Singularity avoidance of SSRMS-type manipulator based on ADR-SFO

Abstract To address issues such as stalling and low trajectory accuracy of end-effectors near singularities in robotic manipulators, this paper proposes a singularity avoidance algorithm for SSRMS-type manipulators based on Approximated Damped Reciprocal (ADR) and Singular Factor Optimization (SFO). The inverse kinematics method using the ADR function stabilizes joint velocities while improving trajectory accuracy near singularities. A novel gradient operator (SFO) is introduced to avoid singularities by adding joint velocities within the singular subspace, enabling self-motion to bypass singularities. Simulations validate the effectiveness and robustness of the proposed method.