Nonlinear Model Predictive Control of a BMI-Guided Wheelchair for Navigation in Unknown Environments

The ability to discern human intentions from brain signals has opened the possibility of leveraging Brain-Machine Interfaces (BMIs) for the control of robotic devices, especially benefiting individuals with severe motor disabilities. In this work, we present a novel approach for navigating a semiautonomous wheelchair towards targets generated by a BMI, all while ensuring collision avoidance. Our approach employs Nonlinear Model Predictive Control (NMPC) for real-time trajectory generation in unknown and dynamic environments. The empirical results obtained from real-world experiments clearly demonstrate the advancements of our solution over current state-of-the-art techniques. Our implementation is proven to outperform well-established methods in terms of both smoothness and alignment with the user's intended behavior.