Assist-as-Needed Robotic Strategy Based on Velocity Fields for Enhancing Motor Training

摘要

Designing robotic assistance strategies that prioritize users' effort and minimize robot intervention based on task or physiological performance measures, without mandating precise tracking of a time-dependent trajectory, poses a significant challenge. This article introduces a new assist-as-needed (AAN) robotic training strategy centered on an adaptive velocity field, which guides users smoothly towards a desired path without imposing explicit time constraints. It promotes participation by reducing assistance based on task performance and/or muscular effort. Unlike previous works, the low-level controller allows fine-tuning of the robot's accuracy in tracking the velocity field and gradually reduces the assistance as the free motion area around the desired trajectory is approached. This approach facilitates seamless transitions into and out of the free motion area, where a damping force is provided to ensure stable movements. An additional standout feature is the presence of a move-ahead strategy that avoids shortcuts. Two experiments were conducted to assess the effectiveness and advantages of the AAN strategy. Each experiment involved a different contour-following task with parameters, such as, the shortest distance from the desired path (Experiment 1) and muscular strength (Experiment 2) regulating the level of robotic assistance. In Experiment 1, the proposed strategy was compared against both a conventional haptic-constraint-based approach and no robotic assistance. Results indicate that the AAN robotic strategy enables faster task completion, smoother movements, reduced interaction forces, and diminished robot intervention. Moreover, its real-time adaptability based on task performance and physiological data suggests potential benefits for motor learning programs. In summary, the AAN strategy holds promise for improving motor skills by enhancing individual effort, eliminating time constraints, and allowing for adaptive assistance levels.