On the Performance of Jerk-Constrained Time-Optimal Trajectory Planning for Industrial Manipulators

TL;DR

This paper introduces a convex optimization-based method for jerk-constrained time-optimal trajectory planning in industrial robots, improving safety, energy efficiency, and motion smoothness.

Contribution

It proposes a novel convex formulation and iterative solution approach for jerk-constrained TOTP, addressing non-convexity challenges and ensuring safe, smooth trajectories.

Findings

Trajectories with jerk limits reduce peak power and torque.

The method improves tracking accuracy and safety.

Experimental results validate the approach on real robots.

Abstract

Jerk-constrained trajectories offer a wide range of advantages that collectively improve the performance of robotic systems, including increased energy efficiency, durability, and safety. In this paper, we present a novel approach to jerk-constrained time-optimal trajectory planning (TOTP), which follows a specified path while satisfying up to third-order constraints to ensure safety and smooth motion. One significant challenge in jerk-constrained TOTP is a non-convex formulation arising from the inclusion of third-order constraints. Approximating inequality constraints can be particularly challenging because the resulting solutions may violate the actual constraints. We address this problem by leveraging convexity within the proposed formulation to form conservative inequality constraints. We then obtain the desired trajectories by solving an $\boldsymbol n$-dimensional Sequential Linear Program (SLP) iteratively until convergence. Lastly, we evaluate in a real robot the performance of trajectories generated with and without jerk limits in terms of peak power, torque efficiency, and tracking capability.