ECAL 1999: Advances in Artificial Life pp 329-333 | Cite as
A Visualization Tool for the Mini-Robot Khepera: Behavior Analysis and Optimization
Abstract
The design of behavior generating control structures for real robots acting autonomously in a real and changing environment is a complex task. This is in particular true with respect to the debugging process, the documentation of the encountered behavior, its quantitative analysis and the final evaluation. To successfully implement such a behavior, it is vital to couple the synthesis on a simulator and the experiment on a real robot with a thorough analysis. The available simulator tools in general only allow behavioral snapshots and do not provide the option of online interference. In order to cure these shortcomings, a visualization tool for aposteriori graphical analysis of recorded data sets which gives access to all relevant internal states and parameters of the system is presented. The mini-robot Khepera has been chosen as experimentatory platform.
Keywords
Mobile Robot Visualization Tool Angular Error Real Robot Evolutionary RoboticPreview
Unable to display preview. Download preview PDF.
References
- 1.V. Braitenberg. Vehicles: Experiments in Synthetic Psychology. MIT Press/Bradford Books, 1984.Google Scholar
- 2.R. A. Brooks. Intelligence without Representation. Artificial Intelligence, 47:139–159, 1987.CrossRefGoogle Scholar
- 3.Laboratoire de Microinformatique l’Ecole Polytechnique Fédérale de Lausanne. Mobile Robots as Research Tools: The K-Robot Family. http://diwww.epfl.ch/Khepera/, 1998.
- 4.D. Floreano and F. Mondada. Evolutionary neurocontrollers for autonomous mobile robots. Neural Networks, 11:1461–1478, 1998.CrossRefGoogle Scholar
- 5.N. Jakobi. Evolutionary Robotics and the Radical Envelope of Noise Hypothesis. Journal Of Adaptive Behaviour, 6, 1997.Google Scholar
- 6.N. Jakobi. The Minimal Simulation Approach To Evolutionary Robotics. In Proc. of ER’98, AI Systems Books, 1998.Google Scholar
- 7.D. Lambrinos. Navigating with an Adaptive Light Compass. In Proc. of 3rd Eur. Conf. on Art. Life, volume 929 of Lecture Notes in Artificial Intelligence, pages 602–613. Springer, 1995.Google Scholar
- 8.A. Löffler, J. Klahold, and U. Rückert. The Dynamical Nightwatch’s Problem Solved by the Autonomous Micro-Robot Khepera. In Proc. of 3rd Eur. Conf. on Art. Evolution, volume 1363 of Lecture Notes in Computer Science, pages 303–313. Springer, 1998.Google Scholar
- 9.O. Michel. Khepera Simulator 2.0, Webots, 1999. http://diwww.epfl.ch/lami/team/michel/khep-sim/ and http://www.cyberbotics.com/.
- 10.O. Miglino, H. H. Lund, and S. Nolfi. Evolving mobile robots in simulated and real environments. In Proc. of 3rd Eur. Conf. on Art. Life, volume 929 of Lecture Notes in Artificial Intelligence, pages 417–434. Springer, 1995.Google Scholar
- 11.A. G. Pipe, B. Carse, T. C. Fogarty, and A. Winfied. Learning Subjective “Cognitive Maps” in the Presence of Sensory-Motor Errors. In Proc. of 3rd Eur. Conf. on Art. Life, volume 929 of Lecture Notes in Artificial Intelligence, pages 463–476. Springer, 1995.Google Scholar
- 12.E. Prem. Grounding and the Entailment Structure in Robots and Artificial Life. In Proc. of 3rd Eur. Conf. on Art. Life, volume 929 of Lecture Notes in Artificial Inteligence, pages 39–51. Springer, 1995.Google Scholar
- 13.C. Scheier and R. Pfeifer. Classification as Sensory-Motor Coordination, A Case Study on Autonomous Agents. In Proc. of 3rd Eur. Conf. on Art. Life, volume 929 of Lecture Notes in Artificial Intelligence, pages 657–667. Springer, 1995.Google Scholar