Artificial neural network-based ground reaction force estimation and learning for dynamic-legged robot systems.

ABSTRACT: 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 ap

SUBMITTER: An J 

PROVIDER: S-EPMC10773877 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature