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Evolving neural network controllers to produce leg cycles for gait generation

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2 Author(s)
G. B. Parker ; Comput. Sci., Connecticut Coll., New London, CT, USA ; Zhiyi Li

The generation of gaits for hexapod locomotion controllers can be divided into two main parts: the cyclic action of a single leg (leg cycles) and the coordination of all legs to combine individual leg cycles to produce forward movement. In this paper, we use a genetic algorithm (GA) to evolve the structure of an artificial neural network (NN) that produces leg cycles in a hexapod robot. The movement of the robot's leg is controlled by a horizontal servo and vertical servo. The servos are controlled by a NN that generates a cycle of pulses. With minimal restrictions on the structure of the NN a GA is used to find the parameters of neurons and the connections between them. The pulse sequences generated by the evolved NNs resulted in leg cycles that produced efficient forward movement.

Published in:

Automation Congress, 2002 Proceedings of the 5th Biannual World  (Volume:14 )

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