Singularity Avoidance with Application to Online Trajectory Optimization for Serial Manipulators

TL;DR

This paper introduces a real-time singularity avoidance method for serial manipulators that leverages known singular configurations and potential functions, improving computational efficiency and trajectory quality.

Contribution

It presents a novel approach using tailored potential functions for singularity avoidance, specifically analyzing different robot kinematics and demonstrating improved performance.

Findings

Reduced average computation time

Shorter trajectories in time and path length

Effective singularity avoidance in simulations

Abstract

This work proposes a novel singularity avoidance approach for real-time trajectory optimization based on known singular configurations. The focus of this work lies on analyzing kinematically singular configurations for three robots with different kinematic structures, i.e., the Comau Racer 7-1.4, the KUKA LBR iiwa R820, and the Franka Emika Panda, and exploiting these configurations in form of tailored potential functions for singularity avoidance. Monte Carlo simulations of the proposed method and the commonly used manipulability maximization approach are performed for comparison. The numerical results show that the average computing time can be reduced and shorter trajectories in both time and path length are obtained with the proposed approach