Estimation and Control of Motor Core Temperature with Online Learning of Thermal Model Parameters: Application to Musculoskeletal Humanoids

TL;DR

This paper introduces an online learning approach for thermal model parameters to accurately estimate and manage motor core temperature in humanoid robots, enhancing continuous movement capabilities.

Contribution

It presents a novel online learning method for thermal models and a temperature management system with anomaly detection for humanoid robot motors.

Findings

Accurate motor temperature estimation using online learning.

Effective temperature management and anomaly detection in humanoid motors.

Successful application to musculoskeletal humanoids enabling continuous movement.

Abstract

The estimation and management of motor temperature are important for the continuous movements of robots. In this study, we propose an online learning method of thermal model parameters of motors for an accurate estimation of motor core temperature. Also, we propose a management method of motor core temperature using the updated model and anomaly detection method of motors. Finally, we apply this method to the muscles of the musculoskeletal humanoid and verify the ability of continuous movements.