Singularity Avoidance with Application to Online Trajectory Optimization for Serial Manipulators
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
Keywords
Cite
@article{arxiv.2211.02516,
title = {Singularity Avoidance with Application to Online Trajectory Optimization for Serial Manipulators},
author = {Florian Beck and Minh Nhat Vu and Christian Hartl-Nesic and Andreas Kugi},
journal= {arXiv preprint arXiv:2211.02516},
year = {2023}
}
Comments
8 pages, 2 figures, Accepted for publication at IFAC World Congress 2023