Work

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

Year: 2025

Type: conference-paper

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.

Source: Journal of Physics Conference Series

DOI: 10.1088/1742-6596/2977/1/012072

Authors: Fengshi Ke, Yang Luo, Yiwen Zhao

Institutions: Shenyang Ligong University, Shenyang Institute of Automation, Chinese Academy of Sciences

Language: English

Topic: Robotic Mechanisms and Dynamics

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4408612732

FWCI:

Cites: 7

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Related to: 10