Cost-Efficient Global Robot Navigation in Rugged Off-Road Terrain

This PhD thesis aims at finding a global robot navigation strategy for rugged off-road terrain which is robust against inaccurate self-localization, scalable to large environments, but also cost-efficient, e.g. able to generate navigation paths which optimize a cost measure closely related to terrain traversability. In order to meet this goal, aspects of both metrical and topological navigation techniques are combined. A primarily topological map is extended with the previously lacking capability of cost-efficient path planning and map extension. Further innovations include a multi-dimensional cost measure for topological edges, a method to learn these costs based on live feedback from the robot and a set of extrapolation methods to predict the traversability costs for untraversed edges. The thesis presents two sophisticated new image analysis techniques to optimize cost prediction based on the shape and appearance of surrounding terrain. Experimental results indicate that the proposed global navigation system is indeed able to perform cost-efficient, large scale path planning. At the same time, the need to maintain a fine-grained, global world model which would reduce the scalability of the approach is avoided.Kosteneffiziente globale Roboternavigation in rauhem, unstrukturiertem Geländ

Cost-Efficient Global Robot Navigation in Rugged Off-Road Terrain

This PhD thesis aims at finding a global robot navigation strategy for rugged off-road terrain which is robust against inaccurate self-localization, scalable to large environments, but also cost-efficient, e.g. able to generate navigation paths which optimize a cost measure closely related to terrain traversability. In order to meet this goal, aspects of both metrical and topological navigation techniques are combined. A primarily topological map is extended with the previously lacking capability of cost-efficient path planning and map extension. Further innovations include a multi-dimensional cost measure for topological edges, a method to learn these costs based on live feedback from the robot and a set of extrapolation methods to predict the traversability costs for untraversed edges. The thesis presents two sophisticated new image analysis techniques to optimize cost prediction based on the shape and appearance of surrounding terrain. Experimental results indicate that the proposed global navigation system is indeed able to perform cost-efficient, large scale path planning. At the same time, the need to maintain a fine-grained, global world model which would reduce the scalability of the approach is avoided.Kosteneffiziente globale Roboternavigation in rauhem, unstrukturiertem Geländ