TY - JOUR
T1 - Dynamic balance of a bipedal robot using neural network training with simulated annealing
AU - Angeles-García, Yoqsan
AU - Calvo, Hiram
AU - Sossa, Humberto
AU - Anzueto-Ríos, Álvaro
N1 - Publisher Copyright:
Copyright © 2022 Angeles-García, Calvo, Sossa and Anzueto-Ríos.
PY - 2022/7/28
Y1 - 2022/7/28
N2 - This work proposes using an evolutionary optimization method known as simulated annealing to train artificial neural networks. These neural networks are used to control posture stabilization of a humanoid robot in a simulation. A total of eight multilayer perceptron neural networks are used. Although the control is used mainly for posture stabilization and not displacement, we propose a posture set to achieve this, including right leg lift in sagittal plane and right leg lift in frontal plane. At the beginning, tests are carried out only considering gravitational force and reaction force between the floor and the humanoid; then tests are carried out with two disturbances: tilted ground and adding a mass to the humanoid. We found that using simulated annealing the robot maintains its stability at all times, decreasing the number of epochs needed to converge, and also, showing flexibility and adaptability to disturbances. The way neural networks learn is analyzed; videos of the movements made, and the model for further experimentation are provided.
AB - This work proposes using an evolutionary optimization method known as simulated annealing to train artificial neural networks. These neural networks are used to control posture stabilization of a humanoid robot in a simulation. A total of eight multilayer perceptron neural networks are used. Although the control is used mainly for posture stabilization and not displacement, we propose a posture set to achieve this, including right leg lift in sagittal plane and right leg lift in frontal plane. At the beginning, tests are carried out only considering gravitational force and reaction force between the floor and the humanoid; then tests are carried out with two disturbances: tilted ground and adding a mass to the humanoid. We found that using simulated annealing the robot maintains its stability at all times, decreasing the number of epochs needed to converge, and also, showing flexibility and adaptability to disturbances. The way neural networks learn is analyzed; videos of the movements made, and the model for further experimentation are provided.
KW - bipedal robot
KW - machine learning
KW - neural network control
KW - neurorobotics
KW - simulated annealing
UR - http://www.scopus.com/inward/record.url?scp=85135844551&partnerID=8YFLogxK
U2 - 10.3389/fnbot.2022.934109
DO - 10.3389/fnbot.2022.934109
M3 - Artículo
C2 - 35966372
AN - SCOPUS:85135844551
SN - 1662-5218
VL - 16
JO - Frontiers in Neurorobotics
JF - Frontiers in Neurorobotics
M1 - 934109
ER -