Admittance Model Optimization for Gait Balance Assistance of a Robotic Walker: Passive Model-based Mechanical Assessment

This paper presents an optimization of an admittance control model for gait balance assistance offered by a walker-type assistive robot. We previously introduced the notion of quasi-passive physical Human-Robot Interaction (pHRI) where a non-wearable assistive device adaptively achieves supportability for providing physical assistance and operability to follow the user's intuitive operation. Aiming to mitigate the falling risk of elderly people with reduced mobility with our pHRI approach, we propose a hierarchical algorithm to optimize an admittance control model for a walker robot. By employing dynamic trajectories such as Zero Moment Point (ZMP) and Divergent Component of Motion (DCM) with optimization, our controller provides appropriate physical interaction to improve the gait stability while considering intrinsic body dynamics. In the current implementation, based on a model predictive control (MPC) framework, we formulate the optimization problems in the form of quadratic programming (QP), making the optimization suitable for real-time interaction. Through mechanical assessments with passive walking models of compass gait, we demonstrate the feasibility of our proposed optimization framework in stabilizing the limit cycle gait with minimized assistance.