In recent years, driver-assistance systems have emerged as one major possibility to increase comfort and safety in road traffic. Still, cost is one major hindrance to the widespread use of safety systems such as lane-change or blind spot warning. To facilitate the widespread adoption of such safety systems, thus increasing safety for all traffic participants, the use of cost-efficient components is of crucial importance. Within this work we investigate the use of cost-efficient, widely used ultrasonic sensors for the tracking of passing-by vehicles at high velocities. Therefore, a particle filter with some mixture tracking capabilities is implemented to fuse the signals from 6 us-sensors. The main focus of this work lies on the development of a more detailed sensor model that is used in this particle filter. Additionally, a strategy to take into account object-visibility w.r.t. the different sensors is outlined. The derived concept is evaluated experimentally in real road traffic. The applicability of the tracking result in context of lane-change-decision-aid and blind-spot-surveillance systems is analyzed.

Non-holonomic, omnidirectional undercarriages that are composed of steered standard wheels seem to provide a solid compromise between versatility, flexibility and high robustness against various ground conditions. However, such undercarriages are characterized by the occurrence of a number of singular configurations. To avoid these singular configurations most control-approaches restrict the admissible configuration-space thus eventually reducing the mobility and flexibility of the undercarriage. Within this work a state-space representation that forms a locally singularity-free atlas of the admissible configuration-space is presented. Based on this state-space description a switching based controller is developed that incorporates the former singular regions into the used configuration space and thus allows to exploit the full flexibility of non-holonomic, omnidirectional undercarriages. The implemented controller is quantitatively and qualitatively evaluated and compared to one approach that avoids the singular regions and one that completely neglects the non-holonomic bindings.

Currently, pseudo-omnidirectional, wheeled mobile robots with independently steered and driven wheels seem to provide a solid compromise between complexity, flexibility and robustness. Yet, such undercarriages are imposed to the risk of actuator fighting and suffer from singular regions within their configuration space. To address these problems we expand a previously developed potential field (PF) based approach by expanding it with a predictive horizon. The proposed method is based on a model predictive control (MPC) approach, incorporating a gradient descent optimization step via the Pontryagin minimum principle. To enforce adherence to the constraints during optimization, we modify the Lagrange-multipliers within the backpropagation of the costates. The proposed approach is evaluated simulatively w.r.t. the undercarriage of the Care-O-bot®3 mobile robot and is compared to the potential field based and a model predictive control approach.

For mobile platforms with steerable standard wheels it is necessary to precisely coordinate rotation and steering angle of their wheels. An established approach to ensure this is to represent the current state of motion in form of the Instantaneous Centre of Motion (ICM) and to derive a trajectory within this space. However, while control in the ICM space does guarantee adherence to the system's nonholonomic constraints, it does not avoid the system's singular configurations. Within this work we address the problem of singularity avoidance within the ICM space. Singularities related to the mathematical representation of the ICM are reduced by a reformulation of this representation. Furthermore, a controller based on artificial potential fields avoiding singular configurations of the robot by representing them as obstacles in the derived ICM space is designed. The resulting controller is particularized and analyzed w.r.t. the Care-O-bot 3 demonstrator.

This paper introduces Care-O-bot®3, a highly integrated and compact service robot with manipulation, navigation and vision capabilities. In particular, Care-O-bot®3 combines the best of available technology including a 7 DOF light-weight arm, an omnidirectional platform and many high-end sensors along with a sustainable, end user oriented design concept enabling many interaction possibilities.

Robots are emerging from industrial plants toward every people's daily life. Thus, navigation in and understanding of human related environments becomes a prerequisite for the systems of tomorrow. Most such environments can be efficiently described using line segments. However, incorporation of extent information is often difficult, as line segments are seldom observed completely and erroneous data-association may corrupt the information associated to a certain segment. To reduce such problems this paper proposes a statistically driven description of line segments. The corresponding parameters are decomposed into line and corner features, which are separately tracked through an extended Kalman filter (EKF). Information about the extent of the lines is encoded statistically. Therefore, we use a method to recursively incorporate the information gained through a time-series of measurements. Thus, the covariance matrix belonging to a line segment grows as new regions of the corresponding line are discovered. Experimental results obtained by implementation on the mobile platform ITrike show the validity of our algorithm.