Three different neuromorphic vision sensors; a 2D smooth optical flow sensor, a 1D tracker sensor and a 1D motion sensor are modeled in a simulator. The sensors are modeled according to their spatio-temporal properties and the model is validated with experimental data obtained in previous work. The model parameters are fitted according to validation data, where a cost function is optimized with a gradient descent approach. The simulator used is the Open Dynamics Engine (ODE) and the experimental data from previous work was collected in a typical RoboCup scenario. A soccer playing robot is here used to provide reference information from a standard digital tracker system. By modeling the output from neuromorphic vision sensors, it is possible to reconstruct the experiment in the simulator. We will present simulated data from this particular experiment and compare it to the ground truth validation data. This work is important in order to reduce the time needed for real-world experiments for the set-up and optimization phase during the integration of such sensors into robotic systems. At the end we will also make an indication of the direction of future research.

The use of three types of spatio-temporal processing elements is investigated for optical sensory preprocessing in order to solve robot control problems in mobile robotics. The sensory elements are optical analog VLSI silicon retina type devices that do on-chip gradient operations and perform a current mode hysteretic winner-take-all function. Each sensor device extracts a characteristic feature from the optical input: position of highest contrast along a 1-D array, maximum speed along a 1-D array, maximum optical flow on a 2-D array. These are continuously calculated by the respective sensory devices. The sensory devices are applied in a mobile robotics application. They are used for active ball control, ball velocity prediction and active gaze-control for RoboCup Middle-Size League robots.

We present a robotic software development framework for rapid simulation and programming of mobile robot teams. The framework uses a specification-centred generative approach. A robot control program is specified in an intuitive graphical representation of a hyper-graph of typed data processing elements. This specification is automatically refined to all design artifacts required in a robotic software development environment: simulation models, robot control programs, team communication infrastructure, documentation, and real-time monitoring. Although our framework was originally intended to design behavior-based robot control programs using the dual dynamics architecture, we demonstrate the flexibility of the environment by an example which integrates a "classical" control schema into a dual dynamics behavior system. Experimental validation was done on GMD-Robots, our team of mid-size league RoboCup robots.

In this paper we present a new data model for tool integration that extends existing data models by an abstract graph model.We provide a framework service based on this model which generates implementations of graph-based design representations from a conceptual schema.In two case studies we use them for rapid construction of a high-level synthesis tool and for easy fine-grained integration to an existing simulator. Tool Integration and Construction Using Generated Graph-Based Design RepresentationsAnsgar Bredenfeld