Dataset Information

Dynamic balance of a bipedal robot using neural network training with simulated annealing.

ABSTRACT: 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.

SUBMITTER: Angeles-Garcia Y

PROVIDER: S-EPMC9366121 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Dynamic balance of a bipedal robot using neural network training with simulated annealing.

Angeles-García Yoqsan Y Calvo Hiram H Sossa Humberto H Anzueto-Ríos Álvaro Á

Frontiers in neurorobotics 20220728

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. A ...[more]

PMID: 35966372

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Project description:Legged robots have become popular in recent years due to their ability to locomote on rough terrains; these robots are able to walk on narrow stepping-stones, go upstairs, and explore soft ground such as sand. Ground reaction force (GRF) is the force exerted on the body by the ground when they are in contact. This is a key element and is widely used for programming the locomotion of the legged robots. Being capable of estimating the GRF is advantageous over measuring it with the actual sensor system. Estimating allows one to simplify the system, and it is meant to be capable of prediction, and so on. In this article, we present a neural network approach for GRF estimation for the legged robot system. In order to fundamentally study the GRF estimation of the robot leg, we demonstrate our approach for a single-legged robot with a degree of freedom (DoF) of two with hip and knee joints on a flat-surface. The first joint is directly driven from the actuator, and another joint is belt-pulley driven from the second actuator to take advantage of the long range of motion. The neural network is designed to estimate GRF without attaching force sensors such as load cells, and the encoder is the only sensor used for the estimation. We propose a two-staged multi-layer perceptron (MLP) solution based on supervised learning to estimate GRF in the physical-world. The first stage of the MLP model is trained using datasets from the simulation, enabling it to estimate the simulation-staged GRF. The second stage of the MLP model is trained in the physical world using the simulation-staged GRF obtained from the first stage MLP as the input. This approach enables the second stage MLP to bridge the simulation to the physical world. The root mean squared error (RMSE) is 0.9949 N on the validation datasets in the best case. The performance of the trained network is evaluated when the robot follows trajectories that are not used in training the two-stage GRF estimation network.

Dataset Information

Dynamic balance of a bipedal robot using neural network training with simulated annealing.

Publications

Dynamic balance of a bipedal robot using neural network training with simulated annealing.

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