Abstract
Using decentralized control structures for robot control can offer a lot of advantages, such as less complexity, better fault tolerance and more flexibility. In this paper the evolution of recurrent artificial neural networks as centralized and decentralized control architectures will be demonstrated. Both designs will be analyzed concerning their structure-function relations and robustness against lesion experiments. As an application, a gravitationally driven robotic system will be introduced. Its task can be allocated to a cooperative behavior of five subsystems. A co-evolutionary strategy for generating five autonomous agents in parallel will be described.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Baldassarre, G., Parisi, D., Nolfi, S.: Coordination and behavior integration in cooperating simulated robots. In: Proc. of the 8th International Conference on the Simulation of Adaptive Behavior (SAB 2004), pp. 385–394 (2004)
Beer, R.D.: The dynamics of active categorical perception in an evolved model agent. Adaptive Behavior 11(4), 209–243 (2003)
Cao, Y.U., Fukunaga, A.S., Kahng, A.B.: Cooperative mobile robotics: Antecedents and directions. Autonomous Robots 4, 7–27 (1997)
Clark, A.: Being There: Putting Brain, Body, and World Together Again. MIT Press, Cambridge (1996)
Harvey, I., Di Paolo, E.A., Wood, R., Quinn, M., Tuci, E.: Evolutionary robotics: a new scientific tool for studying cognition. Artificial Life 11, 79–98 (2005)
Hülse, M., Wischmann, S., Pasemann, F.: Structure and function of evolved neuro-controllers for autonomous robots. Connection Science 16(4), 249–266 (2004)
Ijspeert, A.J., Martinoli, A., Billard, A., Gambardella, L.M.: Collaboration through the exploitation of local interactions in autonomous collective robotics: The stick pulling experiment. Autonomous Robots 11(2), 149–171 (2001)
Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press, Cambridge (2000)
Pasemann, F., Steinmetz, U., Hülse, M., Lara, B.: Robot control and the evolution of modular neurodynamics. Theory in Biosciences 120, 311–326 (2001)
Pfeifer, R.: On the role of embodiment in the emergence of cognition: Grey walter’s turtles and beyond. In: Proc. of the workshop The legacy of Grey Walter, Bristol (2002)
Pfeifer, R., Scheier, C.: Understanding Intelligence. MIT Press, Cambridge (1999)
Quinn, M., Smith, L., Mayley, G., Husbands, P.: Evolving controllers for a homogeneous system of physical robots: Structured cooperation with minimal sensors. Philosophical Transactions of the Royal Society of London, Series A: Mathematical, Physical and Engineering Sciences 361, 2321–2344 (2003)
Resnick, M.: Turtles, termites, and traffic jams: explorations in massively parallel microworlds. MIT Press, Cambridge (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wischmann, S., Hülse, M., Pasemann, F. (2005). (Co)Evolution of (De)Centralized Neural Control for a Gravitationally Driven Machine. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds) Advances in Artificial Life. ECAL 2005. Lecture Notes in Computer Science(), vol 3630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553090_19
Download citation
DOI: https://doi.org/10.1007/11553090_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28848-0
Online ISBN: 978-3-540-31816-3
eBook Packages: Computer ScienceComputer Science (R0)