Goal Estimation-based Adaptive Shared Control for Brain-Machine Interfaces Remote Robot Navigation

TL;DR

This paper introduces a goal estimation-based shared control approach for brain-machine interface teleoperation of robots, improving command continuity and robustness by estimating user intent and adaptively blending autonomous and user commands.

Contribution

The novel method estimates user goals from noisy BMI commands and adaptively combines autonomous and user inputs based on confidence levels, enhancing teleoperation control.

Findings

Improved command continuity and robustness in robot navigation.

Effective goal estimation from noisy BMI signals.

Adaptive weighting enhances shared control performance.

Abstract

In this study, we propose a shared control method for teleoperated mobile robots using brain-machine interfaces (BMI). The control commands generated through BMI for robot operation face issues of low input frequency, discreteness, and uncertainty due to noise. To address these challenges, our method estimates the user's intended goal from their commands and uses this goal to generate auxiliary commands through the autonomous system that are both at a higher input frequency and more continuous. Furthermore, by defining the confidence level of the estimation, we adaptively calculated the weights for combining user and autonomous commands, thus achieving shared control.