Arrow Research search

Author name cluster

Arno Bücken

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
2 author rows

Possible papers

2

IROS Conference 2003 Conference Paper

Enhancing a robot-centric virtual reality system towards the simulation of fire

  • Eckhard Freund
  • Jürgen Roßmann
  • Arno Bücken

Today's robot applications often include multi-robot multi-user environments which are planed thoroughly in 3D before installation. The key to gain the maximum benefit out of the already available models it is to reuse them for other purposes than only robot-simulation. One of these aspects surely is the training of future operators of the cell which is important especially for environments that are difficult to access for training. For example a production line in the automotive industry may not be shut down for a day of emergency-training. In this paper we will show how to reuse already modelled robot cells and mechanisms of the Cosimir simulation-system in emergency-training. We present an approach for graphical visualisation and simulation of fire. We show how to get realistic impressions of fire using advanced particle-simulation and how to use the advantages of particles to trigger states in a modified cellular automata used for the simulation of fire-behaviour. By this we can have a simulated fire developing and reacting on other fires and different substances as water, CO/sub 2/ or oxygen. It turns out that the mechanisms implemented for fire-simulation are also valuable for robot-simulation. So there is a mutual benefit between the robot-simulation and the simulation of fire. The methods proposed in this paper have been implemented and successfully tested on Cosimir, a commercial robot-and VR-simulation-system.

AAAI Conference 1996 Short Paper

Learning Topological Maps: An Alternative Approach

  • Arno Bücken

Our goal is autonomous real-time control of a mobile robot. In this paper we want to show a possibility to learn topological maps of a large-scale indoor environment autonomously. In the literature there are two paradigms how to store information on the environment of a robot: as a grid-based (geometric) or as a topological map. While grid-based maps are considerably easy to learn and maintain, topological maps are quite compact and facilitate fast motion-planning.