Automatically Designing Robot Controllers and Sensor Morphology with Genetic Programming

Abstract : Genetic programming provides an automated design strategy to evolve complex controllers based on evolution in nature. In this contribution we use genetic programming to automatically evolve efficient robot controllers for a corridor following task. Based on tests executed in a simulation environment we show that very robust and efficient controllers can be obtained. Also, we stress that it is important to provide sufficiently diverse fitness cases, offering a sound basis for learning more complex behaviour. The evolved controller is successfully applied to real environments as well. Finally, controller and sensor morphology are co-evolved, clearly resulting in an improved sensor configuration.
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Harris Papadopoulos; Andreas S. Andreou; Max Bramer. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. Springer, IFIP Advances in Information and Communication Technology, AICT-339, pp.86-93, 2010, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-16239-8_14〉
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Bert Bonte, Bart Wyns. Automatically Designing Robot Controllers and Sensor Morphology with Genetic Programming. Harris Papadopoulos; Andreas S. Andreou; Max Bramer. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. Springer, IFIP Advances in Information and Communication Technology, AICT-339, pp.86-93, 2010, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-16239-8_14〉. 〈hal-01060655〉

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