Simultaneous Estimation of Human Motion Intention and Time-Varying Arm Stiffness for Enhanced Human–Robot Interaction

Recent advances in physiological human motor control research indicate that human endpoint stiffness magnitude increases linearly with grasp force. Based on these findings, a scheme was proposed in this article to integrate the linear quadratic estimation (LQE) filter with the stiffness model inferred from grasp force, which can simultaneously estimate the human arm's stiffness and motion intention. Then, an online variable impedance controller (VIC) was designed based on these estimations for physical human–robot interaction (pHRI). The proposed stiffness model and estimation method were validated through experiments using a planar robotic interface. To assess its performance in practical pHRI tasks, the implementation of human arm stiffness and intention estimation combining with VIC was extended to teleoperation peg-in-hole and robot-assisted rehabilitation tasks. The experimental results demonstrate that the proposed method can effectively estimate human motion intention and arm stiffness simultaneously. Compared to existing methods, the proposed VIC enhances pHRI in terms of increased flexibility, effective guidance, and reduced human effort.