klaus-Dieter
    This article presents an overview over the whole process of generating a precise and rich 3D representation of the local environment of a moving mobile outdoor robot. The resulting model of this process is a camera image textured triangle mesh which is triangulated from a motion-corrected laser scanned 3D point cloud. The demanding requirements of autonomous off-road environment model acquisition are handled by applying a multi-sensor fusion approach. Additionally to the model creation process a novel way of detecting and describing feature points on a piece-wise linear 3D surfaces is presented. The set of feature points generated by the proposed method is a valuable abstraction of a whole outdoor scene that can be used in several following processing steps like mesh simplification or segmentation and robotics-specific tasks like obstacle detection or classification. All described methods are implemented and successfully used on the award-winning robot AMOR.
  • Zeitschriftenartikel
    Cognitive Navigation
    (KI - Künstliche Intelligenz: Vol. 25, No. 2, 2011) Hundelshausen, Felix; Luettel, Thorsten; Wuensche, Hans-Joachim
    In this paper we propose a navigation framework for flexible off-road navigation that allows to use different navigation paradigms depending on the given situation. We first classify existing navigation approaches into, global navigation, reactive navigation and guided navigation and then show how a unified view leads to a very flexible navigation architecture that we call affordance hierarchy.
  • Zeitschriftenartikel
    Themenheft Offroad-Robotik
    (KI - Künstliche Intelligenz: Vol. 25, No. 2, 2011) Berns, Karsten; Armbrust, Christopher
  • Zeitschriftenartikel
    Offroad Navigation Using Adaptable Motion Patterns
    (KI - Künstliche Intelligenz: Vol. 25, No. 2, 2011) Hoeller, Frank; Röhling, Timo; Schulz, Dirk
    We present a navigation system which is able to steer an electronically controlled ground vehicle to given destinations considering all obstacles in its vicinity. The approach is designed for vehicles without a velocity controlled drive-train, making it especially useful for typical remote-controlled vehicles. The vehicle is controlled by sets of commands, each set representing a specific maneuver. These sets are combined in a tree-building procedure to form trajectories towards the given destination. While the sets of commands are executed the vehicle’s behavior is measured to refine the prediction used for path generation. This enables the approach to adapt to surface alterations. We tested our system using a 400 kg EOD robot in an outdoor environment.
  • Zeitschriftenartikel
    Towards Pathplanning for Unmanned Ground Vehicles (UGV) in 3D Plane-Maps of Unstructured Environments
    (KI - Künstliche Intelligenz: Vol. 25, No. 2, 2011) Vaskevicius, Narunas; Birk, Andreas
    Work in progress on efficient long range path-planning for unmanned ground vehicles (UGV) is presented. It builds upon own work on 3D mapping in unstructured environments, which uses large planar patches for representation and registration of range data. The planar patches allow a very fast assessment of drivability as indicated by experiments with several data-sets.