This paper deals with the problem of energy constrained gait optimization for bipedal walking. We present a solution to this problem obtained by applying a recently introduced heuristic method, the Alliance Algorithm (AA), and compare its performance against a Genetic Algorithm (GA). We show experimentally that the intrinsic ability of the AA to handle hard constraints enables it to find solutions significantly better than the GA. Also with the constraint removed the AA show more reliable optimization results. Finally, we show that the final gait obtained through this method outperforms most solutions to this problem presented in previous works, in terms of walking speed.
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