Co-Optimization of Tool Orientations, Kinematic Redundancy, and Waypoint Timing for Robot-Assisted Manufacturing

TL;DR

The paper tackles the challenge of concurrently optimizing tool orientation, kinematic redundancy, and waypoint timing for robot-assisted manufacturing with long toolpaths by formulating a dual-robot 6+2 DoF model and a kinematic-smoothness objective Φ_smooth that integrates joint velocity, acceleration, and jerk. It introduces a decomposition-based out-of-core SQP framework that solves many-waypoint trajectories in parallel, supported by initialization strategies, a sub-problem SQP solver, and a result-correction step. Across simulations and physical AM experiments, the method yields substantial improvements in kinematic smoothness, reduced vibrations, and better surface quality compared with decoupled approaches and local filtering, while achieving dramatic reductions in computational time through decomposition. Limitations include the time to train the collision-detection proxy and potential suboptimality from post-optimization correction, suggesting future work on adaptive collision detection and broader applicability to AM/SM tasks. The approach advances practical, scalable trajectory optimization for complex manufacturing systems with large waypoint counts and multiple robots.

Abstract

In this paper, we present a concurrent and scalable trajectory optimization method to improve the quality of robot-assisted manufacturing. Our method simultaneously optimizes tool orientations, kinematic redundancy, and waypoint timing on input toolpaths with large numbers of waypoints to improve kinematic smoothness while incorporating manufacturing constraints. Differently, existing methods always determine them in a decoupled manner. To deal with the large number of waypoints on a toolpath, we propose a decomposition-based numerical scheme to optimize the trajectory in an out-of-core manner, which can also run in parallel to improve the efficiency. Simulations and physical experiments have been conducted to demonstrate the performance of our method in examples of robot-assisted additive manufacturing.

Figures (28)

• Figure 1: The kinematic model of a robotic manufacturing system: (a) the dual-robots system with 6+2 DoFs, and (b) the schematic of the local coordinate systems around each waypoint $\mathbf{p}_i$.
• Figure 2: The influence of orientations on additive manufacturing: the ideal conditions are given in (a)-(c) and the corresponding failure cases are shown in (d)-(f).
• Figure 3: An illustration for two sets of toolpath-segments (upper and lower ones), where the toolpath-segments in the same set can be optimized simultaneously in parallel.
• Figure 4: The training process of the proxy functions for collision detection.
• Figure 5: The dual-robot system used for AM experiments, where the printer head is mounted on the end-effector of the 6-DoF robot and the workpiece is mounted on the working plate of a position table with 2-DoF.