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

Jerk-constrained trajectories offer a wide range of advantages thatcollectively improve the performance of robotic systems, including increasedenergy efficiency, durability, and safety. In this paper, we present a novelapproach to jerk-constrained time-optimal trajectory planning (TOTP), whichfollows a specified path while satisfying up to third-order constraints toensure safety and smooth motion. One significant challenge in jerk-constrainedTOTP is a non-convex formulation arising from the inclusion of third-orderconstraints. Approximating inequality constraints can be particularlychallenging because the resulting solutions may violate the actual constraints.We address this problem by leveraging convexity within the proposed formulationto form conservative inequality constraints. We then obtain the desiredtrajectories by solving an n-dimensional Sequential LinearProgram (SLP) iteratively until convergence. Lastly, we evaluate in a realrobot the performance of trajectories generated with and without jerk limits interms of peak power, torque efficiency, and tracking capability.