Future requirements for drastic reduction of CO2production and energy consumption will lead to significant changes in the way we see mobility in the years to come. However, the automotive industry has identified significant barriers to the adoption of electric vehicles, including reduced driving range and greatly increased refueling times. Automated cars have the potential to reduce the environmental impact of driving, and increase the safety of motor vehicle travel. The current state-of-the-art in vehicle automation requires a suite of expensive sensors. While the cost of these sensors is decreasing, integrating them into electric cars will increase the price and represent another barrier to adoption. The V-Charge Project, funded by the European Commission, seeks to address these problems simultaneously by developing an electric automated car, outfitted with close-to-market sensors, which is able to automate valet parking and recharging for integration into a future transportation system. The final goal is the demonstration of a fully operational system including automated navigation and parking. This paper presents an overview of the V-Charge system, from the platform setup to the mapping, perception, and planning sub-systems.

This paper presents an algorithm for the global localization of mobile robots. The general idea is to build a local map incrementally which is matched to a global map in each localization step. The matching algorithm is a very time and memory efficient enhancement of the common Random Sample Consensus (RANSAC). It is executed a fixed number of iterations computing a set of hypotheses of the current robot pose. This paper describes how to handle the resulting hypotheses, i.e. a method is introduced deciding when the robot is localized reliably enough. The algorithm has been implemented and its characteristics are evaluated and discussed in an experimental section.

The capability of solving the simultaneous localization and mapping (SLAM) problem is one of the fundamental tasks of mobile robots and many research has focused on this problem over the last decades. In this paper, the SLAM problem is considered as the problem of finding an optimal path through a tree resulting in minimum costs. For this purpose, we apply the Ant Colony Optimization meta-heuristic, which belongs to the class of ant algorithms. It has been successfully employed to solve the well known Traveling Salesman Problem with several thousands of cities. We use a simple scan matching technique for generating a rough pre-solution to the SLAM problem. The (inconsistent) map is partitioned into fragments. A new fragment is initialized as soon as the robot has moved several meters. We draw samples from Gaussian distributions representing alignments of consecutive fragments. The resulting set of samples is interpreted as a tree-like data structure with weights assigned to the edges. We use our own variant of an ant algorithm for finding the optimal path through the tree. Real-world experimental results demonstrate the characteristics of our method.

This paper introduces a parallelized variant of the Random Sample Matching (RANSAM) approach, which is a very time and memory efficient enhancement of the common Random Sample Consensus (RANSAC). RANSAM exploits the theory of the birthday attack whose mathematical background is known from cryptography. The RANSAM technique can be applied to various fields of application such as mobile robotics, computer vision, and medical robotics. Since standard computers feature multi-core processors nowadays, a considerable speedup can be obtained by distributing selected subtasks of RANSAM among the available cores. First of all this paper addresses the parallelization of the RANSAM approach. Several important characteristics are derived from a probabilistic point of view. Moreover, we apply a fuzzy criterion to compute the matching quality, which is an important step towards real-time capability. The algorithm has been implemented for Windows and for the QNX RTOS. In an experimental section the performance of both implementations is compared and our theoretical results are validated.

This paper describes a new solution of the simultaneous localization and mapping (SLAM) problem. Instead of building one global consistent map, aimed by the most common SLAM techniques, we compute a set of local metrical maps and fuse them to a graph-like structure resulting in a topological map. Thus, our approach does not require a global metrical map consistency. The main contribution of this paper is an algorithm for closing spatial loops. Loop closing means, that a subset of the edges of the graph representing the topological map forms a cycle. To this end we describe a very efficient enhancement of the well-known RANSAC technique for actively recognizing regions explored by the robot previously. This improvement exploits the theory of the birthday attack whose mathematical background is known from cryptography. A fast sample-based scan matcher is employed to compute the local maps. We derive the covariance of the current robot pose from the sample distribution in order to perform a recognition only when loop closing is very likely. Our approach has been implemented and experimental results show its excellent performance.

In this paper, we present a new method for sensor fusion in robot assembly. In our approach, model information can be derived automatically from CAD-data. We introduce force torque maps, which are either computed automatically exploiting modern graphical processors or are measured by scanning forces and torques during contact motions. Subsequently, force torque maps are applied as model information during execution of real assembly tasks. Also, computer vision is included by comparing relative poses of features in virtual images with their real relative poses given from measured images. For fusion of these two (or more) different sensors we suggest to use particle filters. Experiments with variations of peg in hole tasks in a real work cell demonstrate our new approach to be very useful for the whole process chain from planning to execution.