Abstract
A humanoid walking gait synthesizing approach, which is able to generate gaits in both sagittal and frontal planes, is presented in this paper. To further improve the humanoid walking gait in consideration of both ZMP (Zero Moment Point) and energy consumption constraints, a two-stage optimization method is proposed. At the first stage, real-coded GAs (genetic algorithms) are used to generate a set of near-optimal walking gaits. At the second stage, the near-optimal walking gaits are used as training samples for a GA-based NN (neural network) to further improve the humanoid walking gait. By making use of the global optimization capability of GAs, the GA-based NN can solve the local minima problem. The proposed approach is able to generate near-optimal walking gait at any speed in feasible range. Experiments are conducted to verify the effectiveness of the proposed method.
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Tang, Z., Zhou, C., Sun, Z. (2005). Humanoid Walking Gait Optimization Using GA-Based Neural Network. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_37
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DOI: https://doi.org/10.1007/11539117_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
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