Online Trajectory Generation With Variable Geometry Stair Ascent for Powered Exoskeleton

Abstract

Powered exoskeletons offer a promising solution for individuals who require assistance with daily activities. In addition to walking on flat terrain, the y are evolving to handle advanced scenarios such as stairs, allowing for a wider range of activities. To facilitate this advancement, an effective and safe tool is required for the planning and generation of gait trajectories. This study proposes a novel online trajectory generation method for an exoskeleton to aid in stair ascent. Initially, a finite state machine model is designed to assist the user in shifting their center of gravity. Subsequently, a Bézier curve-based path interpolation approach is introduced to generate the path between each state. Finally, a time scaling and path reparameterization method is employed to avoid the singularity problem. Two kinds of numerical simulations and real exoskeleton experiments demonstrate that the proposed method can effectively and safely generate the trajectories to assist users in ascending stairs. Additionally, the effectiveness of the exoskeleton's assistance is verified through a comparison of electromyography (EMG) signals from seven muscles. The results show that there was a reduction in muscle activation ranging from 22% to 81% for the different muscles analyzed.