Virtually Constrained Admittance Control Using Feedback Linearization for Physical Human-Robot Interaction With Rehabilitation Exoskeletons

Abstract

Robot-assisted rehabilitation focuses in part on path-based assist-as-needed reaching rehabilitation, which dynamically adapts the level of robot assistance during physical therapy to ensure patient progress along a predefined trajectory without overreliance on the system. Additionally, bimanual exoskeletons have enabled asymmetric rehabilitation schemes, which leverage the patient's healthy side to guide the rehabilitation through interactions with objects in virtual reality that replicate activities of daily living. Within the context of physical human–robot interaction, these tasks can be formulated as constraints on the space of allowable motions. This study introduces a novel feedback linearization-inspired time-invariant admittance control scheme that enforces these motion constraints by isolating and stabilizing the component of the virtual dynamics transversal to the constraint. The methodology is applied to two rehabilitation tasks: 1) a path-guided reaching task with restoring force field and 2) a bimanual interaction with a virtual object. Each task is then evaluated on one of two drastically different exoskeleton systems: 1) the V-Rex, a nonanthropomorphic full-body haptic device and 2) the EXO-UL8, an anthropomorphic bimanual upper-limb exoskeleton. The two systems exist on opposite ends of the task/joint space control, nonredundant/redundant, off-the-shelf (industrial)/custom, nonanthropomorphic/anthropomorphic spectra. Experimental results validate and support the methodology as a generalizable approach to enabling constrained admittance control for rehabilitation robots.