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

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. We conducted navigation experiments in both simulated environments and participant experiments in real environments including user ratings, using a pseudo-BMI setup. As a result, the proposed method significantly reduced obstacle collisions in all experiments. It markedly shortened path lengths under almost all conditions in simulations and, in participant experiments, especially when user inputs become more discrete and noisy (p<0.01). Furthermore, under such challenging conditions, it was demonstrated that users could operate more easily, with greater confidence, and at a comfortable pace through this system.