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

Abstract

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.

Kinematic Analysis and Simulation of an Industrial Rail-Mounted Robot Manipulator Using Ruckig for Enhanced Path Planning

“…in [5], [14] the time optimal trajectory planning for a redundant manipulator with a 6-axis robot manipulator mounted on a rail as a case study was discussed. In [15] a time optimal solution based on iterative optimization has been evaluated with promising outcomes where it manages to reduce the average absolute values of the position error and the robot's jerk by a huge factor compared with [16]. In [17] another algorithm is presented to ensure motion smoothness which has also taken the robot jerk constrained into account.…”