Energy-Efficient Hip Joint Offsets in Humanoid Robot via Taguchi Method and Bio-inspired Analysis

Authors
Kim, JihunYang, JaehaYang, Seung TaeOh, YonghwanLee, Giuk
Issue Date
2020-10
Publisher
MDPI
Citation
APPLIED SCIENCES-BASEL, v.10, no.20
Abstract
Although previous research has improved the energy efficiency of humanoid robots to increase mobility, no study has considered the offset between hip joints to this end. Here, we optimized the offsets of hip joints in humanoid robots via the Taguchi method to maximize energy efficiency. During optimization, the offsets between hip joints were selected as control factors, and the sum of the root-mean-square power consumption from three actuated hip joints was set as the objective function. We analyzed the power consumption of a humanoid robot model implemented in physics simulation software. As the Taguchi method was originally devised for robust optimization, we selected turning, forward, backward, and sideways walking motions as noise factors. Through two optimization stages, we obtained near-optimal results for the humanoid hip joint offsets. We validated the results by comparing the root-mean-square (RMS) power consumption of the original and optimized humanoid models, finding that the RMS power consumption was reduced by more than 25% in the target motions. We explored the reason for the reduction of power consumption through bio-inspired analysis from human gait mechanics. As the distance between the left and right hip joints in the frontal plane became narrower, the amplitude of the sway motion of the upper body was reduced. We found that the reduced sway motion of the upper body of the optimized joint configuration was effective in improving energy efficiency, similar to the influence of the pathway of the body's center of gravity (COG) on human walking efficiency.
Keywords
OPTIMAL-DESIGN; OPTIMAL-DESIGN; humanoid robot; energy efficiency; Taguchi method
ISSN
2076-3417
URI
https://pubs.kist.re.kr/handle/201004/118041
DOI
10.3390/app10207287
Appears in Collections:
KIST Article > 2020
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