Abstract
Loosely inspired by natural evolution, evolutionary robotics combines evolutionary computation and agent-based modelling to provide a set of tools for the automated design of robots. Evolutionary robotics have been used to address various challenging engineering problems in robotics, such as co-evolving robot morphology and control architecture, or learning coordinated behaviours for swarm of robots in open environments.
Evolutionary robotics makes it possible for the researcher and practitioner to address problems for which finding near-optimal solutions is already a difficult challenge. Such problems are often characterized by poorly-defined task objectives as well as involving unconventional search spaces, and usually involve non-linear dynamics and complex interaction patterns between the parts involved.
This chapter describes the challenges and issues in evolutionary robotics, and provides a glimpse at the mechanisms at work behind the algorithms. In addition, a particular emphasis is put on the ability for these algorithms to balance between the search for pure performance and the discovery of novel, and possibly unexpected, solutions.
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Bredeche, N. (2015). Artificial Evolution of Autonomous Robots and Virtual Creatures. In: Heams, T., Huneman, P., Lecointre, G., Silberstein, M. (eds) Handbook of Evolutionary Thinking in the Sciences. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9014-7_29
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