No abstract

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

For mobile platforms with steerable standard wheels it is necessary to precisely coordinate rotation and steering angle of their wheels. Especially for redundantly actuated platforms the misalignments of a single wheel directly leads to invalid configurations which may cause degraded motion of the platform and high internal forces. An established approach to deal with this problem is to represent the current state of motion in form of the Instantaneous Centre of Motion (ICM) and to derive a valid trajectory for this point. However, this representation bears severe numerical drawbacks. To remedy those numerical problems an alternative ICM representation based on spherical coordinates is proposed in this work. Furthermore, the relations between ICM and generalized robot velocities are addressed. It is shown, that one receives a basis of a subspace within the kinematical constraints' nullspace by decomposing the generalized velocity vector in spherical coordinates. Finally the proposed ICM-based control is particularized and simulative analyzed w.r.t. the Care-O-bot 3 demonstrator

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 configurationspace 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