Learning-aided state estimation for robotic rollators with experimental validation

Abstract

While demand for assistive technology has risen with aging populations and concomitant increase in mobility disabilities, conventional (passive) walker designs have demonstrated safety and usability limitations. Robotic rollators (or 4-wheeled walkers) have been proposed to address concerns, including slip, fall, and collision risks. To develop control systems for robotic rollators, accurate estimation of the states is required. While model-based estimation approaches have been widely investigated for mobile robots, robotic rollators present unique challenges due to model parameter changes and uncertainties. In contrast, data-driven estimation approaches require sufficient excitation modes during learning to address corner cases. The proposed learning-aided state estimation (L-ASE) method augments an unscented transformation observer with a long short term memory (LSTM) based learning algorithm to estimate rollator states by using on-board inertial measurement unit data and wheel speeds. The stability and boundedness of the error covariance is investigated. The developed learning-aided estimation method is also experimentally verified for the walker-assisted gait and demonstrates superior performance using a robotic rollator platform in rigorous testing conditions.