Entropy (Apr 2023)

A Cartesian-Based Trajectory Optimization with Jerk Constraints for a Robot

  • Zhiwei Fan,
  • Kai Jia,
  • Lei Zhang,
  • Fengshan Zou,
  • Zhenjun Du,
  • Mingmin Liu,
  • Yuting Cao,
  • Qiang Zhang

DOI
https://doi.org/10.3390/e25040610
Journal volume & issue
Vol. 25, no. 4
p. 610

Abstract

Read online

To address the time-optimal trajectory planning (TOTP) problem with joint jerk constraints in a Cartesian coordinate system, we propose a time-optimal path-parameterization (TOPP) algorithm based on nonlinear optimization. The key insight of our approach is the presentation of a comprehensive and effective iterative optimization framework for solving the optimal control problem (OCP) formulation of the TOTP problem in the (s,s˙)-phase plane. In particular, we identify two major difficulties: establishing TOPP in Cartesian space satisfying third-order constraints in joint space, and finding an efficient computational solution to TOPP, which includes nonlinear constraints. Experimental results demonstrate that the proposed method is an effective solution for time-optimal trajectory planning with joint jerk limits, and can be applied to a wide range of robotic systems.

Keywords