Implicit and Robust Evaluation Methodology for the Evolutionary Design of Feasible Robots
This paper deals with the evolutionary design of feasible and manufacturable robots. Specifically, here we address the problem of defining a methodology for the evaluation of candidate robots that guides the evolution of morphology and control towards a valid design when transferred to reality. We aim to minimize the explicit knowledge introduced by the designer in the fitness function. As a consequence of this higher flexibility, we must include elements to ensure that the obtained robots are feasible. To do it, we propose an extension of the principles proposed by classical authors from traditional evolutionary robotics to brain-body evolution. In this paper we describe this methodology and show its application in a benchmark example of evolutionary robot design. To this end, previously presented elements like the structural definition of the robotic units, the encoding of the morphology and control and the specific evolutionary algorithm applied are also briefly described.
Unable to display preview. Download preview PDF.
- 1.Sims, K.: Evolving virtual creatures. In: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, pp. 15–22. ACM (1994)Google Scholar
- 4.Marbach, D., Ijspeert, A.: Online optimization of modular robot locomotion. In: IEEE International Conference on Mechatronics and Automation, vol. 1, pp. 248–253. IEEE (2005)Google Scholar
- 5.Rommerman, M., Kuhn, D., Kirchner, F.: Robot design for space missions using evolutionary computation. In: IEEE Congress on Evolutionary Computation, pp. 2098–2105 (2009)Google Scholar
- 8.Faiña, A., Orjales, F., Bellas, F., Duro, R.J.: First steps towards a heterogeneous modular robotic architecture for intelligent industrial operation. In: Workshop on Reconfigurable Modular Robotics, IROS, San Francisco (2011)Google Scholar
- 10.Koenig, N., Howard, A.: Design and use paradigms for gazebo, an open-source multi-robot simulator. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 2149–2154 (2004)Google Scholar