Enhancing Physical Human-Robot Interaction Compliance in Force Sensorless Lead-Through Programming for Direct Drive Manipulators

Lead-through programming (LTP) enables the operators to guide the manipulator directly to the desired target position, significantly improving adaptability and flexibility. However, direct drive manipulators encounter significant challenges in force sensorless LTP due to the high inertia, external force sensitivity, and complications from kinematics singularities. To enhance the interaction compliance and safety, a novel force sensorless LTP control strategy is proposed for direct drive manipulators. Specifically, an enhanced generalized momentum observer (EGMO) based on a nominal model is designed to mitigate phase lag caused by filtering measurement noise through a phase compensation mechanism. Additionally, the introduction of dither signal and radial basis function neural network (RBFNN) learning methods reduces dynamic uncertainties, improving control performance. Then, a kinematics singularity avoidance control method is proposed, which switches between impedance and admittance control by predicting singularity points in advance, enabling the manipulator to avoid kinematic singularity in the task space with minimal position and orientation deviations, promoting a more compliant and safe interaction between the manipulator and the human operator. Finally, the proposed methods are validated on a six-degree-of-freedom (DOF) direct drive manipulator under three typical singular scenarios. The results demonstrate the effectiveness and practicality of the proposed method.